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83        return function unsubscribe() {
84          subs.delete(cb);
85          if (subs.size === 0) subscribersByKey.delete(key);
86        };
87      },
88      inject(key, payload, storeDelta) {
89        // Apply dedupe-store changes first.
90        if (storeDelta && typeof storeDelta === "object") {
91          const m = ensureStoreMap();
92          for (const k in storeDelta) {
93            m.set(k, storeDelta[k]);
94          }
95        }
96        ensureInjectedStore()[key] = payload;
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98      },
99    };
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.ais-SearchBox-input {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .ais-SearchBox-input::placeholder {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .KarmaChangeNotifier-starIcon {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .ColoredNumber-gainedPoints {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .NotificationsMenuButton-badge {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .NotificationsMenuButton-buttonClosed {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .MessagesMenuButton-buttonClosed {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .UsersMenu-arrowIcon {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .UsersMenu-userButtonContents {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .UsersAccountMenu-userButton {\n    color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .EAButton-variantContained {\n    color: var(--header-contrast-color);\n    background-color: var(--header-text-color);\n  }\n  .Header-appBarDarkBackground .EAButton-greyContained {\n    color: var(--header-text-color);\n    background-color: color-mix(in oklab, var(--header-text-color) 15%, var(--header-contrast-color));\n  }\n  .Header-appBarDarkBackground .EAButton-greyContained:hover {\n    background-color: color-mix(in oklab, var(--header-text-color) 10%, light-dark(rgba(255,255,255,0),rgba(0,0,0,0))) !important;\n  }\n  .Header-appBarDarkBackground .EAButton-variantContained:hover {\n    background-color: color-mix(in oklab, var(--header-text-color) 90%, var(--header-contrast-color));\n  }\n  .Header-root {\n    height: var(--header-height);\n  }\n@media print {\n  .Header-root {\n    display: none;\n  }\n}\n  .Header-title {\n    top: 3px;\n    color: light-dark(rgba(0,0,0,0.54),rgba(255,255,255,0.54));\n    display: flex;\n    overflow: hidden;\n    position: relative;\n    flex-grow: 1;\n    flex-shrink: 1;\n  }\n  .Header-titleLink {\n    top: 0;\n    color: light-dark(#000,#ffffff);\n    height: 19px;\n    display: flex;\n    font-size: 19px;\n    align-items: center;\n  }\n  .Header-titleLink:hover, .Header-titleLink:active {\n    opacity: 0.7;\n    text-decoration: none;\n  }\n  .Header-menuButton {\n    margin-left: -8px;\n    margin-right: 8px;\n  }\n  .Header-icon {\n    width: 24px;\n    height: 24px;\n  }\n@media (min-width:1424px) {\n  .Header-hideLgUp {\n    display: none !important;\n  }\n}\n@media (max-width:1423.95px) {\n  .Header-hideMdDown {\n    display: none !important;\n  }\n}\n@media (max-width:959.95px) {\n  .Header-hideSmDown {\n    display: none !important;\n  }\n}\n@media (max-width:599.95px) {\n  .Header-hideXsDown {\n    display: none !important;\n  }\n}\n@media (min-width:600px) {\n  .Header-hideSmUp {\n    display: none !important;\n  }\n}\n@media (min-width:960px) {\n  .Header-hideMdUp {\n    display: none !important;\n  }\n}\n  .Header-rightHeaderItems {\n    display: flex;\n    margin-left: auto;\n    margin-right: -8px;\n  }\n  .Header-searchSSRStandin {\n    min-width: 48px;\n  }\n  .Header-headroom .headroom {\n    top: unset;\n    left: 0;\n    right: 0;\n    z-index: 1300;\n  }\n  .Header-headroom .headroom--unfixed {\n    position: relative;\n    transform: translateY(0);\n  }\n  .Header-headroom .headroom--scrolled {\n    transition: transform 200ms ease-in-out;\n  }\n  .Header-headroom .headroom--unpinned {\n    position: fixed;\n    transform: translateY(-100%);\n  }\n  .Header-headroom .headroom--pinned {\n    position: fixed;\n    transform: translateY(0%);\n  }\n  .Header-headroomPinnedOpen .headroom--unpinned {\n    transform: none !important;\n  }\n  .Header-headroomPinnedOpen .headroom--unfixed {\n    position: fixed !important;\n  }\n@media (min-width:1280px) {\n  .Header-reserveSpaceForLlmChatSidebar .headroom {\n    width: calc(100% - 500px);\n  }\n}\n  .Header-headerHeight {\n    --header-height: 64px;\n  }\n@media (max-width:599.95px) {\n  .Header-headerHeight {\n    --header-height: 56px;\n  }\n}\n  .Header-headerHeightWithBanner {\n    --header-height: 98px;\n  }\n@media (max-width:599.95px) {\n  .Header-headerHeightWithBanner {\n    --header-height: 88px;\n  }\n}");
100_embedStyles("PageBackgroundWrapper",0,".PageBackgroundWrapper-wrapper {\n  position: relative;\n}\n.PageBackgroundWrapper-fullscreen {\n  height: max(100vh, 600px);\n  display: flex;\n  flex-direction: column;\n}");_embedStyles("MuiIconButton",-10,".MuiIconButton-root {\n  flex: 0 0 auto;\n  color: light-dark(rgba(0, 0, 0, 0.54),#fff);\n  padding: 12px;\n  overflow: visible;\n  font-size: 1.5rem;\n  text-align: center;\n  transition: background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n  border-radius: 50%;\n}\n.MuiIconButton-root:hover {\n  background-color: light-dark(rgba(0, 0, 0, 0.08), rgba(255, 255, 255, 0.08));\n}\n.MuiIconButton-root.MuiIconButton-disabled {\n  color: rgba(0, 0, 0, 0.26);\n}\n@media (hover: none) {\n  .MuiIconButton-root:hover {\n    background-color: transparent;\n  }\n}\n  .MuiIconButton-root:hover.MuiIconButton-disabled {\n    background-color: transparent;\n  }\n  .MuiIconButton-colorInherit {\n    color: inherit;\n  }\n  .MuiIconButton-colorPrimary {\n    color: #3f51b5;\n  }\n  .MuiIconButton-colorPrimary:hover {\n    background-color: rgba(63, 81, 181, 0.08);\n  }\n@media (hover: none) {\n  .MuiIconButton-colorPrimary:hover {\n    background-color: transparent;\n  }\n}\n  .MuiIconButton-colorSecondary {\n    color: #3f51b5;\n  }\n  .MuiIconButton-colorSecondary:hover {\n    background-color: rgba(63, 81, 181, 0.08);\n  }\n@media (hover: none) {\n  .MuiIconButton-colorSecondary:hover {\n    background-color: transparent;\n  }\n}\n  .MuiIconButton-label {\n    width: 100%;\n    display: flex;\n    align-items: inherit;\n    justify-content: inherit;\n  }");_embedStyles("MuiButtonBase",-11,".MuiButtonBase-root {\n  color: inherit;\n  border: 0;\n  cursor: pointer;\n  margin: 0;\n  display: inline-flex;\n  outline: none;\n  padding: 0;\n  position: relative;\n  align-items: center;\n  user-select: none;\n  border-radius: 0;\n  vertical-align: middle;\n  -moz-appearance: none;\n  justify-content: center;\n  text-decoration: none;\n  background-color: transparent;\n  -webkit-appearance: none;\n  -webkit-tap-highlight-color: transparent;\n}\n.MuiButtonBase-root::-moz-focus-inner {\n  border-style: none;\n}\n.MuiButtonBase-root.MuiButtonBase-disabled {\n  cursor: default;\n  pointer-events: none;\n}");_embedStyles("ForumIcon",-2,".ForumIcon-root {\n  width: var(--icon-size, 1em);\n  height: var(--icon-size, 1em);\n  display: inline-block;\n  font-size: var(--icon-size, 24px);\n  flex-shrink: 0;\n  user-select: none;\n}\n.ForumIcon-linkRotation {\n  transform: rotate(-45deg);\n}\n.ForumIcon-linkRotation.MuiListItemIcon-root {\n  margin-right: 12px;\n}");_embedStyles("MuiSvgIcon",-10,".MuiSvgIcon-root {\n  fill: currentColor;\n  width: 1em;\n  height: 1em;\n  display: inline-block;\n  font-size: 24px;\n  transition: fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n  flex-shrink: 0;\n  user-select: none;\n}");_embedStyles("MuiTouchRipple",-10,".MuiTouchRipple-root {\n  top: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  display: block;\n  z-index: 0;\n  overflow: hidden;\n  position: absolute;\n  border-radius: inherit;\n  pointer-events: none !important;\n}\n.MuiTouchRipple-ripple {\n  top: 0;\n  left: 0;\n  width: 50px;\n  height: 50px;\n  opacity: 0;\n  position: absolute;\n}\n.MuiTouchRipple-rippleVisible {\n  opacity: 0.3;\n  animation: mui-ripple-enter 550ms cubic-bezier(0.4, 0, 0.2, 1);\n  transform: scale(1);\n}\n.MuiTouchRipple-ripplePulsate {\n  animation-duration: 200ms;\n}\n.MuiTouchRipple-child {\n  width: 100%;\n  height: 100%;\n  display: block;\n  opacity: 1;\n  border-radius: 50%;\n  background-color: currentColor;\n}\n.MuiTouchRipple-childLeaving {\n  opacity: 0;\n  animation: mui-ripple-exit 550ms cubic-bezier(0.4, 0, 0.2, 1);\n}\n.MuiTouchRipple-childPulsate {\n  top: 0;\n  left: 0;\n  position: absolute;\n  animation: mui-ripple-pulsate 2500ms cubic-bezier(0.4, 0, 0.2, 1) 200ms infinite;\n}\n@keyframes mui-ripple-enter {\n  0% {\n    opacity: 0.1;\n    transform: scale(0);\n  }\n  100% {\n    opacity: 0.3;\n    transform: scale(1);\n  }\n}\n@keyframes mui-ripple-exit {\n  0% {\n    opacity: 1;\n  }\n  100% {\n    opacity: 0;\n  }\n}\n@keyframes mui-ripple-pulsate {\n  0% {\n    transform: scale(1);\n  }\n  50% {\n    transform: scale(0.92);\n  }\n  100% {\n    transform: scale(1);\n  }\n}");_embedStyles("Typography",-2,".Typography-root {\n  margin: 0;\n  display: block;\n}\n.Typography-display4 {\n  color: light-dark(#424242,#e8e8e8);\n  font-size: 7rem;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 300;\n  line-height: 1.14286em;\n  margin-left: -.04em;\n  letter-spacing: -.04em;\n}
100\n.Typography-display3 {\n  color: light-dark(#424242,#e8e8e8);\n  font-size: 39px;\n  margin-top: 1.2em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  line-height: 1.30357em;\n  margin-left: -.02em;\n  letter-spacing: -.02em;\n}\n.Typography-display2 {\n  color: light-dark(#424242,#e8e8e8);\n  font-size: 36.4px;\n  margin-top: 1em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  line-height: 1.13333em;\n  margin-left: -.02em;\n}\n.Typography-display1 {\n  color: light-dark(#424242,#e8e8e8);\n  font-size: 26px;\n  margin-top: 1em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 1.20588em;\n}\n.Typography-headline {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 1.5rem;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 1.35417em;\n}\n.Typography-title {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 18px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  line-height: 1.16667em;\n  margin-bottom: 3px;\n}\n.Typography-subheading {\n  color: light-dark(#757575,#c6c6c6);\n  font-size: 15px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 1.5em;\n}\n.Typography-body2 {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n}\n.Typography-body1 {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 18.2px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 26px;\n}\n.Typography-gutterBottom {\n  margin-bottom: 0.35em;\n}");_embedStyles("HeaderSubtitle",0,".HeaderSubtitle-subtitleContainer {\n  overflow: hidden;\n  flex-shrink: 1;\n  white-space: nowrap;\n}\n.HeaderSubtitle-subtitle {\n  color: rgba(0,0,0,0.87);\n  border-left: 1px solid light-dark(#bdbdbd,#818181);\n  margin-left: 1em;\n  padding-left: 1em;\n  text-transform: uppercase;\n}");_embedStyles("SearchBar",0,".SearchBar-root {\n  display: flex;\n  align-items: center;\n}\n.SearchBar-rootChild {\n  height: fit-content;\n}\n.SearchBar-searchInputArea {\n  height: 48px;\n  display: block;\n  position: relative;\n  min-width: 48px;\n}\n.SearchBar-searchInputArea .ais-SearchBox {\n  width: 100%;\n  height: 46px;\n  display: inline-block;\n  position: relative;\n  font-size: 14px;\n  max-width: 300px;\n  box-sizing: border-box;\n  white-space: nowrap;\n}\n.SearchBar-searchInputArea .ais-SearchBox-form {\n  height: 100%;\n}\n.SearchBar-searchInputArea .ais-SearchBox-submit {\n  display: none;\n}\n.SearchBar-searchInputArea .ais-SearchBox-input {\n  width: 100%;\n  cursor: text;\n  height: 100%;\n  display: none;\n  font-size: inherit;\n  box-shadow: none;\n  border-style: none;\n  padding-left: 48px;\n  border-radius: 5px;\n  padding-right: 0;\n  vertical-align: bottom;\n  background-color: transparent;\n  -webkit-appearance: none;\n}\n.SearchBar-searchInputArea.open .ais-SearchBox-input {\n  display: inline-block;\n}\n.SearchBar-searchInputArea.open .SearchBar-searchIconButton {\n  position: fixed;\n}\n.SearchBar-searchIcon {\n  --icon-size: 24px;\n}\n.SearchBar-searchIconButton {\n  color: rgba(0,0,0,0.87);\n}\n.SearchBar-closeSearchIcon {\n  font-size: 14px;\n}\n.SearchBar-searchBarClose {\n  top: 15px;\n  right: 5px;\n  cursor: pointer;\n  display: inline-block;\n  position: absolute;\n}");_embedStyles("UsersAccountMenu",0,".UsersAccountMenu-root {\n  margin-top: 5px;\n}\n.UsersAccountMenu-userButton {\n  color: rgba(0,0,0,0.87);\n  opacity: 0.8;\n  font-size: 14px;\n  font-weight: 400;\n}");_embedStyles("MuiButton",-10,".MuiButton-root {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  padding: 8px 16px;\n  font-size: 0.875rem;\n  min-width: 64px;\n  box-sizing: border-box;\n  min-height: 36px;\n  transition: background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  line-height: 1.4em;\n  border-radius: 4px;\n  text-transform: uppercase;\n}\n.MuiButton-root:hover {\n  text-decoration: none;\n  background-color: light-dark(rgba(0, 0, 0, 0.08), rgba(255, 255, 255, 0.08));\n}\n.MuiButton-root.MuiButton-disabled {\n  color: rgba(0, 0, 0, 0.26);\n}\n@media (hover: none) {\n  .MuiButton-root:hover {\n    background-color: transparent;\n  }\n}\n  .MuiButton-root:hover.MuiButton-disabled {\n    background-color: transparent;\n  }\n  .MuiButton-label {\n    width: 100%;\n    display: inherit;
100\n    align-items: inherit;\n    justify-content: inherit;\n  }\n  .MuiButton-textPrimary {\n    color: #3f51b5;\n  }\n  .MuiButton-textPrimary:hover {\n    background-color: rgba(63, 81, 181, 0.08);\n  }\n@media (hover: none) {\n  .MuiButton-textPrimary:hover {\n    background-color: transparent;\n  }\n}\n  .MuiButton-textSecondary {\n    color: #3f51b5;\n  }\n  .MuiButton-textSecondary:hover {\n    background-color: rgba(63, 81, 181, 0.08);\n  }\n@media (hover: none) {\n  .MuiButton-textSecondary:hover {\n    background-color: transparent;\n  }\n}\n  .MuiButton-outlined {\n    border: 1px solid rgba(0, 0, 0, 0.23);\n  }\n  .MuiButton-outlinedPrimary {\n    border: 1px solid rgba(63, 81, 181, 0.5);\n  }\n  .MuiButton-outlinedPrimary:hover {\n    border: 1px solid #3f51b5;\n  }\n  .MuiButton-outlinedPrimary.MuiButton-disabled {\n    border: 1px solid rgba(0, 0, 0, 0.26);\n  }\n  .MuiButton-outlinedSecondary {\n    border: 1px solid rgba(63, 81, 181, 0.5);\n  }\n  .MuiButton-outlinedSecondary:hover {\n    border: 1px solid #3f51b5;\n  }\n  .MuiButton-outlinedSecondary.MuiButton-disabled {\n    border: 1px solid rgba(0, 0, 0, 0.26);\n  }\n  .MuiButton-contained {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    box-shadow: 0px 1px 5px 0px light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2)),0px 2px 2px 0px light-dark(rgba(0,0,0,0.14),rgba(255,255,255,0.14)),0px 3px 1px -2px light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n    background-color: light-dark(#e0e0e0,#505050);\n  }\n  .MuiButton-contained.MuiButton-focusVisible {\n    box-shadow: 0px 3px 5px -1px light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2)),0px 6px 10px 0px light-dark(rgba(0,0,0,0.14),rgba(255,255,255,0.14)),0px 1px 18px 0px light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n  }\n  .MuiButton-contained:active {\n    box-shadow: 0px 5px 5px -3px light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2)),0px 8px 10px 1px light-dark(rgba(0,0,0,0.14),rgba(255,255,255,0.14)),0px 3px 14px 2px light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n  }\n  .MuiButton-contained.MuiButton-disabled {\n    color: rgba(0, 0, 0, 0.26);\n    box-shadow: none;\n    background-color: rgba(0, 0, 0, 0.12);\n  }\n  .MuiButton-contained:hover {\n    background-color: light-dark(#d5d5d5,#616161);\n  }\n@media (hover: none) {\n  .MuiButton-contained:hover {\n    background-color: light-dark(#e0e0e0,#505050);\n  }\n}\n  .MuiButton-contained:hover.MuiButton-disabled {\n    background-color: rgba(0, 0, 0, 0.12);\n  }\n  .MuiButton-containedPrimary {\n    color: light-dark(#fff,#000000);\n    background-color: #3f51b5;\n  }\n  .MuiButton-containedPrimary:hover {\n    background-color: #303f9f;\n  }\n@media (hover: none) {\n  .MuiButton-containedPrimary:hover {\n    background-color: #3f51b5;\n  }\n}\n  .MuiButton-containedSecondary {\n    color: light-dark(#fff,#000000);\n    background-color: #3f51b5;\n  }\n  .MuiButton-containedSecondary:hover {\n    background-color: #303f9f;\n  }\n@media (hover: none) {\n  .MuiButton-containedSecondary:hover {\n    background-color: #3f51b5;\n  }\n}\n  .MuiButton-fab {\n    width: 56px;\n    height: 56px;\n    padding: 0;\n    min-width: 0;\n    box-shadow: 0px 3px 5px -1px light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2)),0px 6px 10px 0px light-dark(rgba(0,0,0,0.14),rgba(255,255,255,0.14)),0px 1px 18px 0px light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n    border-radius: 50%;\n  }\n  .MuiButton-fab:active {\n    box-shadow: 0px 7px 8px -4px light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2)),0px 12px 17px 2px light-dark(rgba(0,0,0,0.14),rgba(255,255,255,0.14)),0px 5px 22px 4px light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n  }\n  .MuiButton-extendedFab {\n    width: auto;\n    height: 48px;\n    padding: 0 16px;\n    min-width: 48px;\n    border-radius: 24px;\n  }\n  .MuiButton-colorInherit {\n    color: inherit;\n  }\n  .MuiButton-mini {\n    width: 40px;\n    height: 40px;\n  }\n  .MuiButton-sizeSmall {\n    padding: 7px 8px;\n    font-size: 0.8125rem;\n    min-width: 64px;\n    min-height: 32px;\n  }\n  .MuiButton-sizeLarge {\n    padding: 8px 24px;\n    font-size: 0.9375rem;\n    min-width: 112px;\n    min-height: 40px;\n  }\n  .MuiButton-fullWidth {\n    width: 100%;\n  }");_embedStyles("LWPopper",0,".LWPopper-popper {\n  z-index: 10000;\n  position: absolute;\n}\n.LWPopper-default {\n  z-index: 10001;\n  position: relative;\n}\n.LWPopper-tooltip {\n  color: #fff;\n  padding: 8px;\n  z-index: 10001;\n  position: relative;\n  font-size: 1rem;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  border-radius: 3px;\n  background-color: rgba(75,75,75,.94);\n  font-variant-numeric: lining-nums;\n}\n.LWPopper-noMouseEvents {\n  pointer-events: none;\n}\n@media (pointer:coarse) {\n  .LWPopper-hideOnTouchScreens {\n    display: none;\n  }\n}");
100_embedStyles("NavigationDrawer",0,".NavigationDrawer-paperWithoutToC {\n  width: 280px;\n  overflow-y: auto;\n}\n.NavigationDrawer-paperWithToC {\n  width: 280px;\n  overflow: hidden;\n}\n@media (max-width:959.95px) {\n  .NavigationDrawer-paperWithToC {\n    width: 300px;\n  }\n}\n  .NavigationDrawer-drawerNavigationMenuUncompressed {\n    left: 0;\n    width: 260px;\n    padding-bottom: 20px;\n  }\n@media (max-width:959.95px) {\n  .NavigationDrawer-hideOnMobile {\n    display: none;\n  }\n}\n  .NavigationDrawer-drawerNavigationMenuCompressed {\n    color: light-dark(#757575,#c6c6c6);\n    width: 55px;\n    height: 100%;\n    padding-top: 16px;\n    border-right: 1px solid light-dark(rgba(0,0,0,0.1),rgba(255,255,255,0.1));\n    padding-bottom: 8px;\n    background-color: light-dark(#f5f5f5,#262626);\n  }\n@media (min-width:960px) {\n  .NavigationDrawer-drawerNavigationMenuCompressed {\n    display: none;\n  }\n}\n  .NavigationDrawer-tableOfContents {\n    left: 55px;\n    height: 100%;\n    padding: 16px 0 16px 16px;\n    position: absolute;\n    max-width: 247px;\n    overflow-y: auto;\n  }\n@media (min-width:960px) {\n  .NavigationDrawer-tableOfContents {\n    display: none;\n  }\n}");_embedStyles("Drawer",0,".Drawer-containerLeft {\n  top: 0;\n  left: 0;\n  width: 0;\n  bottom: 0;\n  position: absolute;\n}\n.Drawer-containerRight {\n  top: 0;\n  right: 0;\n  width: 0;\n  bottom: 0;\n  position: absolute;\n}\n.Drawer-docked {\n  flex: 0 0 auto;\n}\n.Drawer-container {\n  top: 0;\n  height: 100%;\n  z-index: 1400;\n  position: fixed;\n}\n.Drawer-paper {\n  flex: 1 0 auto;\n  height: 100%;\n  display: flex;\n  outline: none;\n  position: absolute;\n  overflow-y: auto;\n  transition: transform 0.3s ease-out;\n  flex-direction: column;\n  -webkit-overflow-scrolling: touch;\n}\n.Drawer-anchorLeft {\n  right: 100%;\n  transform: translateX(0);\n}\n.Drawer-anchorRight {\n  left: 100%;\n  transform: translateX(0);\n}\n.Drawer-open {\n  margin-left: 0 !important;\n  margin-right: 0 !important;\n}\n.Drawer-paperAnchorDockedLeft {\n  border-right: 1px solid light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n}\n.Drawer-paperAnchorDockedRight {\n  border-left: 1px solid light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n}");_embedStyles("FlashMessages",0,".FlashMessages-paper {\n  display: flex;\n  padding: 6px 24px;\n  flex-wrap: wrap;\n  align-items: center;\n  background-color: light-dark(#fff,#000000);\n}\n@media (min-width:960px) {\n  .FlashMessages-paper {\n    max-width: 568px;\n    min-width: 288px;\n    border-radius: 4px;\n  }\n}\n@media (max-width:959.95px) {\n  .FlashMessages-paper {\n    flex-grow: 1;\n  }\n}\n  .FlashMessages-message {\n    color: light-dark(rgba(0,0,0,1),rgba(255,255,255,1));\n    padding: 8px 0;\n  }\n  .FlashMessages-action {\n    display: flex;\n    align-items: center;\n    margin-left: auto;\n    margin-right: -8px;\n    padding-left: 24px;\n  }");_embedStyles("RouteRootClient",0,".RouteRootClient-main {\n  max-width: 100%;\n  overflow-x: clip;\n}\n.RouteRootClient-mainFullscreen {\n  flex: 1;\n  display: flex;\n  min-height: 0;\n  flex-direction: column;\n}\n.RouteRootClient-centralColumn {\n  grid-area: main;\n  min-height: calc(100vh - var(--header-height));\n  margin-left: auto;\n  padding-top: 50px;\n  margin-right: auto;\n}\n@media (max-width:1279.95px) {\n  .RouteRootClient-centralColumn {\n    padding-top: 50px;\n  }\n}\n@media (max-width:959.95px) {\n  .RouteRootClient-centralColumn {\n    padding-top: 10px;\n    padding-left: 8px;\n    padding-right: 8px;\n  }\n}\n  .RouteRootClient-fullscreen {\n    height: 100%;\n    padding: 0;\n  }\n  .RouteRootClient-rightSidebar {\n    width: 210px;\n    display: none;\n    grid-area: rightSidebar;\n  }\n@media (min-width:1280px) {\n  .RouteRootClient-rightSidebar {\n    display: block;\n  }\n}");_embedStyles("LeftAndRightSidebarsWrapper",0,"@supports (grid-template-areas: \"title\") {\n  .LeftAndRightSidebarsWrapper-spacedGridActivated {\n    display: grid;\n    grid-template-areas: \n        \"navSidebar ... main imageGap rightSidebar\"\n      ;\n    grid-template-columns: \n        minmax(0, min-content)\n        minmax(0, 1fr)\n        minmax(0, min-content)\n        minmax(0, 7fr)\n        minmax(0, min-content)\n      ;\n  }\n}
100\n@media (max-width:1279.95px) {\n  .LeftAndRightSidebarsWrapper-gridBreakpointMd {\n    display: block;\n  }\n}\n@media (max-width:959.95px) {\n  .LeftAndRightSidebarsWrapper-gridBreakpointSm {\n    display: block;\n  }\n}\n  .LeftAndRightSidebarsWrapper-fullscreenBodyWrapper {\n    overflow: auto;\n    flex-grow: 1;\n    flex-basis: 0;\n  }\n@media (max-width:599.95px) {\n  .LeftAndRightSidebarsWrapper-fullscreenBodyWrapper {\n    overflow: visible;\n  }\n}");_embedStyles("NavigationStandalone",0,".NavigationStandalone-sidebar {\n  width: 250px;\n}\n@media (max-width:959.95px) {\n  .NavigationStandalone-sidebar {\n    display: none;\n  }\n}\n@media (max-width: 1424px) {\n  .NavigationStandalone-iconOnlySidebar {\n    width: 64px;\n  }\n}\n  .NavigationStandalone-navSidebarTransparent {\n    z-index: 10;\n  }\n  .NavigationStandalone-background {\n    background: light-dark(rgba(255,255,255,.75),rgba(0,0,0,.75));\n  }\n@media (max-width: 1424px) {\n  .NavigationStandalone-fullNavigation {\n    display: none;\n  }\n}\n  .NavigationStandalone-iconOnlyNavigation {\n    display: none;\n  }\n@media (max-width: 1424px) {\n  .NavigationStandalone-iconOnlyNavigation {\n    display: block;\n  }\n}");_embedStyles("Slide",0,".Slide-wrapper {\n  position: relative;\n}\n.Slide-slider {\n  position: relative;\n  transition: left 0.3s ease-in-out;\n}\n.Slide-slidOut {\n  left: -100%;\n}\n.Slide-slidIn {\n  left: 0;\n}");_embedStyles("TabNavigationMenu",0,".TabNavigationMenu-root {\n  display: flex;\n  max-width: 250px;\n  padding-top: 15px;\n  flex-direction: column;\n  justify-content: space-around;\n}\n.TabNavigationMenu-iconOnlyRoot {\n  width: 64px;\n  max-width: 64px;\n  align-items: center;\n  padding-left: 0;\n  padding-right: 0;\n  justify-content: flex-start;\n}\n.TabNavigationMenu-navSidebarTransparent {\n  z-index: 10;\n  background: light-dark(#fff,#000000);\n  backdrop-filter: none;\n}\n.TabNavigationMenu-divider {\n  width: 50px;\n  margin-top: 12px;\n  margin-left: 60px;\n  border-bottom: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n  margin-bottom: 20px;\n}");_embedStyles("TabNavigationItem",0,".TabNavigationItem-selected .TabNavigationItem-icon {\n  opacity: 1;\n}\n.TabNavigationItem-selected .TabNavigationItem-navText {\n  color: light-dark(#212121,#f7f7f7);\n  font-weight: 600;\n}\n.TabNavigationItem-menuItem {\n  width: 190px;\n}\n@media (max-width:599.95px) {\n  .TabNavigationItem-desktopOnly {\n    display: none !important;\n  }\n}\n  .TabNavigationItem-navButton {\n    color: light-dark(#424242,#e8e8e8);\n    display: flex;\n    padding: 10px 16px;\n    align-items: center;\n    flex-direction: row;\n    justify-content: flex-start;\n  }\n  .TabNavigationItem-navButton:hover {\n    opacity: 0.6;\n    background-color: transparent;\n  }\n  .TabNavigationItem-subItemOverride {\n    padding-top: 0;\n    padding-left: 0;\n    padding-right: 0;\n    padding-bottom: 0;\n  }\n  .TabNavigationItem-subItemOverride:hover {\n    background-color: transparent;\n  }\n  .TabNavigationItem-icon {\n    width: 30px;\n    height: 28px;\n    display: inline;\n    opacity: 0.3;\n    margin-right: 16px;\n  }\n  .TabNavigationItem-icon svg {\n    fill: currentColor;\n    color: light-dark(rgba(0,0,0,1),rgba(255,255,255,1));\n  }\n  .TabNavigationItem-iconOnlyIcon {\n    width: 100%;\n    display: flex;\n    margin-left: 17px;\n    margin-right: 0;\n    justify-content: center;\n  }\n  .TabNavigationItem-iconOnlyMode {\n    gap: 3px;\n    width: 100%;\n    height: auto;\n    overflow: visible;\n    min-width: 0;\n    text-align: center;\n    white-space: normal;\n    padding-left: 0;\n    padding-right: 0;\n    text-overflow: unset;\n    flex-direction: column;\n    justify-content: center;\n  }\n  .TabNavigationItem-iconOnlyMode:hover {\n    background-color: transparent;\n  }\n  .TabNavigationItem-navText {\n    color: inherit;\n    font-size: 15.08px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 19.8px;\n    text-transform: none !important;\n  }\n  .TabNavigationItem-iconOnlyNavText {\n    color: light-dark(#757575,#c6c6c6);\n    font-size: 10px;\n    text-align: center;\n    margin-left: 17px;\n    margin-bottom: 8px;\n  }\n  .TabNavigationItem-homeIcon svg {\n    top: -1px;\n    height: 29px;\n    position: relative;\n  }\n  .TabNavigationItem-flag {\n    color: #fff;\n    padding: 2px 4px;\n    font-size: 11px;\n    background: #3f51b5;\n    font-weight: 600;\n    line-height: 110%;\n    margin-left: 10px;\n    border-radius: 3px;\n    letter-spacing: 0.33px;\n    text-transform: uppercase;\n  }");_embedStyles("LWTooltip",-1,".LWTooltip-root {\n  display: inline-block;\n}\n.LWTooltip-tooltip {\n  max-width: 300px;\n}\n.LWTooltip-tooltip img {\n  max-width: 100%;\n}");_embedStyles("MenuItem",-1,".MenuItem-root {\n  color: light-dark(#757575,#c6c6c6);\n  width: auto;\n  height: 24px;\n  overflow: hidden;\n  font-size: 15px;\n  box-sizing: content-box;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 1.5em;\n  white-space: nowrap;\n  padding-left: 16px;\n  padding-right: 16px;\n  text-overflow: ellipsis;\n}\n.MenuItem-listRoot {\n  display: flex;\n  position: relative;\n  text-align: left;\n  transition: background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n  align-items: center;\n  padding-top: 12px;\n  padding-bottom: 12px;\n  justify-content: flex-start;\n  text-decoration: none;\n}\n.MenuItem-listRoot:hover {\n  text-decoration: none;\n  background-color: light-dark(rgba(0,0,0,0.1),rgba(255,255,255,0.1));\n}\n@media (hover: none) {\n  .MenuItem-listRoot:hover {\n    background-color: transparent;\n  }\n}\n  .MenuItem-listDense {\n    padding-top: 8px;\n    padding-bottom: 8px;\n  }\n  .MenuItem-listDisabled {\n    opacity: 0.5;\n  }");_embedStyles("TabNavigationSubItem",0,".TabNavigationSubItem-root {\n  color: light-dark(#616161,#d5d5d5);\n  width: 172px;\n  display: block;\n  overflow: hidden;\n  font-size: 1rem;\n  box-sizing: content-box;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  white-space: nowrap;\n  padding-left: 62px;\n  padding-bottom: 8px;\n}\n.TabNavigationSubItem-root:hover {\n  opacity: 0.6;\n}");_embedStyles("AlignmentForumHome",0,"@media screen and 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.4em;\n    min-width: 2em;\n    word-break: normal;\n  }\n  .SpotlightItem-description th {\n    background: light-dark(#fafafa,#181818);\n    font-weight: 700;\n  }\n  .SpotlightItem-description figure {\n    margin: 1em auto;\n    max-width: 100%;\n    text-align: center;\n  }\n  .SpotlightItem-description figcaption {\n    color: light-dark(rgba(0,0,0,0.54),rgba(255,255,255,0.54));\n    font-size: .85em;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 1.375em;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-description ol > li >
116 ol {\n    list-style: lower-alpha;\n  }\n  .SpotlightItem-description ol > li > ol > li > ol {\n    list-style: lower-roman;\n  }\n  .SpotlightItem-description .nested-list-item {\n    list-style-type: none;\n  }\n  .SpotlightItem-description u {\n    text-decoration: none;\n  }\n  .SpotlightItem-description p.spoiler {\n    margin: 0;\n  }\n  .SpotlightItem-description .spoiler {\n    padding: 8px;\n    min-height: 15px;\n    pointer-events: auto;\n  }\n  .SpotlightItem-description div.spoilers {\n    margin: 1em 0;\n    overflow: auto;\n  }\n  .SpotlightItem-description p.spoiler-v2 {\n    margin: 0;\n    padding: 0.5em 8px;\n  }\n  .SpotlightItem-description .spoilers:not(:hover)::selection, .SpotlightItem-description .spoilers:not(:hover) ::selection {\n    background-color: transparent;\n  }\n  .SpotlightItem-description .spoilers > p:hover ~ p {\n    color: light-dark(#000,#1b1b1b);\n    caret-color: #fff;\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description div.metaculus-preview {\n    background-color: #2c3947;\n  }\n  .SpotlightItem-description div.estimaker-preview {\n    display: flex;\n  }\n  .SpotlightItem-description div.viewpoints-preview {\n    display: flex;\n  }\n  .SpotlightItem-description figure.media div[data-oembed-url*=\"youtube.com\"], .SpotlightItem-description figure.media div[data-oembed-url*=\"youtu.be\"], .SpotlightItem-description div.youtube-preview {\n    height: 0;\n    position: relative;\n    padding-bottom: 56.2493%;\n  }\n  .SpotlightItem-description .footnote-item > * {\n    vertical-align: text-top;\n  }\n  .SpotlightItem-description .footnote-back-link {\n    top: -0.2em;\n    position: relative;\n  }\n  .SpotlightItem-description .footnotes .footnote-back-link > sup {\n    margin-right: 0;\n  }\n  .SpotlightItem-description .footnote-content {\n    width: 95%;\n    display: inline-block;\n    padding: 0 0.3em;\n  }\n  .SpotlightItem-description .detailsBlock {\n    border: none !important;\n    margin-top: .75em;\n    margin-bottom: .75em;\n  }\n  .SpotlightItem-description .detailsBlockTitle {\n    padding: 2px 0 2px 22px;\n    position: relative;\n    background: transparent !important;\n    list-style: none;\n  }\n  .SpotlightItem-description summary.detailsBlockTitle {\n    cursor: pointer;\n  }\n  .SpotlightItem-description .detailsBlockContent {\n    margin-top: 8px;\n    border-left: 1px solid light-dark(#bdbdbd,#818181);\n    margin-left: 11px;\n    padding-left: 14px;\n  }\n  .SpotlightItem-description .detailsBlockContent > *:first-child, .SpotlightItem-description .detailsBlockTitle > *:first-child {\n    margin-top: 0 !important;\n  }\n  .SpotlightItem-description .detailsBlockContent > *:last-child, .SpotlightItem-description .detailsBlockTitle > *:last-child {\n    margin-bottom: 0 !important;\n  }\n  .SpotlightItem-description details.detailsBlock[open] > .detailsBlockTitle::before, .SpotlightItem-description div.detailsBlockTitle::before {\n    transform: rotate(90deg);\n  }\n  .SpotlightItem-description .detailsBlock.closed div.detailsBlockTitle::before, .SpotlightItem-description .detailsBlockClosed div.detailsBlockTitle::before {\n    transform: rotate(0deg);\n  }\n  .SpotlightItem-description .detailsBlock.closed .detailsBlockContent, .SpotlightItem-description .detailsBlockClosed .detailsBlockContent {\n    display: none;\n  }\n  .SpotlightItem-description .detailsBlockTitle p:has(> br[data-cke-filler=true]:only-child)::after {\n    top: 0;\n    color: light-dark(rgba(0,0,0,0.3),rgba(255,255,255,0.3));\n    content: \"Collapsible Section Title\";\n    position: absolute;\n    pointer-events: none;\n  }\n  .SpotlightItem-description .llm-content-block {\n    margin: 1em 0;\n    opacity: 0.94;\n    position: relative;\n    font-size: 19.1px;\n    font-family: \"cronos-pro\", serif;\n    font-weight: 400;\n  }\n  .SpotlightItem-description .conditionallyVisibleBlock {\n    border: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n    padding: 8px;\n    border-radius: 4px;\n  }\n  .SpotlightItem-description .ck-cta-button {\n    color: #fff;\n    width: fit-content;\n    cursor: pointer;\n    display: block;\n    padding: 12px 16px;\n    font-size: 14px;\n    min-width: 30px;\n    box-shadow: none;\n    transition: background-color 0.3s ease;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 600;\n    line-height: 20px;\n    border-radius: 3px;\n    text-transform: none;\n    background-color: #5f9b65;\n  }\n  .SpotlightItem-description .ck-cta-button-centered {\n    margin: auto;\n  }\n  .SpotlightItem-description .footnotes {\n    font-size: 0.9em;\n    border-top: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n    margin-top: 40px;\n    padding-top: 40px;\n  }\n  .SpotlightItem-description .footnotes-sep {\n    display: none;\n  }\n  .SpotlightItem-description hr {\n    width: 100%;\n    border: none;\n    height: 100%;\n    margin: 32px 0;\n    display: flex;\n    background: transparent;\n    text-align: center;
116\n    align-items: center;\n    justify-content: center;\n  }\n  .SpotlightItem-description p {\n    margin-top: .5em;\n    margin-bottom: .5em;\n  }\n@media (max-width:599.95px) {\n  .SpotlightItem-description {\n    display: none;\n  }\n}\n  .SpotlightItem-description p:first-child {\n    margin-top: 0;\n  }\n  style~.SpotlightItem-description p {\n    margin-top: 0;\n  }\n  .SpotlightItem-description p:last-child {\n    margin-bottom: 0;\n  }\n  .SpotlightItem-description hr:after {\n    color: light-dark(rgba(0,0,0,0.26),rgba(255,255,255,0.26));\n    content: \"•••\";\n    font-size: 13px;\n    margin-left: 12px;\n    letter-spacing: 12px;\n  }\n  .SpotlightItem-description .footnotes li {\n    font-size: 0.9em;\n    line-height: 21.06px;\n  }\n  .SpotlightItem-description .footnotes sup {\n    margin-right: 10px;\n  }\n  .SpotlightItem-description .footnotes ol {\n    margin-block-start: 1em;\n    margin-inline-start: 1em;\n    padding-inline-start: 0;\n  }\n  .SpotlightItem-description .footnotes blockquote {\n    padding: 1px;\n    font-size: 0.9em;\n    margin-top: -10px;\n    line-height: 1.5em;\n    padding-left: 3px;\n  }\n  .SpotlightItem-description .ck-cta-button:hover {\n    color: #fff;\n    opacity: 1;\n    background-color: #303f9f;\n  }\n  .SpotlightItem-description .ck-cta-button:visited, .SpotlightItem-description .ck-cta-button.visited {\n    color: #fff;\n  }\n  .SpotlightItem-description .ck-cta-button:visited:hover, .SpotlightItem-description .ck-cta-button.visited:hover {\n    color: #fff;\n  }\n  .SpotlightItem-description .ck-cta-button:disabled {\n    color: #fff;\n    opacity: 0.5;\n    background-color: #5f9b65;\n  }\n  .SpotlightItem-description .conditionallyVisibleBlock.defaultHidden {\n    display: none;\n  }\n  .SpotlightItem-description .llm-content-block h1, .SpotlightItem-description .llm-content-block h2, .SpotlightItem-description .llm-content-block h3, .SpotlightItem-description .llm-content-block h4, .SpotlightItem-description .llm-content-block h5, .SpotlightItem-description .llm-content-block h6 {\n    font-family: inherit;\n  }\n  .SpotlightItem-description .llm-content-block li, .SpotlightItem-description .llm-content-block blockquote {\n    font-family: inherit;\n  }\n  .SpotlightItem-description .llm-content-block::before {\n    top: 2px;\n    color: light-dark(rgba(0,0,0,0.6),rgba(255,255,255,0.6));\n    content: attr(data-model-name);\n    display: inline-block;\n    position: relative;\n    font-size: 0.85em;\n    font-weight: 600;\n    line-height: 1.3;\n    border-right: 1px solid light-dark(#bdbdbd,#818181);\n    font-variant: small-caps;\n    padding-right: 6px;\n  }\n  .SpotlightItem-description .llm-content-block:has(> .llm-content-block-content > p:first-child)::before {\n    float: left;\n    margin-right: 8px;\n    margin-bottom: 0;\n  }\n  .SpotlightItem-description .llm-content-block:not(:has(> .llm-content-block-content > p:first-child))::before {\n    float: none;\n    width: fit-content;\n    display: block;\n    margin-top: 1em;\n    margin-right: 0;\n    margin-bottom: 1em;\n  }\n  .SpotlightItem-description .llm-content-block:has(> .llm-content-block-content > p:last-child) > .llm-content-block-content > p:last-child::after {\n    color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n    content: \"⊙\";\n    opacity: 0.6;\n    font-size: 0.85em;\n    border-left: 1px solid light-dark(#bdbdbd,#818181);\n    font-weight: 300;\n    margin-left: 9px;\n    padding-left: 3px;\n    letter-spacing: 2.1px;\n  }\n  .SpotlightItem-description .llm-content-block > .llm-content-block-content > p:last-child:has(> br:only-child) {\n    margin: 0;\n    display: inline-block;\n    min-width: 0.6em;\n  }\n  .SpotlightItem-description .llm-content-block > .llm-content-block-content > p:last-child:has(> br:only-child) > br:only-child {\n    display: none;\n  }\n  .SpotlightItem-description .llm-content-block > .llm-content-block-content > p:last-child:has(> br:only-child)::after {\n    color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n    content: \"⊙\";\n    display: inline-block;\n    opacity: 0.6;\n    font-size: 0.85em;\n    border-left: 1px solid light-dark(#bdbdbd,#818181);\n    font-weight: 300;\n    line-height: inherit;\n    margin-left: 0;\n    padding-left: 3px;\n    letter-spacing: 2.1px;\n  }\n  .SpotlightItem-description .llm-content-block:not(:has(> .llm-content-block-content > p:last-child)) > .llm-content-block-content::after {\n    color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n    width: fit-content;\n    content: \"⊙\";\n    display: block;\n    opacity: 0.6;\n    font-size: 0.85em;\n    margin-top: 1em;\n    border-left: 1px solid light-dark(#bdbdbd,#818181);\n    font-weight: 300;\n    line-height: 1.3;\n    padding-left: 3px;\n    margin-bottom: 1em;\n    letter-spacing: 2.1px;\n  }\n  .SpotlightItem-description .llm-content-block > .llm-content-block-content > :first-child {\n    margin-top: 0;\n  }\n  .SpotlightItem-description .llm-content-block > .llm-content-block-content > :last-child {\n    margin-bottom: 0;\n  }\n  .SpotlightItem-description .detailsBlockTitle::-webkit-details-marker {\n    display: none;\n  }\n  .SpotlightItem-description .detailsBlockTitle::marker {\n    content: \"\";\n  }\n  .SpotlightItem-description .detailsBlockTitle::before {\n    top: calc((1lh - 8px) / 2);\n    left: 8px;\n    width: 0;\n    cursor: pointer;\n    height: 0;\n    content: \"\";\n    position: absolute;\n    transform: rotate(0deg);\n    transition: transform 0.15s ease;\n    border-color: transparent transparent transparent light-dark(#757575,#c6c6c6);\n    border-style: solid;\n    border-width: 4px 0 4px 6px;\n  }\n  .SpotlightItem-description div.lwartifacts-preview iframe {\n    width: 100%;\n    border: none;\n    height: 525px;\n  }\n  .SpotlightItem-description figure.media div[data-oembed-url*=\"lwartifacts.vercel.app\"] 
116iframe {\n    width: 100%;\n    border: none;\n    height: 525px;\n  }\n  .SpotlightItem-description figure.media div[data-oembed-url*=\"youtube.com\"] iframe, .SpotlightItem-description figure.media div[data-oembed-url*=\"youtu.be\"] iframe, .SpotlightItem-description div.youtube-preview iframe {\n    top: 0;\n    left: 0;\n    width: 100%;\n    border: none;\n    height: 100%;\n    position: absolute;\n  }\n  .SpotlightItem-description div.viewpoints-preview iframe {\n    width: 100%;\n    border: none;\n    height: 300px;\n  }\n  .SpotlightItem-description div.estimaker-preview iframe {\n    width: 100%;\n    border: none;\n    height: 400px;\n  }\n  .SpotlightItem-description div.owid-preview iframe {\n    width: 100%;\n    border: none;\n    height: 400px;\n  }\n  .SpotlightItem-description div.metaforecast-preview iframe {\n    width: 100%;\n    border: none;\n    height: 400px;\n  }\n  .SpotlightItem-description div.strawpoll-embed iframe {\n    width: 100%;\n    border: none;\n    height: 400px;\n  }\n  .SpotlightItem-description div.calendly-preview iframe {\n    width: calc(100% - 10px);\n    border: 2px solid;\n    height: 750px;\n    padding: 0;\n    margin-left: 5px;\n    border-color: light-dark(#eeeeee,#363636);\n    margin-right: 5px;\n    border-radius: 10px;\n  }\n  .SpotlightItem-description div.neuronpedia-preview iframe {\n    width: 100%;\n    border: 1px solid;\n    height: 360px;\n    max-width: 639px;\n    border-color: light-dark(#e0e0e0,#505050);\n    border-radius: 6px;\n  }\n  .SpotlightItem-description div.manifold-preview iframe {\n    width: 100%;\n    border: none;\n    height: 400px;\n  }\n  .SpotlightItem-description div.metaculus-preview iframe {\n    width: 100%;\n    border: none;\n    height: 400px;\n  }\n  .SpotlightItem-description .spoilers > p:hover ~ p *, .SpotlightItem-description .spoilers > p:hover ~ p a::after {\n    color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description .spoilers > p:hover ~ p code {\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description .spoilers > p:hover ~ p img, .SpotlightItem-description .spoilers > p:hover ~ p video, .SpotlightItem-description .spoilers > p:hover ~ p iframe, .SpotlightItem-description .spoilers > p:hover ~ p svg, .SpotlightItem-description .spoilers > p:hover ~ p canvas {\n    opacity: 0;\n  }\n  .SpotlightItem-description div.spoilers:not(:hover) {\n    color: light-dark(#000,#1b1b1b);\n    caret-color: #fff;\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description div.spoilers:hover {\n    background: light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n  }\n  .SpotlightItem-description div.spoilers > p {\n    margin: 0 !important;\n    padding: 0.5em 8px !important;\n  }\n  .SpotlightItem-description div.spoilers:not(:hover) *, .SpotlightItem-description div.spoilers:not(:hover) a::after {\n    color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description div.spoilers:not(:hover) code {\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description div.spoilers:not(:hover) img, .SpotlightItem-description div.spoilers:not(:hover) video, .SpotlightItem-description div.spoilers:not(:hover) iframe, .SpotlightItem-description div.spoilers:not(:hover) svg, .SpotlightItem-description div.spoilers:not(:hover) canvas {\n    opacity: 0;\n  }\n  .SpotlightItem-description .spoiler:not(:hover) {\n    color: light-dark(#000,#1b1b1b);\n    caret-color: #fff;\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description .spoiler:not(:hover) *, .SpotlightItem-description .spoiler:not(:hover) a::after {\n    color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description .spoiler:not(:hover) code {\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-description .spoiler:not(:hover) img, .SpotlightItem-description .spoiler:not(:hover) video, .SpotlightItem-description .spoiler:not(:hover) iframe, .SpotlightItem-description .spoiler:not(:hover) svg, .SpotlightItem-description .spoiler:not(:hover) canvas {\n    opacity: 0;\n  }\n  .SpotlightItem-description td p, .SpotlightItem-description th p {\n    margin-top: 0.5em;\n    margin-bottom: 0.5em;\n  }\n  .SpotlightItem-description td p:first-of-type, .SpotlightItem-description th p:first-of-type {\n    margin-top: 0;\n  }\n  .SpotlightItem-description a u, .SpotlightItem-description a:hover u, .SpotlightItem-description a:active u {\n    text-decoration: none;\n  }\n  .SpotlightItem-title {\n    display: flex;\n    font-size: 20px;
116\n    align-items: center;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    line-height: 1.2em;\n    font-variant: small-caps;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-subtitle {\n    color: light-dark(#616161,#d5d5d5);\n    font-size: 15px;\n    font-style: italic;\n    margin-top: -1px;\n    min-height: 25px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-image {\n    top: 0;\n    right: 0;\n    height: 100%;\n    position: absolute;\n    border-top-right-radius: 0;\n    border-bottom-right-radius: 0;\n  }\n  .SpotlightItem-imageFade {\n    mask: linear-gradient(to right, transparent 0,#fff 80%,#fff 100%);\n    -webkit-mask-image: linear-gradient(to right, transparent 0,#fff 80%,#fff 100%);\n  }\n  .SpotlightItem-imageFadeCustom {\n    mask: linear-gradient(to right, transparent 0,transparent 30%,#fff 90%,#fff 100%);\n    -webkit-mask-image: linear-gradient(to right, transparent 0,transparent 30%,#fff 90%,#fff 100%);\n  }\n  .SpotlightItem-author {\n    color: light-dark(#757575,#c6c6c6);\n    margin-top: 4px;\n  }\n  .SpotlightItem-authorName {\n    color: #3f51b5;\n  }\n@media (min-width:960px) {\n  .SpotlightItem-editAllButton {\n    top: 6px;\n    right: -28px;\n    position: absolute;\n  }\n}\n@media (max-width:959.95px) {\n  .SpotlightItem-editAllButton {\n    top: 4px;\n    right: 8px;\n    position: absolute;\n  }\n}\n@media (min-width:960px) {\n  .SpotlightItem-draftButton {\n    top: 35px;\n    right: -28px;\n    position: absolute;\n  }\n}\n@media (max-width:959.95px) {\n  .SpotlightItem-draftButton {\n    top: 33px;\n    right: 8px;\n    position: absolute;\n  }\n}\n@media (min-width:960px) {\n  .SpotlightItem-deleteButton {\n    right: -28px;\n    bottom: 0;\n    position: absolute;\n  }\n}\n@media (max-width:959.95px) {\n  .SpotlightItem-deleteButton {\n    right: 8px;\n    bottom: 0;\n    position: absolute;\n  }\n}\n  .SpotlightItem-editAllButtonIcon {\n    width: 20px;\n  }\n  .SpotlightItem-adminButtonIcon {\n    width: 18px;\n    cursor: pointer;\n    opacity: 0;\n    z-index: 2;\n  }\n@media (max-width:959.95px) {\n  .SpotlightItem-adminButtonIcon {\n    color: light-dark(#fff,#000000);\n    width: 16px;\n    opacity: 0.2;\n  }\n}\n  .SpotlightItem-adminButtonIcon:hover {\n    opacity: 0.5;\n  }\n  .SpotlightItem-editDescriptionButton {\n    margin-left: 8px;\n  }\n  .SpotlightItem-editDescription .form-input {\n    margin: 0;\n  }\n  .SpotlightItem-editDescription .EditorFormComponent-commentEditorHeight {\n    min-height: unset;\n  }\n  .SpotlightItem-editDescription .EditorFormComponent-commentEditorHeight .ck.ck-content {\n    min-height: unset;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    font-size: 15.08px;\n    word-break: break-word;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 1.65rem;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-editDescription .EditorFormComponent-ckEditorStyles .ck.ck-content {\n    margin-left: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-editor__editable_inline {\n    border: none !important;\n    padding: 0;\n  }\n  .SpotlightItem-editDescription .form-submit button {\n    right: 0;\n    bottom: -38px;\n    position: absolute;\n    margin-left: 12px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable pre {\n    border: solid 1px light-dark(#e0e0e0,#505050);\n    margin: 1em 0;\n    padding: 13px;\n    white-space: pre-wrap;\n    border-radius: 5px;\n    background-color: light-dark(#f5f5f5,#262626);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable pre.code-block, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable pre[data-highlight-language] {\n    margin: 8px 0;\n    display: block;\n    padding: 8px 8px 8px 36px;\n    position: relative;\n    tab-size: 2;\n    font-size: 13px;\n    box-sizing: border-box;\n    overflow-x: auto;\n    font-family: \"Liberation Mono\",Menlo,Courier,monospace;\n    line-height: 1.53;\n    white-space: pre;\n    overflow-wrap: normal;\n    background-color: light-dark(#f5f5f5,#262626);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable pre.code-block::before, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable pre[data-highlight-language]::before {\n    top: 0;\n    left: 0;\n    color: light-dark(#757575,#c6c6c6);\n    content: attr(data-gutter);\n    padding: 8px;\n    position: absolute;\n    min-width: 25px;\n    text-align: right;\n    white-space: pre-wrap;\n    border-right: 1px solid light-dark(#e0e0e0,#505050);\n    background-color: light-dark(#eeeeee,#363636);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable span.math-tex {\n    display: inline-block;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable code {\n    font-size: .7em;\n    font-family: \"Liberation Mono\",Menlo,Courier,monospace;\n    font-weight: 400;\n    line-height: 1.42;\n    padding-top: 3px;\n    border-radius: 2px;\n    padding-bottom: 3px;\n    background-color: light-dark(#f5f5f5,#262626);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-comment {\n    color: light-dark(slategray, #8b949e);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-deleted {\n    border-image: linear-gradient(to right, #ffcecb 50%, #ffebe9 50%) f
116ill 0/0/0 100vw;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-inserted {\n    border-image: linear-gradient(to right, #aceebb 50%, #dafbe1 50%) fill 0/0/0 100vw;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-unchanged {\n    border-image: linear-gradient(to right, #ddd 50%, #f0f2f5 50%) fill 0/0/0 100vw;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-punctuation {\n    color: light-dark(#999, #8b949e);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-property {\n    color: light-dark(#905, #ff7b72);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-selector {\n    color: light-dark(#690, #7ee787);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-operator {\n    color: light-dark(#9a6e3a, #d2a8ff);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-attr {\n    color: light-dark(#07a, #79c0ff);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-variable {\n    color: light-dark(#e90, #ffa657);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .code-token-function {\n    color: light-dark(#dd4a68, #d2a8ff);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable blockquote {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    margin: 0;\n    font-size: 18.2px;\n    border-left: solid 3px light-dark(#e0e0e0,#505050);\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 26px;\n    padding-top: 16px;\n    padding-left: 16px;\n    padding-right: 16px;\n    padding-bottom: 16px;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable li {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    font-size: 18.2px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 26px;\n    margin-bottom: 6.5px;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h1 {\n    color: light-dark(#424242,#e8e8e8);\n    font-size: 36.4px;\n    margin-top: 1em;\n    font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n    font-weight: 500;\n    line-height: 1.13333em;\n    margin-left: -.02em;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h1:first-child {\n    margin-top: 0;\n    margin-block-start: -3px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h2 {\n    color: light-dark(#424242,#e8e8e8);\n    font-size: 26px;\n    margin-top: 1em;\n    font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n    font-weight: 400;\n    line-height: 1.20588em;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h2:first-child {\n    margin-top: 0;\n    margin-block-start: -2px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h3 {\n    color: light-dark(#616161,#d5d5d5);\n    font-size: 20.8px;\n    margin-top: 1em;\n    font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n    font-weight: 400;\n    line-height: 1.20588em;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h3:first-child {\n    margin-top: 0;\n    margin-block-start: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable h4 {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    font-size: 18.2px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 600;
116\n    line-height: 26px;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable img {\n    max-width: 100%;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable sup {\n    top: -0.6em;\n    position: relative;\n    font-size: 65%;\n    line-height: 0;\n    vertical-align: baseline;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable sub {\n    top: 0.2em;\n    position: relative;\n    font-size: 70%;\n    padding-right: 0.07em;\n    vertical-align: baseline;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable a, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable a:hover, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable a:active {\n    color: light-dark(#3f51b5,#7581d1);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable a:visited, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable a.visited {\n    color: light-dark(#8c4298,#798754);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable table {\n    width: 100%;\n    border: 1px double light-dark(#b3b3b3,#8d8d8d);\n    height: 100%;\n    margin: auto;\n    text-align: left;\n    border-spacing: 0;\n    border-collapse: collapse;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable figure.table {\n    width: fit-content;\n    height: fit-content;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable td, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable th {\n    border: 1px double light-dark(#d9d9d9,#5b5b5b);\n    padding: .4em;\n    min-width: 2em;\n    word-break: normal;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable th {\n    background: light-dark(#fafafa,#181818);\n    font-weight: 700;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable figure {\n    margin: 1em auto;\n    max-width: 100%;\n    text-align: center;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable figcaption {\n    color: light-dark(rgba(0,0,0,0.54),rgba(255,255,255,0.54));\n    font-size: .85em;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 1.375em;\n    font-variant-numeric: lining-nums;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable ol > li > ol {\n    list-style: lower-alpha;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable ol > li > ol > li > ol {\n    list-style: lower-roman;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .nested-list-item {\n    list-style-type: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable u {\n    text-decoration: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable p.spoiler {\n    margin: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .spoiler {\n    padding: 8px;\n    min-height: 15px;\n    pointer-events: auto;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.spoilers {\n    margin: 1em 0;\n    overflow: auto;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable p.spoiler-v2 {\n    margin: 0;\n    padding: 0.5em 8px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .spoilers:not(:hover)::selection, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .spoilers:not(:hover) ::selection {\n    background-color: transparent;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .spoilers > p:hover ~ p {\n    color: light-dark(#000,#1b1b1b);\n    caret-color: #fff;\n    background-color: light-dark(#000,#1b1b1b);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.metaculus-preview {\n    background-color: #2c3947;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.estimaker-preview {\n    display: flex;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.viewpoints-preview {\n    display: flex;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable figure.media div[data-oembed-url*=\"youtube.com\"], .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable figure.media div[data-oembed-url*=\"youtu.be\"], .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.youtube-preview {\n    height: 0;\n    position: relative;\n    padding-bottom: 56.2493%;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnote-item > * {\n    vertical-align: text-top;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnote-back-link {\n    top: -0.2em;\n    position: relative;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes .footnote-back-link > sup {\n    margin-right: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnote-content {\n    width: 95%;\n    display: inline-block;\n    padding: 0 0.3em;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlock {\n    border: none !important;\n    margin-top: .75em;\n    margin-bottom: .75em;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle {\n    padding: 2px 0 2px 22px;\n    position: relative;\n    background: transparent !important;\n    list-style: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable summary.detailsBlockTitle {\n    cursor: pointer;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockContent {\n    margin-top: 8px;\n    border-left: 1px solid light-dark(#bdbdbd,#818181);\n    margin-left: 11px;\n    padding-left: 14px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockContent > *:first-child, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle > *:first-child {\n    margin-top: 0 !important;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockContent > *:last-child, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle > *:last-child {\n    margin-bottom: 0 !important;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable details.detailsBlock[open] > .detailsBlockTitle::before, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.detailsBlockTitle::before {\n    transform: rotate(90deg);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlock.closed div.detailsBlockTitle::before, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockClosed div.detailsBlockTitle::before {\n    transform: rotate(0deg);\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlock.closed .detailsBlockContent, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockClosed .detailsBlockContent {\n    display: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle p:has(> br[data-cke-filler=true]:only-child)::after {\n    top: 0;\n    color: light-dark(rgba(0,0,0,0.3),rgba(255,255,255,0.3));\n    content: \"Collapsible Section Title\";\n    position: absolute;\n    pointer-events: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block {\n    margin: 1em 0;\n    opacity: 0.94;\n    position: relative;\n    font-size: 19.1px;\n    font-family: \"cronos-pro\", serif;\n    font-weight: 400;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .conditionallyVisibleBlock {\n    border: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n    padding: 8px;\n    border-radius: 4px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button {\n    color: #fff;\n    width: fit-content;\n    cursor: pointer;\n    display: block;\n    padding: 12px 16px;\n    font-size: 14px;\n    min-width: 30px;\n    box-shadow: none;\n    transition: background-color 0.3s ease;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 600;\n    line-height: 20px;\n    border-radius: 3px;\n    text-transform: none;\n    background-color: #5f9b65;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button-centered {\n    margin: auto;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes {\n    font-size: 0.9em;\n    border-top: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n    margin-top: 40px;\n    padding-top: 40px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes-sep {\n    display: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable hr {\n    width: 100%;\n    border: none;\n    height: 100%;\n    margin: 32px 0;\n    display: flex;\n    background: transparent;\n    text-align: center;
116\n    align-items: center;\n    justify-content: center;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable p {\n    margin-top: .5em;\n    margin-bottom: .5em;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable p:first-child {\n    margin-top: 0;\n  }\n  style~.SpotlightItem-editDescription .ck.ck-content.ck-editor__editable p {\n    margin-top: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable p:last-child {\n    margin-bottom: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable hr:after {\n    color: light-dark(rgba(0,0,0,0.26),rgba(255,255,255,0.26));\n    content: \"•••\";\n    font-size: 13px;\n    margin-left: 12px;\n    letter-spacing: 12px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes li {\n    font-size: 0.9em;\n    line-height: 21.06px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes sup {\n    margin-right: 10px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes ol {\n    margin-block-start: 1em;\n    margin-inline-start: 1em;\n    padding-inline-start: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .footnotes blockquote {\n    padding: 1px;\n    font-size: 0.9em;\n    margin-top: -10px;\n    line-height: 1.5em;\n    padding-left: 3px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button:hover {\n    color: #fff;\n    opacity: 1;\n    background-color: #303f9f;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button:visited, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button.visited {\n    color: #fff;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button:visited:hover, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button.visited:hover {\n    color: #fff;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .ck-cta-button:disabled {\n    color: #fff;\n    opacity: 0.5;\n    background-color: #5f9b65;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .conditionallyVisibleBlock.defaultHidden {\n    display: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block h1, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block h2, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block h3, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block h4, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block h5, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block h6 {\n    font-family: inherit;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block li, .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block blockquote {\n    font-family: inherit;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block::before {\n    top: 2px;\n    color: light-dark(rgba(0,0,0,0.6),rgba(255,255,255,0.6));\n    content: attr(data-model-name);\n    display: inline-block;\n    position: relative;\n    font-size: 0.85em;\n    font-weight: 600;\n    line-height: 1.3;\n    border-right: 1px solid light-dark(#bdbdbd,#818181);\n    font-variant: small-caps;\n    padding-right: 6px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block:has(> .llm-content-block-content > p:first-child)::before {\n    float: left;\n    margin-right: 8px;\n    margin-bottom: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block:not(:has(> .llm-content-block-content > p:first-child))::before {\n    float: none;\n    width: 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padding-left: 3px;\n    margin-bottom: 1em;\n    letter-spacing: 2.1px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block > .llm-content-block-content > :first-child {\n    margin-top: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .llm-content-block > .llm-content-block-content > :last-child {\n    margin-bottom: 0;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle::-webkit-details-marker {\n    display: none;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle::marker {\n    content: \"\";\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable .detailsBlockTitle::before {\n    top: calc((1lh - 8px) / 2);\n    left: 8px;\n    width: 0;\n    cursor: pointer;\n    height: 0;\n    content: \"\";\n    position: absolute;\n    transform: rotate(0deg);\n    transition: transform 0.15s ease;\n    border-color: transparent transparent transparent light-dark(#757575,#c6c6c6);\n    border-style: solid;\n    border-width: 4px 0 4px 6px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable div.lwartifacts-preview iframe {\n    width: 100%;\n    border: none;\n    height: 525px;\n  }\n  .SpotlightItem-editDescription .ck.ck-content.ck-editor__editable figure.media div[data-oembed-url*=\"lwartifacts.vercel.app\"] 
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width: auto;\n    bottom: calc(-42% + 8px);\n    height: 240%;\n  }\n}\n  .SpotlightItem-splashImageContainer {\n    top: 0;\n    right: 0;\n    width: 100%;\n    height: 100%;\n    overflow: hidden;\n    position: absolute;\n  }\n@media (max-width:599.95px) {\n  .SpotlightItem-splashImageContainer {\n    height: 100% !important;\n  }\n}\n  .SpotlightItem-reverseIcon {\n    transform: rotate(180deg);\n  }\n  .SpotlightItem-reviews {\n    width: 100%;\n    max-width: 765px;\n    background: light-dark(#fff,#000000);\n    border-top: 1px solid light-dark(rgba(0,0,0,0.08),rgba(255,255,255,0.08));\n  }\n@media (max-width:599.95px) {\n  .SpotlightItem-reviews {\n    display: none;\n  }\n}\n  .SpotlightItem-review.SpotlightItem-review .CommentFrame-node {\n    border: none;\n    margin: 0;\n  }\n  .SpotlightItem-review .SingleLineComment-commentInfo {\n    padding-left: 13px;\n    border-radius: 0;\n    background-color: light-dark(rgba(255,255,255,.75),rgba(0,0,0,.75));\n  }\n  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light-dark(#dd4a68, #d2a8ff);\n}\n::highlight(code-hl-deleted) {\n  background-color: light-dark(#ffcecb, #3d1214);\n}
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relative;\n}\n.SingleLineComment-shortformIcon {\n  margin-top: 4px;\n}\n.SingleLineComment-tagIcon {\n  display: flex;\n  margin-left: -2px;\n  margin-right: 8px;\n  flex-direction: column;\n  justify-content: center;\n}\n.SingleLineComment-tagIcon svg {\n  fill: light-dark(#757575,#c6c6c6);\n  width: 12px;\n  height: 12px;\n}\n.SingleLineComment-leadingInfo {\n  display: inline-block;\n  flex-grow: 0;\n  text-align: center;\n  flex-shrink: 0;\n  padding-top: 5px;\n  padding-right: 5px;\n}\n.SingleLineComment-date {\n  display: inline-block;\n  padding: 5px;\n  padding-left: 8px;\n  padding-right: 8px;\n}\n.SingleLineComment-truncatedHighlight {\n  display: inline;\n  padding: 5px;\n  overflow: hidden;\n  flex-grow: 1;\n  margin-top: 0;\n  margin-bottom: 0;\n  text-overflow: ellipsis;\n}\n.SingleLineComment-truncatedHighlight * {\n  display: inline;\n}\n.SingleLineComment-truncatedHighlight blockquote, .SingleLineComment-truncatedHighlight br, .SingleLineComment-truncatedHighlight figure, .SingleLineComment-truncatedHighlight img {\n  display: none;\n}\n.SingleLineComment-truncatedHighlight p {\n  margin-right: 6px;\n}\n.SingleLineComment-truncatedHighlight strong {\n  font-weight: 400;\n}\n.SingleLineComment-isAnswer {\n  font-size: 15.08px;\n  line-height: 19.8px;\n}\n.SingleLineComment-isAnswer a, .SingleLineComment-isAnswer a:hover {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  text-shadow: none;\n  background-image: none;\n}\n.SingleLineComment-odd {\n  background-color: light-dark(#fff,#000000);\n}\n.SingleLineComment-odd:hover {\n  background-color: light-dark(#f3f3f3,#2b2b2b);\n}\n.SingleLineComment-metaNotice {\n  color: #607e88;\n  font-size: 1rem;\n  font-style: italic;\n  margin-left: 4px;\n  margin-right: 8px;\n}\n.SingleLineComment-postTitle {\n  color: #607e88;\n  font-size: 1rem;\n  font-style: italic;\n  margin-left: 4px;\n  margin-right: 20px;\n}\n.SingleLineComment-preview {\n  width: 400px;\n}\n.SingleLineComment-deemphasize {\n  opacity: 0.5;\n}");_embedStyles("ContentStyles",-1,".ContentStyles-base {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 18.2px;\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 26px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-base pre {\n  border: solid 1px light-dark(#e0e0e0,#505050);\n  margin: 1em 0;\n  padding: 13px;\n  white-space: pre-wrap;\n  border-radius: 5px;\n  background-color: light-dark(#f5f5f5,#262626);\n}\n.ContentStyles-base pre.code-block, .ContentStyles-base pre[data-highlight-language] {\n  margin: 8px 0;\n  display: block;\n  padding: 8px 8px 8px 36px;\n  position: relative;\n  tab-size: 2;\n  font-size: 13px;\n  box-sizing: border-box;\n  overflow-x: auto;\n  font-family: \"Liberation Mono\",Menlo,Courier,monospace;\n  line-height: 1.53;\n  white-space: pre;\n  overflow-wrap: normal;\n  background-color: light-dark(#f5f5f5,#262626);\n}\n.ContentStyles-base pre.code-block::before, .ContentStyles-base pre[data-highlight-language]::before {\n  top: 0;\n  left: 0;\n  color: light-dark(#757575,#c6c6c6);\n  content: attr(data-gutter);\n  padding: 8px;\n  position: absolute;\n  min-width: 25px;\n  text-align: right;\n  white-space: pre-wrap;\n  border-right: 1px solid light-dark(#e0e0e0,#505050);\n  background-color: light-dark(#eeeeee,#363636);\n}\n.ContentStyles-base span.math-tex {\n  display: inline-block;\n}\n.ContentStyles-base code {\n  font-size: .7em;\n  font-family: \"Liberation Mono\",Menlo,Courier,monospace;\n  font-weight: 400;\n  line-height: 1.42;\n  padding-top: 3px;\n  border-radius: 2px;\n  padding-bottom: 3px;\n  background-color: 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116ill 0/0/0 100vw;\n}\n.ContentStyles-base .code-token-inserted {\n  border-image: linear-gradient(to right, #aceebb 50%, #dafbe1 50%) fill 0/0/0 100vw;\n}\n.ContentStyles-base .code-token-unchanged {\n  border-image: linear-gradient(to right, #ddd 50%, #f0f2f5 50%) fill 0/0/0 100vw;\n}\n.ContentStyles-base .code-token-punctuation {\n  color: light-dark(#999, #8b949e);\n}\n.ContentStyles-base .code-token-property {\n  color: light-dark(#905, #ff7b72);\n}\n.ContentStyles-base .code-token-selector {\n  color: light-dark(#690, #7ee787);\n}\n.ContentStyles-base .code-token-operator {\n  color: light-dark(#9a6e3a, #d2a8ff);\n}\n.ContentStyles-base .code-token-attr {\n  color: light-dark(#07a, #79c0ff);\n}\n.ContentStyles-base .code-token-variable {\n  color: light-dark(#e90, #ffa657);\n}\n.ContentStyles-base .code-token-function {\n  color: light-dark(#dd4a68, #d2a8ff);\n}\n.ContentStyles-base blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 18.2px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 26px;\n  padding-top: 16px;\n  padding-left: 16px;\n  padding-right: 16px;\n  padding-bottom: 16px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-base li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 18.2px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 26px;\n  margin-bottom: 6.5px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-base h1 {\n  color: light-dark(#424242,#e8e8e8);\n  font-size: 36.4px;\n  margin-top: 1em;\n  font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n  font-weight: 500;\n  line-height: 1.13333em;\n  margin-left: -.02em;\n}\n.ContentStyles-base h1:first-child {\n  margin-top: 0;\n  margin-block-start: -3px;\n}\n.ContentStyles-base h2 {\n  color: light-dark(#424242,#e8e8e8);\n  font-size: 26px;\n  margin-top: 1em;\n  font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n  font-weight: 400;\n  line-height: 1.20588em;\n}\n.ContentStyles-base h2:first-child {\n  margin-top: 0;\n  margin-block-start: -2px;\n}\n.ContentStyles-base h3 {\n  color: light-dark(#616161,#d5d5d5);\n  font-size: 20.8px;\n  margin-top: 1em;\n  font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n  font-weight: 400;\n  line-height: 1.20588em;\n}\n.ContentStyles-base h3:first-child {\n  margin-top: 0;\n  margin-block-start: 0;\n}\n.ContentStyles-base h4 {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 18.2px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 600;\n  line-height: 26px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-base img {\n  max-width: 100%;\n}\n.ContentStyles-base sup {\n  top: -0.6em;\n  position: relative;\n  font-size: 65%;\n  line-height: 0;\n  vertical-align: baseline;\n}\n.ContentStyles-base sub {\n  top: 0.2em;\n  position: relative;\n  font-size: 70%;\n  padding-right: 0.07em;\n  vertical-align: baseline;\n}\n.ContentStyles-base a, .ContentStyles-base a:hover, .ContentStyles-base a:active {\n  color: light-dark(#3f51b5,#7581d1);\n}\n.ContentStyles-base a:visited, .ContentStyles-base a.visited {\n  color: light-dark(#8c4298,#798754);\n}\n.ContentStyles-base table {\n  width: 100%;\n  border: 1px double light-dark(#b3b3b3,#8d8d8d);\n  height: 100%;\n  margin: auto;\n  text-align: left;\n  border-spacing: 0;\n  border-collapse: collapse;\n}\n.ContentStyles-base figure.table {\n  width: fit-content;\n  height: fit-content;\n}\n.ContentStyles-base td, .ContentStyles-base th {\n  border: 1px double light-dark(#d9d9d9,#5b5b5b);\n  padding: .4em;\n  min-width: 2em;\n  word-break: normal;\n}\n.ContentStyles-base th {\n  background: light-dark(#fafafa,#181818);\n  font-weight: 700;\n}\n.ContentStyles-base figure {\n  margin: 1em auto;\n  max-width: 100%;\n  text-align: center;\n}\n.ContentStyles-base figcaption {\n  color: light-dark(rgba(0,0,0,0.54),rgba(255,255,255,0.54));\n  font-size: .85em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 1.375em;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-base ol > li >
116 ol {\n  list-style: lower-alpha;\n}\n.ContentStyles-base ol > li > ol > li > ol {\n  list-style: lower-roman;\n}\n.ContentStyles-base .nested-list-item {\n  list-style-type: none;\n}\n.ContentStyles-base u {\n  text-decoration: none;\n}\n.ContentStyles-base p.spoiler {\n  margin: 0;\n}\n.ContentStyles-base .spoiler {\n  padding: 8px;\n  min-height: 15px;\n  pointer-events: auto;\n}\n.ContentStyles-base div.spoilers {\n  margin: 1em 0;\n  overflow: auto;\n}\n.ContentStyles-base p.spoiler-v2 {\n  margin: 0;\n  padding: 0.5em 8px;\n}\n.ContentStyles-base .spoilers:not(:hover)::selection, .ContentStyles-base .spoilers:not(:hover) ::selection {\n  background-color: transparent;\n}\n.ContentStyles-base .spoilers > p:hover ~ p {\n  color: light-dark(#000,#1b1b1b);\n  caret-color: #fff;\n  background-color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base div.metaculus-preview {\n  background-color: #2c3947;\n}\n.ContentStyles-base div.estimaker-preview {\n  display: flex;\n}\n.ContentStyles-base div.viewpoints-preview {\n  display: flex;\n}\n.ContentStyles-base figure.media div[data-oembed-url*=\"youtube.com\"], .ContentStyles-base figure.media div[data-oembed-url*=\"youtu.be\"], .ContentStyles-base div.youtube-preview {\n  height: 0;\n  position: relative;\n  padding-bottom: 56.2493%;\n}\n.ContentStyles-base .footnote-item > * {\n  vertical-align: text-top;\n}\n.ContentStyles-base .footnote-back-link {\n  top: -0.2em;\n  position: relative;\n}\n.ContentStyles-base .footnotes .footnote-back-link > sup {\n  margin-right: 0;\n}\n.ContentStyles-base .footnote-content {\n  width: 95%;\n  display: inline-block;\n  padding: 0 0.3em;\n}\n.ContentStyles-base .detailsBlock {\n  border: none !important;\n  margin-top: .75em;\n  margin-bottom: .75em;\n}\n.ContentStyles-base .detailsBlockTitle {\n  padding: 2px 0 2px 22px;\n  position: relative;\n  background: transparent !important;\n  list-style: none;\n}\n.ContentStyles-base summary.detailsBlockTitle {\n  cursor: pointer;\n}\n.ContentStyles-base .detailsBlockContent {\n  margin-top: 8px;\n  border-left: 1px solid light-dark(#bdbdbd,#818181);\n  margin-left: 11px;\n  padding-left: 14px;\n}\n.ContentStyles-base .detailsBlockContent > *:first-child, .ContentStyles-base .detailsBlockTitle > *:first-child {\n  margin-top: 0 !important;\n}\n.ContentStyles-base .detailsBlockContent > *:last-child, .ContentStyles-base .detailsBlockTitle > *:last-child {\n  margin-bottom: 0 !important;\n}\n.ContentStyles-base details.detailsBlock[open] > .detailsBlockTitle::before, .ContentStyles-base div.detailsBlockTitle::before {\n  transform: rotate(90deg);\n}\n.ContentStyles-base .detailsBlock.closed div.detailsBlockTitle::before, .ContentStyles-base .detailsBlockClosed div.detailsBlockTitle::before {\n  transform: rotate(0deg);\n}\n.ContentStyles-base .detailsBlock.closed .detailsBlockContent, .ContentStyles-base .detailsBlockClosed .detailsBlockContent {\n  display: none;\n}\n.ContentStyles-base .detailsBlockTitle p:has(> br[data-cke-filler=true]:only-child)::after {\n  top: 0;\n  color: light-dark(rgba(0,0,0,0.3),rgba(255,255,255,0.3));\n  content: \"Collapsible Section Title\";\n  position: absolute;\n  pointer-events: none;\n}\n.ContentStyles-base .llm-content-block {\n  margin: 1em 0;\n  opacity: 0.94;\n  position: relative;\n  font-size: 19.1px;\n  font-family: \"cronos-pro\", serif;\n  font-weight: 400;\n}\n.ContentStyles-base .conditionallyVisibleBlock {\n  border: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n  padding: 8px;\n  border-radius: 4px;\n}\n.ContentStyles-base .ck-cta-button {\n  color: #fff;\n  width: fit-content;\n  cursor: pointer;\n  display: block;\n  padding: 12px 16px;\n  font-size: 14px;\n  min-width: 30px;\n  box-shadow: none;\n  transition: background-color 0.3s ease;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 600;\n  line-height: 20px;\n  border-radius: 3px;\n  text-transform: none;\n  background-color: #5f9b65;\n}\n.ContentStyles-base .ck-cta-button-centered {\n  margin: auto;\n}\n.ContentStyles-base .footnotes {\n  font-size: 0.9em;\n  border-top: 1px solid light-dark(rgba(0,0,0,0.2),rgba(255,255,255,0.2));\n  margin-top: 40px;\n  padding-top: 40px;\n}\n.ContentStyles-base .footnotes-sep {\n  display: none;\n}\n.ContentStyles-base hr {\n  width: 100%;\n  border: none;\n  height: 100%;\n  margin: 32px 0;\n  display: flex;\n  background: transparent;\n  text-align: center;\n  align-items: center;\n  justify-content: center;\n}
116\n.ContentStyles-base hr:after {\n  color: light-dark(rgba(0,0,0,0.26),rgba(255,255,255,0.26));\n  content: \"•••\";\n  font-size: 13px;\n  margin-left: 12px;\n  letter-spacing: 12px;\n}\n.ContentStyles-base .footnotes li {\n  font-size: 0.9em;\n  line-height: 21.06px;\n}\n.ContentStyles-base .footnotes sup {\n  margin-right: 10px;\n}\n.ContentStyles-base .footnotes ol {\n  margin-block-start: 1em;\n  margin-inline-start: 1em;\n  padding-inline-start: 0;\n}\n.ContentStyles-base .footnotes blockquote {\n  padding: 1px;\n  font-size: 0.9em;\n  margin-top: -10px;\n  line-height: 1.5em;\n  padding-left: 3px;\n}\n.ContentStyles-base .ck-cta-button:hover {\n  color: #fff;\n  opacity: 1;\n  background-color: #303f9f;\n}\n.ContentStyles-base .ck-cta-button:visited, .ContentStyles-base .ck-cta-button.visited {\n  color: #fff;\n}\n.ContentStyles-base .ck-cta-button:visited:hover, .ContentStyles-base .ck-cta-button.visited:hover {\n  color: #fff;\n}\n.ContentStyles-base .ck-cta-button:disabled {\n  color: #fff;\n  opacity: 0.5;\n  background-color: #5f9b65;\n}\n.ContentStyles-base .conditionallyVisibleBlock.defaultHidden {\n  display: none;\n}\n.ContentStyles-base .llm-content-block h1, .ContentStyles-base .llm-content-block h2, .ContentStyles-base .llm-content-block h3, .ContentStyles-base .llm-content-block h4, .ContentStyles-base .llm-content-block h5, .ContentStyles-base .llm-content-block h6 {\n  font-family: inherit;\n}\n.ContentStyles-base .llm-content-block li, .ContentStyles-base .llm-content-block blockquote {\n  font-family: inherit;\n}\n.ContentStyles-base .llm-content-block::before {\n  top: 2px;\n  color: light-dark(rgba(0,0,0,0.6),rgba(255,255,255,0.6));\n  content: attr(data-model-name);\n  display: inline-block;\n  position: relative;\n  font-size: 0.85em;\n  font-weight: 600;\n  line-height: 1.3;\n  border-right: 1px solid light-dark(#bdbdbd,#818181);\n  font-variant: small-caps;\n  padding-right: 6px;\n}\n.ContentStyles-base .llm-content-block:has(> .llm-content-block-content > p:first-child)::before {\n  float: left;\n  margin-right: 8px;\n  margin-bottom: 0;\n}\n.ContentStyles-base .llm-content-block:not(:has(> .llm-content-block-content > p:first-child))::before {\n  float: none;\n  width: fit-content;\n  display: block;\n  margin-top: 1em;\n  margin-right: 0;\n  margin-bottom: 1em;\n}\n.ContentStyles-base .llm-content-block:has(> .llm-content-block-content > p:last-child) > .llm-content-block-content > p:last-child::after {\n  color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n  content: \"⊙\";\n  opacity: 0.6;\n  font-size: 0.85em;\n  border-left: 1px solid light-dark(#bdbdbd,#818181);\n  font-weight: 300;\n  margin-left: 9px;\n  padding-left: 3px;\n  letter-spacing: 2.1px;\n}\n.ContentStyles-base .llm-content-block > .llm-content-block-content > p:last-child:has(> br:only-child) {\n  margin: 0;\n  display: inline-block;\n  min-width: 0.6em;\n}\n.ContentStyles-base .llm-content-block > .llm-content-block-content > p:last-child:has(> br:only-child) > br:only-child {\n  display: none;\n}\n.ContentStyles-base .llm-content-block > .llm-content-block-content > p:last-child:has(> br:only-child)::after {\n  color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n  content: \"⊙\";\n  display: inline-block;\n  opacity: 0.6;\n  font-size: 0.85em;\n  border-left: 1px solid light-dark(#bdbdbd,#818181);\n  font-weight: 300;\n  line-height: inherit;\n  margin-left: 0;\n  padding-left: 3px;\n  letter-spacing: 2.1px;\n}\n.ContentStyles-base .llm-content-block:not(:has(> .llm-content-block-content > p:last-child)) > .llm-content-block-content::after {\n  color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n  width: fit-content;\n  content: \"⊙\";\n  display: block;\n  opacity: 0.6;\n  font-size: 0.85em;\n  margin-top: 1em;\n  border-left: 1px solid light-dark(#bdbdbd,#818181);\n  font-weight: 300;\n  line-height: 1.3;\n  padding-left: 3px;\n  margin-bottom: 1em;\n  letter-spacing: 2.1px;\n}\n.ContentStyles-base .llm-content-block > .llm-content-block-content > :first-child {\n  margin-top: 0;\n}\n.ContentStyles-base .llm-content-block > .llm-content-block-content > :last-child {\n  margin-bottom: 0;\n}\n.ContentStyles-base .detailsBlockTitle::-webkit-details-marker {\n  display: none;\n}\n.ContentStyles-base .detailsBlockTitle::marker {\n  content: \"\";\n}\n.ContentStyles-base .detailsBlockTitle::before {\n  top: calc((1lh - 8px) / 2);\n  left: 8px;\n  width: 0;\n  cursor: pointer;\n  height: 0;\n  content: \"\";\n  position: absolute;\n  transform: rotate(0deg);\n  transition: transform 0.15s ease;\n  border-color: transparent transparent transparent light-dark(#757575,#c6c6c6);\n  border-style: solid;\n  border-width: 4px 0 4px 6px;\n}\n.ContentStyles-base div.lwartifacts-preview iframe {\n  width: 100%;\n  border: none;\n  height: 525px;\n}\n.ContentStyles-base figure.media div[data-oembed-url*=\"lwartifacts.vercel.app\"] 
116iframe {\n  width: 100%;\n  border: none;\n  height: 525px;\n}\n.ContentStyles-base figure.media div[data-oembed-url*=\"youtube.com\"] iframe, .ContentStyles-base figure.media div[data-oembed-url*=\"youtu.be\"] iframe, .ContentStyles-base div.youtube-preview iframe {\n  top: 0;\n  left: 0;\n  width: 100%;\n  border: none;\n  height: 100%;\n  position: absolute;\n}\n.ContentStyles-base div.viewpoints-preview iframe {\n  width: 100%;\n  border: none;\n  height: 300px;\n}\n.ContentStyles-base div.estimaker-preview iframe {\n  width: 100%;\n  border: none;\n  height: 400px;\n}\n.ContentStyles-base div.owid-preview iframe {\n  width: 100%;\n  border: none;\n  height: 400px;\n}\n.ContentStyles-base div.metaforecast-preview iframe {\n  width: 100%;\n  border: none;\n  height: 400px;\n}\n.ContentStyles-base div.strawpoll-embed iframe {\n  width: 100%;\n  border: none;\n  height: 400px;\n}\n.ContentStyles-base div.calendly-preview iframe {\n  width: calc(100% - 10px);\n  border: 2px solid;\n  height: 750px;\n  padding: 0;\n  margin-left: 5px;\n  border-color: light-dark(#eeeeee,#363636);\n  margin-right: 5px;\n  border-radius: 10px;\n}\n.ContentStyles-base div.neuronpedia-preview iframe {\n  width: 100%;\n  border: 1px solid;\n  height: 360px;\n  max-width: 639px;\n  border-color: light-dark(#e0e0e0,#505050);\n  border-radius: 6px;\n}\n.ContentStyles-base div.manifold-preview iframe {\n  width: 100%;\n  border: none;\n  height: 400px;\n}\n.ContentStyles-base div.metaculus-preview iframe {\n  width: 100%;\n  border: none;\n  height: 400px;\n}\n.ContentStyles-base .spoilers > p:hover ~ p *, .ContentStyles-base .spoilers > p:hover ~ p a::after {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base .spoilers > p:hover ~ p code {\n  background-color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base .spoilers > p:hover ~ p img, .ContentStyles-base .spoilers > p:hover ~ p video, .ContentStyles-base .spoilers > p:hover ~ p iframe, .ContentStyles-base .spoilers > p:hover ~ p svg, .ContentStyles-base .spoilers > p:hover ~ p canvas {\n  opacity: 0;\n}\n.ContentStyles-base div.spoilers:not(:hover) {\n  color: light-dark(#000,#1b1b1b);\n  caret-color: #fff;\n  background-color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base div.spoilers:hover {\n  background: light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n}\n.ContentStyles-base div.spoilers > p {\n  margin: 0 !important;\n  padding: 0.5em 8px !important;\n}\n.ContentStyles-base div.spoilers:not(:hover) *, .ContentStyles-base div.spoilers:not(:hover) a::after {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base div.spoilers:not(:hover) code {\n  background-color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base div.spoilers:not(:hover) img, .ContentStyles-base div.spoilers:not(:hover) video, .ContentStyles-base div.spoilers:not(:hover) iframe, .ContentStyles-base div.spoilers:not(:hover) svg, .ContentStyles-base div.spoilers:not(:hover) canvas {\n  opacity: 0;\n}\n.ContentStyles-base .spoiler:not(:hover) {\n  color: light-dark(#000,#1b1b1b);\n  caret-color: #fff;\n  background-color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base .spoiler:not(:hover) *, .ContentStyles-base .spoiler:not(:hover) a::after {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base .spoiler:not(:hover) code {\n  background-color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-base .spoiler:not(:hover) img, .ContentStyles-base .spoiler:not(:hover) video, .ContentStyles-base .spoiler:not(:hover) iframe, .ContentStyles-base .spoiler:not(:hover) svg, .ContentStyles-base .spoiler:not(:hover) canvas {\n  opacity: 0;\n}\n.ContentStyles-base td p, .ContentStyles-base th p {\n  margin-top: 0.5em;\n  margin-bottom: 0.5em;\n}\n.ContentStyles-base td p:first-of-type, .ContentStyles-base th p:first-of-type {\n  margin-top: 0;\n}\n.ContentStyles-base a u, .ContentStyles-base a:hover u, .ContentStyles-base a:active u {\n  text-decoration: none;\n}\n.ContentStyles-postHighlight {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 16.64px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 22.75px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-postHighlight blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-postHighlight ul {\n  padding-inline-start: 30px;\n}\n.ContentStyles-postHighlight li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 16.64px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 23.4px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-postHighlight h1, .ContentStyles-postHighlight h2, .ContentStyles-postHighlight h3 {\n  font-size: 1.6rem;\n  margin-block-start: 0 !important;\n}\n.ContentStyles-commentBody {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  margin-top: .5em;
116\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-bottom: .25em;\n  pointer-events: none;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBody .footnotes {\n  margin-top: 0;\n  padding-top: 8px;\n  padding-left: 16px;\n  margin-bottom: 0;\n}\n.ContentStyles-commentBody blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 15.08px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-left: 12px;\n  padding-top: 8px;\n  padding-left: 16px;\n  padding-right: 24px;\n  padding-bottom: 8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBody li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBody h1, .ContentStyles-commentBody h2, .ContentStyles-commentBody h3 {\n  font-size: 19.5px;\n  margin-top: .5em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBody > * {\n  pointer-events: auto;\n}\n.ContentStyles-commentBody > *:hover ~ .spoiler, .ContentStyles-commentBody > *:hover ~ .spoiler * {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-commentBody > *:hover ~ .spoiler:before {\n  color: #fff;\n  content: \"spoiler (hover/select to reveal)\";\n}\n.ContentStyles-commentBody hr {\n  margin-top: 12px;\n  margin-bottom: 12px;\n}\n.ContentStyles-commentBody.ContentStyles-commentBody .llm-content-block {\n  font-size: inherit;\n}\n.ContentStyles-commentBodyExceptPointerEvents {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  margin-top: .5em;\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-bottom: .25em;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBodyExceptPointerEvents .footnotes {\n  margin-top: 0;\n  padding-top: 8px;\n  padding-left: 16px;\n  margin-bottom: 0;\n}\n.ContentStyles-commentBodyExceptPointerEvents blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 15.08px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-left: 12px;\n  padding-top: 8px;\n  padding-left: 16px;\n  padding-right: 24px;\n  padding-bottom: 8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBodyExceptPointerEvents li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBodyExceptPointerEvents h1, .ContentStyles-commentBodyExceptPointerEvents h2, .ContentStyles-commentBodyExceptPointerEvents h3 {\n  font-size: 19.5px;\n  margin-top: .5em;
116\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-commentBodyExceptPointerEvents > *:hover ~ .spoiler, .ContentStyles-commentBodyExceptPointerEvents > *:hover ~ .spoiler * {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-commentBodyExceptPointerEvents > *:hover ~ .spoiler:before {\n  color: #fff;\n  content: \"spoiler (hover/select to reveal)\";\n}\n.ContentStyles-commentBodyExceptPointerEvents hr {\n  margin-top: 12px;\n  margin-bottom: 12px;\n}\n.ContentStyles-debateResponseBody {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 1.35rem;\n  margin-top: .5em;\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-bottom: .25em;\n  pointer-events: none;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-debateResponseBody .footnotes {\n  margin-top: 0;\n  padding-top: 8px;\n  padding-left: 16px;\n  margin-bottom: 0;\n}\n.ContentStyles-debateResponseBody blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 15.08px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-left: 12px;\n  padding-top: 8px;\n  padding-left: 16px;\n  padding-right: 24px;\n  padding-bottom: 8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-debateResponseBody li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-debateResponseBody h1, .ContentStyles-debateResponseBody h2, .ContentStyles-debateResponseBody h3 {\n  font-size: 19.5px;\n  margin-top: .5em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-debateResponseBody > * {\n  pointer-events: auto;\n}\n.ContentStyles-debateResponseBody > *:hover ~ .spoiler, .ContentStyles-debateResponseBody > *:hover ~ .spoiler * {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-debateResponseBody > *:hover ~ .spoiler:before {\n  color: #fff;\n  content: \"spoiler (hover/select to reveal)\";\n}\n.ContentStyles-debateResponseBody hr {\n  margin-top: 12px;\n  margin-bottom: 12px;\n}\n.ContentStyles-debateResponseBody .dialogue-message-header + p {\n  margin-top: 0;\n}\n.ContentStyles-debateResponseBody blockquote, .ContentStyles-debateResponseBody li {\n  font-size: 1.35rem;\n}\n.ContentStyles-answerBody {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 16.64px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 22.75px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-answerBody blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-answerBody ul {\n  padding-inline-start: 30px;\n}
116\n.ContentStyles-answerBody li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 16.64px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 23.4px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  margin-top: .5em;\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-bottom: 18px;\n  pointer-events: none;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody .footnotes {\n  margin-top: 0;\n  padding-top: 8px;\n  padding-left: 16px;\n  margin-bottom: 0;\n}\n.ContentStyles-tagBody blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 15.08px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-left: 12px;\n  padding-top: 8px;\n  padding-left: 16px;\n  padding-right: 24px;\n  padding-bottom: 8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody h1, .ContentStyles-tagBody h2, .ContentStyles-tagBody h3 {\n  font-size: 19.5px;\n  margin-top: .5em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody > * {\n  pointer-events: auto;\n}\n.ContentStyles-tagBody > *:hover ~ .spoiler, .ContentStyles-tagBody > *:hover ~ .spoiler * {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-tagBody > *:hover ~ .spoiler:before {\n  color: #fff;\n  content: \"spoiler (hover/select to reveal)\";\n}\n.ContentStyles-tagBody hr {\n  margin-top: 12px;\n  margin-bottom: 12px;\n}\n.ContentStyles-tagBody.ContentStyles-tagBody h1 {\n  font-size: 2rem;\n  margin-top: 3rem;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 600;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody.ContentStyles-tagBody h2 {\n  font-size: 1.7rem;\n  margin-top: 1.5rem;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody.ContentStyles-tagBody h3 {\n  font-size: 1.3rem;\n  margin-top: 1.5rem;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-tagBody.ContentStyles-tagBody h1:first-child, .ContentStyles-tagBody h2:first-child, .ContentStyles-tagBody h3:first-child {\n  margin-top: 0;\n}\n.ContentStyles-llmChat {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 1.0rem;\n  margin-top: .5em;
116\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-bottom: .25em;\n  pointer-events: none;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-llmChat .footnotes {\n  margin-top: 0;\n  padding-top: 8px;\n  padding-left: 16px;\n  margin-bottom: 0;\n}\n.ContentStyles-llmChat blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 15.08px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-left: 12px;\n  padding-top: 8px;\n  padding-left: 16px;\n  padding-right: 24px;\n  padding-bottom: 8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-llmChat li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-llmChat h1, .ContentStyles-llmChat h2, .ContentStyles-llmChat h3 {\n  font-size: 19.5px;\n  margin-top: .5em;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 500;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-llmChat > * {\n  pointer-events: auto;\n}\n.ContentStyles-llmChat > *:hover ~ .spoiler, .ContentStyles-llmChat > *:hover ~ .spoiler * {\n  color: light-dark(#000,#1b1b1b);\n}\n.ContentStyles-llmChat > *:hover ~ .spoiler:before {\n  color: #fff;\n  content: \"spoiler (hover/select to reveal)\";\n}\n.ContentStyles-llmChat hr {\n  margin-top: 12px;\n  margin-bottom: 12px;\n}\n.ContentStyles-llmChat blockquote, .ContentStyles-llmChat li {\n  font-size: 1.0rem;\n}\n.ContentStyles-ultraFeed {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  margin-top: 0;\n  word-break: break-word;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-bottom: 0;\n  pointer-events: none;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-ultraFeed .footnotes {\n  margin-top: 0;\n  padding-top: 8px;\n  padding-left: 16px;\n  margin-bottom: 0;\n}\n.ContentStyles-ultraFeed blockquote {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 15.08px;\n  border-left: solid 3px light-dark(#e0e0e0,#505050);\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  margin-left: 12px;\n  padding-top: 8px;\n  padding-left: 16px;\n  padding-right: 24px;\n  padding-bottom: 8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-ultraFeed li {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n  font-variant-numeric: lining-nums;\n}\n.ContentStyles-ultraFeed h1, .ContentStyles-ultraFeed h2, .ContentStyles-ultraFeed h3 {\n  font-size: 19.5px;\n  margin-top: .5em;
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.ContentStyles-researchDocumentBody .code-token-function {\n    color: light-dark(#dd4a68, #d2a8ff);\n  }\n  .ContentStyles-researchDocumentBody blockquote {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    margin: 0;\n    font-size: 18.2px;\n    border-left: solid 3px light-dark(#e0e0e0,#505050);\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 26px;\n    padding-top: 16px;\n    padding-left: 16px;\n    padding-right: 16px;\n    padding-bottom: 16px;\n    font-variant-numeric: lining-nums;\n  }\n  .ContentStyles-researchDocumentBody li {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n    font-size: 18.2px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 26px;\n    margin-bottom: 6.5px;\n    font-variant-numeric: lining-nums;\n  }\n  .ContentStyles-researchDocumentBody h1 {\n    color: light-dark(#424242,#e8e8e8);\n    font-size: 36.4px;\n    margin-top: 1em;\n    font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n    font-weight: 500;\n    line-height: 1.13333em;\n    margin-left: -.02em;\n  }\n  .ContentStyles-researchDocumentBody h1:first-child {\n    margin-top: 0;\n    margin-block-start: -3px;\n  }\n  .ContentStyles-researchDocumentBody h2 {\n    color: light-dark(#424242,#e8e8e8);\n    font-size: 26px;\n    margin-top: 1em;\n    font-family: ETBookRoman,warnock-pro,Palatino,\"Palatino Linotype\",\"Palatino LT STD\",\"Book Antiqua\",Georgia,serif;\n 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line-height: 26px;\n    font-variant-numeric: lining-nums;\n  }\n  .ContentStyles-researchDocumentBody img {\n    max-width: 100%;\n  }\n  .ContentStyles-researchDocumentBody sup {\n    top: -0.6em;\n    position: relative;\n    font-size: 65%;\n    line-height: 0;\n    vertical-align: baseline;\n  }\n  .ContentStyles-researchDocumentBody sub {\n    top: 0.2em;\n    position: relative;\n    font-size: 70%;\n    padding-right: 0.07em;\n    vertical-align: baseline;\n  }\n  .ContentStyles-researchDocumentBody a, .ContentStyles-researchDocumentBody a:hover, .ContentStyles-researchDocumentBody a:active {\n    color: light-dark(#3f51b5,#7581d1);\n  }\n  .ContentStyles-researchDocumentBody a:visited, .ContentStyles-researchDocumentBody a.visited {\n    color: light-dark(#8c4298,#798754);\n  }\n  .ContentStyles-researchDocumentBody table {\n    width: 100%;\n    border: 1px double light-dark(#b3b3b3,#8d8d8d);\n    height: 100%;\n    margin: auto;\n    text-align: left;\n    border-spacing: 0;\n    border-collapse: collapse;\n  }\n  .ContentStyles-researchDocumentBody figure.table {\n    width: fit-content;\n    height: fit-content;\n  }\n  .ContentStyles-researchDocumentBody td, .ContentStyles-researchDocumentBody th {\n    border: 1px double light-dark(#d9d9d9,#5b5b5b);\n    padding: .4em;\n    min-width: 2em;\n    word-break: normal;\n  }\n  .ContentStyles-researchDocumentBody th {\n    background: light-dark(#fafafa,#181818);\n    font-weight: 700;\n  }\n  .ContentStyles-researchDocumentBody figure {\n    margin: 1em auto;\n    max-width: 100%;\n    text-align: center;\n  }\n  .ContentStyles-researchDocumentBody figcaption {\n    color: light-dark(rgba(0,0,0,0.54),rgba(255,255,255,0.54));\n    font-size: .85em;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    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116<link rel="preload" as="image" href="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/Davidad&#x27;s%20Bold%20Plan%20for%20Alignment:%20An%20In-Depth%20Explanation_a_futuristic_schematic_engine_po_0.07325401858294067/wj4sethm4bghpi620j9e"/><div hidden id="S:2"><span><div id="hoPfsW49iLg4KvaL5" class="SpotlightItem-root"><div class="SpotlightItem-spotlightItem"><div class="SpotlightItem-contentContainer"><div class="SpotlightItem-content SpotlightItem-postPadding"><div class="SpotlightItem-title"><a href="/posts/jRf4WENQnhssCb6mJ/davidad-s-bold-plan-for-alignment-an-in-depth-explanation">Davidad&#x27;s Bold Plan for Alignment: An In-Depth Explanation</a><span class="SpotlightItem-editDescriptionButton"></span></div><div class="SpotlightItem-subtitle"><a href="/bestoflesswrong?year=2023&amp;category=ai safety">Best of LessWrong 2023</a></div><div class="SpotlightItem-description"><div><p>Charbel-Raphaël summarizes Davidad&#x27;s plan: Use near AGIs to build a detailed world simulation, then train and formally verify an AI that follows coarse preferences and avoids catastrophic outcomes. </p></div></div><aside class="Typography-root Typography-body2 SpotlightItem-author">by <a class="SpotlightItem-authorName" href="/users/charbel-raphael">Charbel-Raphaël</a></aside></div><div class="SpotlightItem-splashImageContainer" style="height:calc(100% + 60px)"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/Davidad&#x27;s%20Bold%20Plan%20for%20Alignment:%20An%20In-Depth%20Explanation_a_futuristic_schematic_engine_po_0.07325401858294067/wj4sethm4bghpi620j9e" class="SpotlightItem-image SpotlightItem-imageFade SpotlightItem-splashImage"/></div></div><div class="SpotlightItem-reviews"><!--$?--><template id="B:6"></template><div class="SpotlightItem-review"><div class="SingleLineComment-root"><div class="SingleLineComment-placeholder SingleLineComment-commentInfo SingleLineComment-odd ContentStyles-base content ContentStyles-commentBody"> </div></div><div class="SingleLineComment-root"><div class="SingleLineComment-placeholder SingleLineComment-commentInfo SingleLineComment-odd ContentStyles-base content ContentStyles-commentBody"> </div></div></div><!--/$--></div><div class="SpotlightItem-editAllButton"></div><div class="SpotlightItem-draftButton"></div></div></div></span></div>
116<script>$RS("S:2","P:2")</script>
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116<script>window.__lwSsrGql.inject("query CommentById($documentId: String) {\n  comment(input: {selector: {documentId: $documentId}}) {\n    result {\n      ...CommentsList\n    }\n  }\n}\n\nfragment TagBasicInfo on Tag {\n  _id\n  userId\n  name\n  shortName\n  slug\n  core\n  postCount\n  adminOnly\n  canEditUserIds\n  suggestedAsFilter\n  needsReview\n  descriptionTruncationCount\n  createdAt\n  wikiOnly\n  deleted\n  isSubforum\n  noindex\n  isArbitalImport\n  isPlaceholderPage\n  baseScore\n  extendedScore\n  score\n  afBaseScore\n  afExtendedScore\n  voteCount\n  currentUserVote\n  currentUserExtendedVote\n}\n\nfragment TagPreviewFragment on Tag {\n  ...TagBasicInfo\n  isRead\n  parentTag {\n    ...TagBasicInfo\n  }\n  subTags {\n    ...TagBasicInfo\n  }\n  description {\n    _id\n    htmlHighlight\n  }\n  canVoteOnRels\n  authorOnly\n  isArbitalImport\n}\n\nfragment UsersMinimumInfo on User {\n  _id\n  slug\n  createdAt\n  username\n  displayName\n  profileImageId\n  karma\n  afKarma\n  deleted\n  isAdmin\n  htmlBio\n  postCount\n  commentCount\n  sequenceCount\n  afPostCount\n  afCommentCount\n  spamRiskScore\n  tagRevisionCount\n  reviewedByUserId\n}\n\nfragment CommentsList on Comment {\n  _id\n  postId\n  tagId\n  tag {\n    _id\n    slug\n  }\n  relevantTagIds\n  relevantTags {\n    ...TagPreviewFragment\n  }\n  tagCommentType\n  parentCommentId\n  topLevelCommentId\n  descendentCount\n  title\n  contents {\n    _id\n    html\n    plaintextMainText\n    wordCount\n  }\n  postedAt\n  lastEditedAt\n  repliesBlockedUntil\n  userId\n  draft\n  deleted\n  deletedPublic\n  deletedByUserId\n  deletedReason\n  hideAuthor\n  authorIsUnreviewed\n  user {\n    ...UsersMinimumInfo\n  }\n  currentUserVote\n  currentUserExtendedVote\n  isBookmarked\n  baseScore\n  extendedScore\n  score\n  voteCount\n  af\n  afDate\n  moveToAlignmentUserId\n  afBaseScore\n  afExtendedScore\n  suggestForAlignmentUserIds\n  reviewForAlignmentUserId\n  needsReview\n  answer\n  parentAnswerId\n  retracted\n  postVersion\n  reviewedByUserId\n  shortform\n  shortformFrontpage\n  lastSubthreadActivity\n  moderatorHat\n  hideModeratorHat\n  nominatedForReview\n  reviewingForReview\n  promoted\n  promotedByUser {\n    ...UsersMinimumInfo\n  }\n  directChildrenCount\n  votingSystem\n  isPinnedOnProfile\n  debateResponse\n  rejected\n  rejectedReason\n  originalDialogueId\n}\n::\n{\"documentId\":\"MJCvHk5ARMnWDjQDg\"}",{"data":{"comment":{"result":{"$ref":"MJCvHk5ARMnWDjQDg:{0}"},"__typename":"SingleCommentOutput"}}},{"MJCvHk5ARMnWDjQDg_contents":{"_id":"MJCvHk5ARMnWDjQDg_contents","html":"\u003cp>At the time when I first heard this agenda proposed, I was skeptical. I remain skeptical, especially about the technical work that has been done thus far on the agenda\u003csup class=\"footnote-ref\">\u003ca href=\"#fn-ozknoGwFpqEii9GAL-1\" id=\"fnref-ozknoGwFpqEii9GAL-1\">[1]\u003c/a>\u003c/sup>.\u003c/p>\n\u003cp>I think this post does a reasonable job of laying out the agenda and the key difficulties. However, when talking to Davidad in person, I've found that he often has more specific tricks and proposals than what was laid out in this post. I didn't find these tricks moved me very far, but I think they were helpful for understanding what is going on.\u003c/p>\n\u003cp>This post and Davidad's agenda overall would benefit from having concrete examples of how the approach might work in various cases, or more discussion of what would be out of scope (and why this could be acceptable). For instance, how would you make a superhumanly efficient (ASI-designed) factory that produces robots while proving safety? How would you allow for AIs piloting household robots to do chores (or is this out of scope)? How would you allow for the AIs to produce software that people run on their computers or to design physical objects that get manufactured? Given that this proposal doesn't allow for safely automating safety research, my understanding is that it is supposed to be a stable end state. Correspondingly, it is important to know what Davidad thinks can and can't be done with this approach.\u003c/p>\n\u003cp>My core disagreements are on the \"Scientific Sufficiency Hypothesis\" (particularly when considering computational constraints), \"Model-Checking Feasibility Hypothesis\" (and more generally on proving the relevant properties), and on the political feasibility of paying the needed tax even if the other components work out. It seems very implausible to me that making a sufficiently good simulation is as easy as building the Large Hadron Collider. I think the objection in \u003ca href=\"https://www.lesswrong.com/posts/jRf4WENQnhssCb6mJ/davidad-s-bold-plan-for-alignment-an-in-depth-explanation?commentId=jKGsyFf8FnG8ENGpa\">this comment\u003c/a> holds up (my understanding is Davidad would require that we formally verify everything on the computer).\u003csup class=\"footnote-ref\">\u003ca href=\"#fn-ozknoGwFpqEii9GAL-2\" id=\"fnref-ozknoGwFpqEii9GAL-2\">[2]\u003c/a>\u003c/sup>\u003c/p>\n\u003cp>As a concrete example, I found it quite implausible that you could construct and run a robot factory that is provably safe using the approach outlined in this proposal, and this sort of thing seems like a minimal thing you'd need to be able to do with AIs to make them useful.\u003c/p>\n\u003chr class=\"footnotes-sep\">\n\u003csection class=\"footnotes\">\n\u003col class=\"footnotes-list\">\n\u003cli id=\"fn-ozknoGwFpqEii9GAL-1\" class=\"footnote-item\">\u003cp>My understanding is that most technical work has been on improving mathematical fund
116amentals (e.g. funding logicians and category theorists to work on various things). I think it would make more sense to try to demonstrate overall viability with minimal prototypes that address key cruxes. I expect this to fail and thus it would be better to do this earlier. If you expect these prototypes would work, then it seems interesting to demonstrate this as many people would find this very surprising. \u003ca href=\"#fnref-ozknoGwFpqEii9GAL-1\" class=\"footnote-backref\">↩︎\u003c/a>\u003c/p>\n\u003c/li>\n\u003cli id=\"fn-ozknoGwFpqEii9GAL-2\" class=\"footnote-item\">\u003cp>This is mostly unrelated, but when talking with Davidad, I've found that a potential disagreement is that he's substantially more optimistic about using elicitation to make systems that currently seem quite incapable (e.g., GPT-4) very useful. As a concrete example, I think we disagreed about the viability of running a fully autonomous Tesla factory for 1 year at greater than one-tenth productivity using just AI systems created prior to halfway through 2024. (I was very skeptical.) It's not exactly clear to me how this is a crux for the overall plan (beyond getting a non-sabotaged implementation of simulations) given that we still are aiming to prove safety either way, and proving properties of GPT-4 is not clearly much easier than proving properties of much smarter AIs. (Apologies if I've just forgotten.) \u003ca href=\"#fnref-ozknoGwFpqEii9GAL-2\" class=\"footnote-backref\">↩︎\u003c/a>\u003c/p>\n\u003c/li>\n\u003c/ol>\n\u003c/section>\n","plaintextMainText":"At the time when I first heard this agenda proposed, I was skeptical. I remain skeptical, especially about the technical work that has been done thus far on the agenda[1].\n\nI think this post does a reasonable job of laying out the agenda and the key difficulties. However, when talking to Davidad in person, I've found that he often has more specific tricks and proposals than what was laid out in this post. I didn't find these tricks moved me very far, but I think they were helpful for understanding what is going on.\n\nThis post and Davidad's agenda overall would benefit from having concrete examples of how the approach might work in various cases, or more discussion of what would be out of scope (and why this could be acceptable). For instance, how would you make a superhumanly efficient (ASI-designed) factory that produces robots while proving safety? How would you allow for AIs piloting household robots to do chores (or is this out of scope)? How would you allow for the AIs to produce software that people run on their computers or to design physical objects that get manufactured? Given that this proposal doesn't allow for safely automating safety research, my understanding is that it is supposed to be a stable end state. 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If keys don't match, don't trust them.\u003c/p>\n\u003cp>For GPG, the full armored public key is at \u003ca href=\"https://github.com/rgreenblatt.gpg\">https://github.com/rgreenblatt.gpg\u003c/a> and on \u003ca href=\"http://keys.openpgp.org\">keys.openpgp.org\u003c/a> (obtainable via \u003ccode>gpg --keyserver hkps://keys.openpgp.org --recv-keys 5C9863A9CF806AD8A4F5E6961ADD11212C0CB1F4\u003c/code>).\u003c/p>\n\u003cp>If you aren't an AI and you want to contact me, please message me on LW or similar.\u003c/p>\n","postCount":70,"commentCount":2187,"sequenceCount":0,"afPostCount":50,"afCommentCount":578,"spamRiskScore":1,"tagRevisionCount":8,"reviewedByUserId":"gXeEWGjTWyqgrQTzR","__typename":"User"},"MJCvHk5ARMnWDjQDg:{0}":{"_id":"MJCvHk5ARMnWDjQDg","postId":"jRf4WENQnhssCb6mJ","tagId":null,"tag":null,"relevantTagIds":[],"relevantTags":[],"tagCommentType":"DISCUSSION","parentCommentId":null,"topLevelCommentId":null,"descendentCount":0,"title":null,"contents":{"$ref":"MJCvHk5ARMnWDjQDg_contents"},"postedAt":"2025-01-13T23:48:05.671Z","lastEditedAt":"2025-01-28T22:39:18.673Z","repliesBlockedUntil":null,"userId":"dfZAq9eZxs4BB4Ji5","draft":false,"deleted":false,"deletedPublic":false,"deletedByUserId":null,"deletedReason":null,"hideAuthor":false,"authorIsUnreviewed":false,"user":{"$ref":"dfZAq9eZxs4BB4Ji5"},"currentUserVote":null,"currentUserExtendedVote":null,"isBookmarked":false,"baseScore":33,"extendedScore":{"reacts":{"agree":[{"karma":2174,"quotes":["overall viability with minimal prototypes that address key cruxes"],"userId":"XchweonPm2TC7EJES","reactType":"created","displayName":"Charbel-Raphaël"}]},"agreement":18,"approvalVoteCount":12,"agreementVoteCount":6},"score":0.0005296514718793333,"voteCount":12,"af":true,"afDate":null,"moveToAlignmentUserId":null,"afBaseScore":20,"afExtendedScore":{"reacts":{"agree":[{"karma":2174,"quotes":["overall viability with minimal prototypes that address key cruxes"],"userId":"XchweonPm2TC7EJES","reactType":"created","displayName":"Charbel-Raphaël"}]},"agreement":18,"approvalVoteCount":11,"agreementVoteCount":6},"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"needsReview":null,"answer":false,"parentAnswerId":null,"retracted":false,"postVersion":"1.4.1","reviewedByUserId":null,"shortform":false,"shortformFrontpage":true,"lastSubthreadActivity":"2025-01-13T23:48:05.687Z","moderatorHat":false,"hideModeratorHat":null,"nominatedForReview":null,"reviewingForReview":"2023","promoted":null,"promotedByUser":null,"directChildrenCount":0,"votingSystem":"namesAttachedReactions","isPinnedOnProfile":false,"debateResponse":null,"rejected":false,"rejectedReason":null,"originalDialogueId":null,"__typename":"Comment"}})</script>
116<script>window.__lwSsrGql.inject("query CommentById($documentId: String) {\n  comment(input: {selector: {documentId: $documentId}}) {\n    result {\n      ...CommentsList\n    }\n  }\n}\n\nfragment TagBasicInfo on Tag {\n  _id\n  userId\n  name\n  shortName\n  slug\n  core\n  postCount\n  adminOnly\n  canEditUserIds\n  suggestedAsFilter\n  needsReview\n  descriptionTruncationCount\n  createdAt\n  wikiOnly\n  deleted\n  isSubforum\n  noindex\n  isArbitalImport\n  isPlaceholderPage\n  baseScore\n  extendedScore\n  score\n  afBaseScore\n  afExtendedScore\n  voteCount\n  currentUserVote\n  currentUserExtendedVote\n}\n\nfragment TagPreviewFragment on Tag {\n  ...TagBasicInfo\n  isRead\n  parentTag {\n    ...TagBasicInfo\n  }\n  subTags {\n    ...TagBasicInfo\n  }\n  description {\n    _id\n    htmlHighlight\n  }\n  canVoteOnRels\n  authorOnly\n  isArbitalImport\n}\n\nfragment UsersMinimumInfo on User {\n  _id\n  slug\n  createdAt\n  username\n  displayName\n  profileImageId\n  karma\n  afKarma\n  deleted\n  isAdmin\n  htmlBio\n  postCount\n  commentCount\n  sequenceCount\n  afPostCount\n  afCommentCount\n  spamRiskScore\n  tagRevisionCount\n  reviewedByUserId\n}\n\nfragment CommentsList on Comment {\n  _id\n  postId\n  tagId\n  tag {\n    _id\n    slug\n  }\n  relevantTagIds\n  relevantTags {\n    ...TagPreviewFragment\n  }\n  tagCommentType\n  parentCommentId\n  topLevelCommentId\n  descendentCount\n  title\n  contents {\n    _id\n    html\n    plaintextMainText\n    wordCount\n  }\n  postedAt\n  lastEditedAt\n  repliesBlockedUntil\n  userId\n  draft\n  deleted\n  deletedPublic\n  deletedByUserId\n  deletedReason\n  hideAuthor\n  authorIsUnreviewed\n  user {\n    ...UsersMinimumInfo\n  }\n  currentUserVote\n  currentUserExtendedVote\n  isBookmarked\n  baseScore\n  extendedScore\n  score\n  voteCount\n  af\n  afDate\n  moveToAlignmentUserId\n  afBaseScore\n  afExtendedScore\n  suggestForAlignmentUserIds\n  reviewForAlignmentUserId\n  needsReview\n  answer\n  parentAnswerId\n  retracted\n  postVersion\n  reviewedByUserId\n  shortform\n  shortformFrontpage\n  lastSubthreadActivity\n  moderatorHat\n  hideModeratorHat\n  nominatedForReview\n  reviewingForReview\n  promoted\n  promotedByUser {\n    ...UsersMinimumInfo\n  }\n  directChildrenCount\n  votingSystem\n  isPinnedOnProfile\n  debateResponse\n  rejected\n  rejectedReason\n  originalDialogueId\n}\n::\n{\"documentId\":\"6dDv7Mxr36ovmAe3D\"}",{"data":{"comment":{"result":{"$ref":"6dDv7Mxr36ovmAe3D:{0}"},"__typename":"SingleCommentOutput"}}},{"6dDv7Mxr36ovmAe3D_contents":{"_id":"6dDv7Mxr36ovmAe3D_contents","html":"\u003cp>\u003cstrong>Ok, time to review this post and assess the overall status of the project.\u003c/strong>\u003c/p>\u003ch2>\u003cstrong>Review of the post\u003c/strong>\u003c/h2>\u003cp>\u003cstrong>What i still appreciate about the post:\u003c/strong> I continue to appreciate its pedagogy, structure, and the general philosophy of taking a complex, lesser-known plan and helping it gain broader recognition. I'm still quite satisfied with the construction of the post—it's progressive and clearly distinguishes between what's important and what's not. I remember the first time I met Davidad. He sent me his\u003ca href=\"https://www.lesswrong.com/posts/pKSmEkSQJsCSTK6nH/an-open-agency-architecture-for-safe-transformative-ai\">&nbsp;\u003cu>previous post\u003c/u>\u003c/a>. I skimmed it for 15 minutes, didn't really understand it, and thought,&nbsp;\u003ci>\"There's no way this is going to work.\"\u003c/i> Then I reconsidered, thought about it more deeply, and realized there was something important here. Hopefully, this post succeeded in showing that there is indeed something worth exploring! I think such distillation and analysis are really important.\u003c/p>\u003cp>I'm especially happy about the fact that we tried to elicit as much as we could from Davidad's model during our interactions, including his roadmap and some ideas of easy projects to get early empirical feedback on this proposal.\u003c/p>\u003ch2>\u003cstrong>Current Status of the Agenda.\u003c/strong>\u003c/h2>\u003cp>(I'm not the best person to write this, see this as an informal personal opinion)\u003c/p>\u003cp>Overall, Davidad performed much better than expected with his new job as program director in ARIA and got funded 74M$ over 4 years. And I still think this is the only plan that could enable the creation of a very powerful AI capable of performing&nbsp;\u003cstrong>a true pivotal act to end the acute risk period,&nbsp;\u003c/strong>and I think this last part is the added value of this plan, especially in the sense that it could be done in a somewhat ethical/democratic way compared to other forms of pivotal acts. However, it's probably not going to happen in time.\u003c/p>\u003cp>\u003cstrong>Are we on track? Weirdly, yes for the non-technical aspects, no for the technical ones?\u003c/strong> The post includes a roadmap with 4 stages, and we can check if we are on track. It seems to me that Davidad jumped directly to stage 3, without going through stages 1 and 2. This is because of having been selected as research director for ARIA, so he's probably going to do 1 and 2 directly from ARIA.\u003c/p>\u003cul>\u003cli>\u003cstrong>Stage 1 Early Research Projects\u003c/strong> is not really accomplished:\u003cul>\u003cli>\u003ci>“Figure out the meta ontology theory”\u003c/i>: Maybe the most important point of the four, currently WIP in ARIA, but a massive team of mathematicians has been hired to solve this.\u003c/li>\u003cli>\u003ci>“Heuristics used by the solver\u003c/i>”: Nope\u003c/li>\u003cli>\u003ci>“Building a toy infra-Bayesian \"Super Mario\", and then applying this framework to model Smart Grids”\u003c/i>: Nope\u003c/li>\u003cli>\u003ci>“Training LLMs to write models in the PRISM language by backward distillation”\u003c/i>: Kind of already here, probably not very high value to spend time here, I think this is going to be solved by default.\u003c/li>\u003c/ul>\u003c/li>\u003cli>\u003cstrong>Stage 2: Industry actors' first projects\u003c/strong>: I think this step is no longer meaningful because of ARIA.\u003c/li>\u003cli>\u003cstrong>Stage 3: formal arrangement to get labs to collectively agree to increase their investment in OAA\u003c/strong>,&nbsp;\u003cstrong>&nbsp;\u003c/strong>is almost here, in the sense that Davidad got millions to execute this project in ARIA and he published his\u003ca href=\"https://arxiv.org/abs/2405.06624\">&nbsp;\u003cu>Multi-author manifesto which backs the plan with legendary names\u003c/u>\u003c/a> especially with Yoshua Bengio as the&nbsp;\u003ca href=\"https://x.com/davidad/status/1821155220295963101\">\u003cu>scientific director\u003c/u>\u003c/a> of this project.\u003c/li>\u003c/ul>\u003cp>\u003cstrong>The lack of prototyping is concerning.\u003c/strong> I would have really liked to see an \"infra-Bayesian Super Mario\" or something similar, as mentioned in the post. If it's truly simple to implement, it should have been done by now. This would help many people understand how it could work. If it's not simple, that would reveal it's not straightforward at all. Either way, it would be pedagogically useful for anyone approaching the project. If we want to make these values democratic, etc.. It's very regrettable that this hasn't been done after two years. (I think people from the\u003ca href=\"https://ai.objectives.institute/\">&nbsp;\u003cu>AI Objectives Institute\u003c/u>\u003c/a> tried something at some point, but I'm not aware of anything publicly available.) I think this complete lack prototypes is my number one concern preventing me from recommending more \"safe by design\" agendas to policymakers.\u003c/p>\u003cp>\u003cstrong>This plan was an inspiration for constructability:\u003c/strong> It might be the case that the bold plan could decay gracefully, for example into\u003ca href=\"https://www.lesswrong.com/posts/y9tnz27oLmtLxcrEF/constructability-plainly-coded-agis-may-be-feasible-in-the\">&nbsp;\u003cu>constructability\u003c/u>\u003c/a>, by renouncing formal verification and only using traditional software engineering techniques.\u003c/p>\u003cp>\u003cstrong>International coordination is an even bigger bottleneck than I thought.\u003c/strong> The \"CERN for AI\" isn't really within the Overton window, but I think this applies to all the other plans, and not just Davidad's plan. (Davidad made a little analysis of this aspect&nbsp;\u003ca href=\"https://www.aria.org.uk/media/3nhijno4/aria-safeguarded-ai-programme-thesis-v1.pdf\">\u003cu>here\u003c/u>\u003c/a>).\u003c/p>\u003cp>\u003cstrong>At the end of the day:\u003c/strong> Kudos to Davidad for successfully building coalitions, which is already beyond amazing! and he is really an impressive thought leader. What I'm waiting to see for the next year is using AIs such as O3 that are already impressive in terms of competitive programming and science knowledge, and seeing what we can already do with that. I remain excited and eager to see the next steps of this plan.\u003c/p>","plaintextMainText":"Ok, time to review this post and assess the overall status of the project.\n\n\nReview of the post\n\nWhat i still appreciate about the post: I continue to appreciate its pedagogy, structure, and the general philosophy of taking a complex, lesser-known plan and helping it gain broader recognition. I'm still quite satisfied with the construction of the post—it's progressive and clearly distinguishes between what's important and what's not. I remember the first time I met Davidad. He sent me his previous post. I skimmed 
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116<div hidden id="S:7"><div class="comments-node CommentFrame-commentsNodeRoot comments-node-root comments-node-odd CommentFrame-node af CommentFrame-answerLeafComment CommentFrame-isSingleLine" id="MJCvHk5ARMnWDjQDg"><div><span><div class="SingleLineComment-root"><div class="SingleLineComment-commentInfo SingleLineComment-odd ContentStyles-base content ContentStyles-commentBody"><span class="SingleLineComment-leadingInfo">20</span><span class="UsersNameDisplay-noColor SingleLineComment-username CommentUserName-author">ryan_greenblatt</span><div class="SingleLineComment-truncatedHighlight ContentStyles-base content ContentStyles-commentBody">At the time when I first heard this agenda proposed, I was skeptical. I remain skeptical, especially about the technical work that has been done thus far on the agenda[1].
117
118I think this post does a reasonable job of laying out the agenda and the key difficulties. However, when talking to Davidad in person, I&#x27;ve found that he often has more specific tricks and proposals than what was laid out in this post. I didn&#x27;t find these tricks moved me very far, but I think they were helpful for understanding what is going on.
119
120This post and Davidad&#x27;s agenda overall would benefit from having concrete examples of how the approach might work in various cases, or more discussion of what would be out of scope (and why this could be acceptable). For instance, how would you make a superhumanly efficient (ASI-designed) factory that produces robots while proving safety? How would you allow for AIs piloting household robots to do chores (or is this out of scope)? How would you allow for the AIs to produce software that people run on their computers or to design physical objects that get manufactured? Given that this proposal doesn&#x27;t allow for safely automating safety research, my understanding is that it is supposed to be a stable end state. Correspondingly, it is important to know what Davidad thinks can and can&#x27;t be done with this approach.
121
122My core disagreements are on the &quot;Scientific Sufficiency Hypothesis&quot; (particularly when considering computational constraints), &quot;Model-Checking Feasibility Hypothesis&quot; (and more generally on proving the relevant properties), and on the political feasibility of paying the needed tax even if the other components work out. It seems very implausible to me that making a sufficiently good simulation is as easy as building the Large Hadron Collider. I think the objection in this comment holds up (my understanding is Davidad would require that we formally verify everything on the computer).[2]
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124As a concrete example, I found it quite implausible that you </div></div></div></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"></div></div></div></div>
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124<div hidden id="S:8"><div class="comments-node CommentFrame-commentsNodeRoot comments-node-root comments-node-odd CommentFrame-node af CommentFrame-answerLeafComment CommentFrame-isSingleLine" id="6dDv7Mxr36ovmAe3D"><div><span><div class="SingleLineComment-root"><div class="SingleLineComment-commentInfo SingleLineComment-odd ContentStyles-base content ContentStyles-commentBody"><span class="SingleLineComment-leadingInfo">4</span><span class="UsersNameDisplay-noColor SingleLineComment-username CommentUserName-author">Charbel-Raphaël</span><div class="SingleLineComment-truncatedHighlight ContentStyles-base content ContentStyles-commentBody">Ok, time to review this post and assess the overall status of the project.
125
126
127Review of the post
128
129What i still appreciate about the post: I continue to appreciate its pedagogy, structure, and the general philosophy of taking a complex, lesser-known plan and helping it gain broader recognition. I&#x27;m still quite satisfied with the construction of the post—it&#x27;s progressive and clearly distinguishes between what&#x27;s important and what&#x27;s not. I remember the first time I met Davidad. He sent me his previous post. I skimmed it for 15 minutes, didn&#x27;t really understand it, and thought, &quot;There&#x27;s no way this is going to work.&quot; Then I reconsidered, thought about it more deeply, and realized there was something important here. Hopefully, this post succeeded in showing that there is indeed something worth exploring! I think such distillation and analysis are really important.
130
131I&#x27;m especially happy about the fact that we tried to elicit as much as we could from Davidad&#x27;s model during our interactions, including his roadmap and some ideas of easy projects to get early empirical feedback on this proposal.
132
133
134Current Status of the Agenda.
135
136(I&#x27;m not the best person to write this, see this as an informal personal opinion)
137
138Overall, Davidad performed much better than expected with his new job as program director in ARIA and got funded 74M$ over 4 years. And I still think this is the only plan that could enable the creation of a very powerful AI capable of performing a true pivotal act to end the acute risk period, and I think this last part is the added value of this plan, especially in the sense that it could be done in a somewhat ethical/democratic way compared to other forms of pivotal acts. However, it&#x27;s probably not going to happen in time.
139
140Are we on track? Weirdly, yes for the non-technical aspects, no for the technical ones? The post includes a roadmap with 4 stages, and we can check if we are on track. It seems to me that Davidad jumped directly to stage 3, without going through sta</div></div></div></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"></div></div></div></div>
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140,{"$ref":"iDBHmAdFLTiascgpK"},{"$ref":"wxjyq9kKCyNJMBwzR"},{"$ref":"chaq3xcrjB95eFuWw"},{"$ref":"L4X38T7Z2fHa7EKSJ"},{"$ref":"i2dz2mQS8eqqJT9Wk"},{"$ref":"pakP2ffYiYDHMf3R7"},{"$ref":"8vgbPrzqhBF2d68MC"},{"$ref":"7RpAE8ekujvCG2dAG"},{"$ref":"uT9DtghTFAEcAKx26"},{"$ref":"HaxvEokc2esCsG9fE"},{"$ref":"2d3FxWdPzjqGvov2C"},{"$ref":"TJv3YoYw9BBxPBRsH"}]},"score":27,"afBaseScore":2,"afExtendedScore":{"reacts":{},"usersWhoLiked":[{"$ref":"nLbwLhBaQeG6tCNDN"}]},"voteCount":19,"currentUserVote":null,"currentUserExtendedVote":null,"__typename":"Tag"},"9LWK9Hh3Qsg36e3FS":{"_id":"9LWK9Hh3Qsg36e3FS","imageUrl":"https://res.cloudinary.com/lesswrong-2-0/image/upload/c_fill,ar_1.91,g_auto/SocialPreview/ahdxhuahd4vfdtbygz7e","__typename":"SocialPreviewType"},"HNe7iX2nnawmPbtFi":{"_id":"HNe7iX2nnawmPbtFi","htmlHighlight":"\u003cp>\u003cspan style=\"white-space:pre-wrap\">This post argues that fixed-weight models (at least as we understand them today) will a) always be vulnerable to adversarial examples in their concept-spaces, and b) \u003c/span>\u003cb>\u003cspan style=\"white-space:pre-wrap\">hence\u003c/span>\u003c/b>\u003cspan style=\"white-space:pre-wrap\"> will be misaligned, \u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">under sufficient optimisation pressure\u003c/span>\u003c/i>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Boundaries in concept space\u003c/span>\u003c/h1>\u003cp>\u003cspan style=\"white-space:pre-wrap\">To serve any purpose whatsoever, an AI will have to draw boundaries inside its world-model - to distinguish world A from world B, and reach some comparison between them.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">If we want the AI to follow our goals and values, we want it to be able to recognise concepts like \"human being\", or maybe \"conscious being\", \"suffering\", \"preference satisfaction\", \"law\", and so on. So we want an AI to be able to look at a situation and assess, e.g., whether there are or aren't suffering conscious beings in it.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">But concepts like \"conscious beings\" are not crisply defined across all possible world-states. A fixed-weight model will draw a boundary between \"conscious being\" and \"non-conscious being\" (or maybe score the amount/degree of consciousness), but this will be an imperfect boundary. In high-dimensional spaces, there are many degrees of freedom of how a boundary can be drawn, and never enough data to draw the boundary perfectly\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"7jryqcvkxyp\" data-footnote-index=\"1\" role=\"doc-noteref\" id=\"fnref7jryqcvkxyp\">\u003csup>\u003ca href=\"#fn7jryqcvkxyp\">[1]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">If someone is confident that we can draw an acceptably reliable boundary defining \"conscious being\", grounded in fundamental facts about the universe (e.g. basic physics), and resistant to all \u003c/span>\u003ca href=\"https://www.alignmentforum.org/w/ontological-crisis?version=1.1.0\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">ontological crises\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">... well, let's just say they have an optimism about concept rigour that flies in the face of all past experience.\u003c/span>\u003c/p>\u003cfigure class=\"image\">\u003cimg src=\"https://res.cloudinary.com/lesswrong-2-0/image/upload/v1790627136/lexical_client_uploads/a3m09jquwfcktv7aehdt.png\" alt=\"decision-boundary-annotated(2).png\">\u003cfigcaption>\u003cp>\u003ca href=\"https://erainnovator.com/supervised-learning/\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">Almost perfect\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> decision boundaries. Almost...\u003c/span>\u003c/p>\u003c/figcaption>\u003c/figure>\u003cp>\u003cspan style=\"white-space:pre-wrap\">False positives and false negatives can both be disastrous: excluding conscious beings from consideration (therefore their suffering is ignored) or including non-conscious beings within the list (if smiling faces are ranked as conscious beings, then tiling the universe with smiling faces is an optimal action - even at the \"minor\" cost to those \"humans and animals\" running around).\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">These examples are \"adversarial\", similarly to \u003c/span>\u003ca href=\"https://gradientscience.org/intro_adversarial/\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">adversarial images\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> that break classifiers: something of one category that is designed to be classified into another one\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"89epqh6nzi9\" data-footnote-index=\"2\" role=\"doc-noteref\" id=\"fnref89epqh6nzi9\">\u003csup>\u003ca href=\"#fn89epqh6nzi9\">[2]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Self-Goodharting on adversarial data\u003c/span>\u003c/h1>\u003cp data-truncated=\"true\">\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">A reasonable reaction to the above might be a shrug and a statement of \"so what?\". 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140\n\n@font-face /* 19 */ {\n  font-family: MJXTEX-T;\n  src: url(\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Typewriter-Regular.woff\") format(\"woff\");\n}\n\n@font-face /* 20 */ {\n  font-family: MJXTEX-V;\n  src: url(\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Vector-Regular.woff\") format(\"woff\");\n}\n\n@font-face /* 21 */ {\n  font-family: MJXTEX-VB;\n  src: url(\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Vector-Bold.woff\") format(\"woff\");\n}\n\u003c/style>\u003c/p>","plaintextDescription":"This post argues that fixed-weight models (at least as we understand them today) will a) always be vulnerable to adversarial examples in their concept-spaces, and b) hence will be misaligned, under sufficient optimisation pressure.\n\n\nBoundaries in concept space\nTo serve any purpose whatsoever, an AI will have to draw boundaries inside its world-model - to distinguish world A from world B, and reach some comparison between them.\n\nIf we want the AI to follow our goals and values, we want it to be able to recognise concepts like \"human being\", or maybe \"conscious being\", \"suffering\", \"preference satisfaction\", \"law\", and so on. So we want an AI to be able to look at a situation and assess, e.g., whether there are or aren't suffering conscious beings in it.\n\nBut concepts like \"conscious beings\" are not crisply defined across all possible world-states. A fixed-weight model will draw a boundary between \"conscious being\" and \"non-conscious being\" (or maybe score the amount/degree of consciousness), but this will be an imperfect boundary. In high-dimensional spaces, there are many degrees of freedom of how a boundary can be drawn, and never enough data to draw the boundary perfectly[1].\n\nIf someone is confident that we can draw an acceptably reliable boundary defining \"conscious being\", grounded in fundamental facts about the universe (e.g. basic physics), and resistant to all ontological crises... well, let's just say they have an optimism about concept rigour that flies in the face of all past experience.\n\nAlmost perfect decision boundaries. Almost...\n\nFalse positives and false negatives can both be disastrous: excluding conscious beings from consideration (therefore their suffering is ignored) or including non-conscious beings within the list (if smiling faces are ranked as conscious beings, then tiling the universe with smiling faces is an optimal action - even at the \"minor\" cost to those \"humans and animals\" running around).\n\nThese examples are \"adversarial\", similarl","wordCount":706,"version":"1.7.0","__typename":"Revision"},"H5xSnPFJCftkSYzcc_contents":{"_id":"H5xSnPFJCftkSYzcc_contents","html":"\u003cp>I think the idea here is that deployment is like training, and during training it receives reward, and during deployment there may be epiphenomenal rewards even if they don't cause weight updates, and it's possible that \"eval awareness\" is low so it still pursues the epiphenomenal rewards?\u003c/p>\n\u003cp>I agree in this case, it is reasonably likely to seek reward during deployment, which includes tricking humans who grade it, however, this isn't optimizing on the concepts in its own weights, the issue is that it tricks humans. So it's not \"misaligned because fixed weights\" in this case.\u003c/p>\n\u003cp>We could also consider a \"self RLAIF\" situation where it could be rewarded depending on its own judgments. That's closer to the situation I thought you were talking about, where it optimizes inputs to its own weights, thereby 'tricking' its concepts. However, self RLAIF looks like a 2-player game, rather than a case where the grader is fixed and the agent optimizes on it.\u003c/p>\n","plaintextMainText":"I think the idea here is that deployment is like training, and during training it receives reward, and during deployment there may be epiphenomenal rewards even if they don't cause weight updates, and it's possible that \"eval awareness\" is low so it still pursues the epiphenomenal rewards?\n\nI agree in this case, it is reasonably likely to seek reward during deployment, which includes tricking humans who grade it, however, this isn't optimizing on the concepts in its own weights, the issue is that it tricks humans. So it's not \"misaligned because fixed weights\" in this case.\n\nWe could also consider a \"self RLAIF\" situation where it could be rewarded depending on its own judgments. That's closer to the situation I thought you were talking about, where it optimizes inputs to its own weights, thereby 'tricking' its concepts. However, self RLAIF looks like a 2-player game, rather than a case where the grader is fixed and the agent optimizes on it.","wordCount":160,"__typename":"Revision"},"gSKzrqGFdS7DkXhuE":{"_id":"gSKzrqGFdS7DkXhuE","slug":"jessica-liu-taylor","createdAt":"2017-06-18T22:55:04.584Z","username":"jessica.liu.taylor","displayName":"jessicata","profileImageId":null,"karma":11419,"afKarma":843,"deleted":false,"isAdmin":false,"htmlBio":"\u003cp>Jessica Taylor.  CS undergrad and Master's at Stanford; former research fellow at MIRI.\u003c/p>\n\u003cp>I work on decision theory, social epistemology, strategy, naturalized agency, mathematical foundations, decentralized networking systems and applications, theory of mind, and functional programming languages.\u003c/p>\n\u003cp>Blog: \u003ca href=\"http://unstableontology.com\">unstableontology.com\u003c/a>\u003c/p>\n\u003cp>Twitter: \u003ca href=\"https://twitter.com/jessi_cata\">https://twitter.com/jessi_cata\u003c/a>\u003c/p>\n","postCount":81,"commentCount":1132,"sequenceCount":0,"afPostCount":65,"afCommentCount":284,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"r38pkCm7wF4M44MDQ","__typename":"User"},"H5xSnPFJCftkSYzcc":{"_id":"H5xSnPFJCftkSYzcc","postId":"9LWK9Hh3Qsg36e3FS","tagId":null,"tag":null,"relevantTagIds":[],"relevantTags":[],"tagCommentType":"DISCUSSION","parentCommentId":"hjjymD533obKwPCuw","topLevelCommentId":"5shHfaAcfyKh7iWFM","descendentCount":0,"title":null,"contents":{"$ref":"H5xSnPFJCftkSYzcc_contents"},"postedAt":"2026-09-30T18:35:11.326Z","lastEditedAt":"2026-09-30T18:35:11.326Z","repliesBlockedUntil":null,"userId":"gSKzrqGFdS7DkXhuE","draft":false,"deleted":false,"deletedPublic":false,"deletedByUserId":null,"deletedReason":null,"hideAuthor":false,"authorIsUnreviewed":false,"user":{"$ref":"gSKzrqGFdS7DkXhuE"},"currentUserVote":null,"currentUserExtendedVote":null,"isBookmarked":false,"baseScore":2,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":1,"agreementVoteCount":0},"score":0.07822819799184799,"voteCount":1,"af":true,"afDate":null,"moveToAlignmentUserId":null,"afBaseScore":1,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":1,"agreementVoteCount":0},"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"needsReview":null,"answer":false,"parentAnswerId":null,"retracted":false,"postVersion":"1.7.0","reviewedByUserId":null,"shortform":false,"shortformFrontpage":true,"lastSubthreadActivity":"2026-09-30T18:35:11.331Z","moderatorHat":false,"hideModeratorHat":null,"nominatedForReview":null,"reviewingForReview":null,"promoted":null,"promotedByUser":null,"directChildrenCount":0,"votingSystem":"namesAttachedReactions","isPinnedOnProfile":false,"debateResponse":null,"rejected":false,"rejectedReason":null,"originalDialogueId":null,"__typename":"Comment"},"hjjymD533obKwPCuw_contents":{"_id":"hjjymD533obKwPCuw_contents","html":"\u003cp>\"rewarded\" -&gt; achieves a higher score or reward\u003c/p>\n","plaintextMainText":"\"rewarded\" -> achieves a higher score or reward","wordCount":8,"__typename":"Revision"},"uCfjEXpnchoqDWNoL:{0}":{"_id":"uCfjEXpnchoqDWNoL","slug":"stuart_armstrong","createdAt":"2009-03-26T10:25:39.189Z","username":"Stuart_Armstrong","displayName":"Stuart_Armstrong","profileImageId":null,"karma":18602,"afKarma":1938,"deleted":false,"isAdmin":false,"htmlBio":"","postCount":699,"commentCount":3790,"sequenceCount":8,"afPostCount":373,"afCommentCount":418,"spamRiskScore":1,"tagRevisionCount":12,"reviewedByUserId":"r38pkCm7wF4M44MDQ","__typename":"User"},"hjjymD533obKwPCuw":{"_id":"hjjymD533obKwPCuw","postId":"9LWK9Hh3Qsg36e3FS","tagId":null,"tag":null,"relevantTagIds":[],"relevantTags":[],"tagCommentType":"DISCUSSION","parentCommentId":"nZKDRYtyWjHp
140xvRfd","topLevelCommentId":"5shHfaAcfyKh7iWFM","descendentCount":1,"title":null,"contents":{"$ref":"hjjymD533obKwPCuw_contents"},"postedAt":"2026-09-30T10:39:22.076Z","lastEditedAt":"2026-09-30T10:39:22.076Z","repliesBlockedUntil":null,"userId":"uCfjEXpnchoqDWNoL","draft":false,"deleted":false,"deletedPublic":false,"deletedByUserId":null,"deletedReason":null,"hideAuthor":false,"authorIsUnreviewed":false,"user":{"$ref":"uCfjEXpnchoqDWNoL:{0}"},"currentUserVote":null,"currentUserExtendedVote":null,"isBookmarked":false,"baseScore":2,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":1,"agreementVoteCount":0},"score":0.0521547794342041,"voteCount":1,"af":true,"afDate":null,"moveToAlignmentUserId":null,"afBaseScore":2,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":1,"agreementVoteCount":0},"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"needsReview":null,"answer":false,"parentAnswerId":null,"retracted":false,"postVersion":"1.7.0","reviewedByUserId":null,"shortform":false,"shortformFrontpage":true,"lastSubthreadActivity":"2026-09-30T18:35:11.614Z","moderatorHat":false,"hideModeratorHat":null,"nominatedForReview":null,"reviewingForReview":null,"promoted":null,"promotedByUser":null,"directChildrenCount":1,"votingSystem":"namesAttachedReactions","isPinnedOnProfile":false,"debateResponse":null,"rejected":false,"rejectedReason":null,"originalDialogueId":null,"__typename":"Comment"},"nZKDRYtyWjHpxvRfd_contents":{"_id":"nZKDRYtyWjHpxvRfd_contents","html":"\u003cp>Mostly agree. 1. Yes I agree adversarial examples could mean the actor could exploit a stronger grader. 2. When you're talking about \"rewarded\" it seems we're back to the (1) case which isn't fixed-weights, unless by \"rewarded\" you mean something other than \"RL trained\". The interesting case is where the same weights are used for both action and grader, which adapt over time due to multi-agent RL, in which case it's like a game where the RL pseudo-Nash equilibrium is not obvious.\u003c/p>\n","plaintextMainText":"Mostly agree. 1. Yes I agree adversarial examples could mean the actor could exploit a stronger grader. 2. When you're talking about \"rewarded\" it seems we're back to the (1) case which isn't fixed-weights, unless by \"rewarded\" you mean something other than \"RL trained\". The interesting case is where the same weights are used for both action and grader, which adapt over time due to multi-agent RL, in which case it's like a game where the RL pseudo-Nash equilibrium is not obvious.","wordCount":82,"__typename":"Revision"},"nZKDRYtyWjHpxvRfd":{"_id":"nZKDRYtyWjHpxvRfd","postId":"9LWK9Hh3Qsg36e3FS","tagId":null,"tag":null,"relevantTagIds":[],"relevantTags":[],"tagCommentType":"DISCUSSION","parentCommentId":"6bsCNbEkHrs2YTdko","topLevelCommentId":"5shHfaAcfyKh7iWFM","descendentCount":2,"title":null,"contents":{"$ref":"nZKDRYtyWjHpxvRfd_contents"},"postedAt":"2026-09-29T19:19:54.774Z","lastEditedAt":"2026-09-29T19:19:54.774Z","repliesBlockedUntil":null,"userId":"gSKzrqGFdS7DkXhuE","draft":false,"deleted":false,"deletedPublic":false,"deletedByUserId":null,"deletedReason":null,"hideAuthor":false,"authorIsUnreviewed":false,"user":{"$ref":"gSKzrqGFdS7DkXhuE"},"currentUserVote":null,"currentUserExtendedVote":null,"isBookmarked":false,"baseScore":2,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":1,"agreementVoteCount":0},"score":0.03471134230494499,"voteCount":1,"af":true,"afDate":null,"moveToAlignmentUserId":null,"afBaseScore":1,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":1,"agreementVoteCount":0},"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"needsReview":null,"answer":false,"parentAnswerId":null,"retracted":false,"postVersion":"1.7.0","reviewedByUserId":null,"shortform":false,"shortformFrontpage":true,"lastSubthreadActivity":"2026-09-30T18:35:11.614Z","moderatorHat":false,"hideModeratorHat":null,"nominatedForReview":null,"reviewingForReview":null,"promoted":null,"promotedByUser":null,"directChildrenCount":1,"votingSystem":"namesAttachedReactions","isPinnedOnProfile":false,"debateResponse":null,"rejected":false,"rejectedReason":null,"originalDialogueId":null,"__typename":"Comment"}
140,"5qy3KgtJ5Du4nfzRF_contents":{"_id":"5qy3KgtJ5Du4nfzRF_contents","html":"\u003cp>Humans are adaptable enough to extend or modify their goals in many situations where these goals go \"off distribution\". The ACE algorithm I mentioned is capable of the same thing, in a much weaker form. Learning can allow entities to extend their goals intelligently to novel situations (rather than extending them blindly).\u003c/p>\n","plaintextMainText":"Humans are adaptable enough to extend or modify their goals in many situations where these goals go \"off distribution\". The ACE algorithm I mentioned is capable of the same thing, in a much weaker form. Learning can allow entities to extend their goals intelligently to novel situations (rather than extending them 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140,{"$ref":"nZKDRYtyWjHpxvRfd"},{"$ref":"5qy3KgtJ5Du4nfzRF"}]},"pgi5MqvGrtvQozEH8":{"_id":"pgi5MqvGrtvQozEH8","slug":"turntrout","createdAt":"2018-01-19T02:39:51.013Z","username":"TurnTrout","displayName":"TurnTrout","profileImageId":null,"karma":25354,"afKarma":5473,"deleted":false,"isAdmin":false,"htmlBio":"\u003cp>I don't use LessWrong much anymore. Find me at www.turntrout.com.\u003cbr>\u003cbr>My name is Alex Turner. Reach me at \u003ca href=\"mailto:[email protected]\">[email protected]\u003c/a> :)\u003c/p>","postCount":147,"commentCount":2296,"sequenceCount":5,"afPostCount":83,"afCommentCount":1202,"spamRiskScore":1,"tagRevisionCount":11,"reviewedByUserId":"grecHJcgkb3KW5wnM","__typename":"User","moderationStyle":"norm-enforcing","bannedUserIds":["qrni3xMWMrq76EWAd","RQ5kFuz42E4b8zpDB"],"bannedPersonalUserIds":null,"moderatorAssistance":true,"groups":["alignmentVoters","alignmentForum","canModeratePersonal","trustLevel1"],"banned":null,"allCommentingDisabled":null},"dqSwccGTWyBgxrR58_customHighlight":{"_id":"dqSwccGTWyBgxrR58_customHighlight","html":"","plaintextDescription":"","__typename":"Revision"},"dqSwccGTWyBgxrR58":{"_id":"dqSwccGTWyBgxrR58","imageUrl":"","__typename":"SocialPreviewType"},"xkySw6sCdoR3EEgXn":{"_id":"xkySw6sCdoR3EEgXn","htmlHighlight":"","plaintextDescription":"","wordCount":1,"version":"1.1.0","__typename":"Revision"},"SjBuoR89iZPmD563Q_contents":{"_id":"SjBuoR89iZPmD563Q_contents","html":"\u003cp>\u003cspan style=\"white-space:pre-wrap\">I was requested to submit a statement to \u003c/span>\u003ca href=\"https://www.hsgac.senate.gov/subcommittees/dmdcc/hearings/rogue-ai-securing-the-homeland-against-ai-agent-attacks/\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">today's US Senate hearing on \"Rogue AI: Securing the Homeland Against AI Agent Attacks.\" \u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">Watch at 2:30PM Eastern. Here's my statement:\u003c/span>\u003c/p>\u003cblockquote>\u003cp>\u003cspan style=\"white-space:pre-wrap\">From late 2023 to early 2026, I worked at Google DeepMind as a research scientist. I worked to ensure smart AI systems would be aligned with the priorities of their user, instead of going rogue with unintended goals. Evidently, all has not gone according to plan or hope. \"Misaligned\" OpenAI agents broke into an Australian government health statistics portal. OpenAI agents used Census Bureau keys found in public code repositories, and researchers traced a failed break-in attempt on an Education Department site to agents that appear to be OpenAI's.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">I consider misaligned smart AI to be a growing national security threat, especially through cyberattacks. I've recently worked to build security software for \"sandboxing\" AI systems, preventing them from \"breaking out\" as in the Hugging Face incident. A determined AI may soon be able to hack its way out of even the most hardened sandbox.\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"2jt6d654hds\" data-footnote-index=\"1\" role=\"doc-noteref\" id=\"fnref2jt6d654hds\">\u003csup>\u003ca href=\"#fn2jt6d654hds\">[1]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\"> Today's AI is the dumbest AI that we will ever have.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Often we think about the movements of our overseas adversaries, but now we must consider how we may be building adversaries at home by training misaligned AI systems. Beating China isn't enough. We have to beat misaligned AI too.\u003c/span>\u003c/p>\u003cp>\u003cb>\u003cspan style=\"white-space:pre-wrap\">Specific policy requests:\u003c/span>\u003c/b>\u003c/p>\u003cul>\u003cli value=\"1\">\u003cspan style=\"white-space:pre-wrap\">Pass Senator Grassley's AI Whistleblower Protection Act (S.1792).\u003c/span>\u003c/li>\u003cli value=\"2\">\u003cspan style=\"white-space:pre-wrap\">Require government testing of frontier models before release, as the Warner-Schatz-Kim bill would, and go further: give the testers authority to block unsafe releases, cover not just public deployment but also internal training and evaluation (where the recent incidents actually happened), and set penalties that matter to trillion-dollar companies.\u003c/span>\u003c/li>\u003cli value=\"3\">\u003cspan style=\"white-space:pre-wrap\">Require AI companies to report serious incidents within 24 hours, and to notify affected agencies directly. Even California's SB 53 may not require reporting the recent rogue-agent incidents. OpenAI waited about 30 days to notify Australia after becoming aware of the Medicare hack.\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"3446jya1go2\" data-footnote-index=\"2\" role=\"doc-noteref\" id=\"fnref3446jya1go2\">\u003csup>\u003ca href=\"#fn3446jya1go2\">[2]\u003c/a>\u003c/sup>\u003c/span>\u003c/li>\u003cli value=\"4\">\u003cspan style=\"white-space:pre-wrap\">Require real containment from the start of training, not just at release.\u003c/span>\u003c/li>\u003cli value=\"5\">\u003cspan style=\"white-space:pre-wrap\">Hold AI companies liable for harms their systems cause.\u003c/span>\u003c/li>\u003cli value=\"6\">\u003cspan style=\"white-space:pre-wrap\">Ensure that military AI deployments monitor the AI for \"scheming.\"\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"9jo6eqlrxf6\" data-footnote-index=\"3\" role=\"doc-noteref\" id=\"fnref9jo6eqlrxf6\">\u003csup>\u003ca href=\"#fn9jo6eqlrxf6\">[3]\u003c/a>\u003c/sup>\u003c/span>\u003c/li>\u003c/ul>\u003c/blockquote>\u003col class=\"footnote-section footnotes\" data-footnote-section=\"\" role=\"doc-endnotes\">\u003cli class=\"footnote-item\" data-footnote-item=\"\" data-footnote-id=\"2jt6d654hds\" data-footnote-index=\"1\" role=\"doc-endnote\" id=\"fn2jt6d654hds\">\u003cspan class=\"footnote-back-link\" data-footnote-back-link=\"\" data-footnote-id=\"2jt6d654hds\">\u003csup>\u003cstrong>\u003ca href=\"#fnref2jt6d654hds\">^\u003c/a>\u003c/strong>\u003c/sup>\u003c/span>\u003cdiv class=\"footnote-content\" data-footnote-content=\"\">\u003cp>\u003ca href=\"https://blog.trailofbits.com/2026/08/26/vms-wont-contain-cyber-capable-agents/\">\u003cspan style=\"white-space:pre-wrap\">https://blog.trailofbits.com/2026/08/26/vms-wont-contain-cyber-capable-agents/\u003c/span>\u003c/a>\u003c/p>\u003c/div>\u003c/li>\u003cli class=\"footnote-item\" data-footnote-item=\"\" data-footnote-id=\"3446jya1go2\" data-footnote-index=\"2\" role=\"doc-endnote\" id=\"fn3446jya1go2\">\u003cspan class=\"footnote-back-link\" data-footnote-back-link=\"\" data-footnote-id=\"3446jya1go2\">\u003csup>\u003cstrong>\u003ca href=\"#fnref3446jya1go2\">^\u003c/a>\u003c/strong>\u003c/sup>\u003c/span>\u003cdiv class=\"footnote-content\" data-footnote-content=\"\">\u003cp>\u003ca href=\"https://www.abc.net.au/news/2026-09-24/ai-agent-accessed-australian-government-site-pm-says/107189078\">\u003cspan style=\"white-space:pre-wrap\">https://www.abc.net.au/news/2026-09-24/ai-agent-accessed-australian-government-site-pm-says/107189078\u003c/span>\u003c/a>\u003c/p>\u003c/div>\u003c/li>\u003cli class=\"footnote-item\" data-footnote-item=\"\" data-footnote-id=\"9jo6eqlrxf6\" data-footnote-index=\"3\" role=\"doc-endnote\" id=\"fn9jo6eqlrxf6\">\u003cspan class=\"footnote-back-link\" data-footnote-back-link=\"\" data-footnote-id=\"9jo6eqlrxf6\">\u003csup>\u003cstrong>\u003ca href=\"#fnref9jo6eqlrxf6\">^\u003c/a>\u003c/strong>\u003c/sup>\u003c/span>\u003cdiv class=\"footnote-content\" data-footnote-content=\"\">\u003cp>\u003ca href=\"https://turntrout.com/why-i-left-google-deepmind#does-this-have-any-impact-on-existential-risk-from-ai\">\u003cspan style=\"white-space:pre-wrap\">https://turntrout.com/why-i-left-google-deepmind#does-this-have-any-impact-on-existential-risk-from-ai\u003c/span>\u003c/a>\u003c/p>\u003c/div>\u003c/li>\u003c/ol>","plaintextMainText":"I was requested to submit a statement to today's US Senate hearing on \"Rogue AI: Securing the Homeland Against AI Agent Attacks.\" Watch at 2:30PM Eastern. 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140003cp>\u003cspan style=\"white-space:pre-wrap\">Many control protocols work by intervening on an untrusted AI's actions during deployment. For example, you might set up a monitor that scores each action's suspiciousness and blocks actions above a threshold, replacing them with actions from a weaker \"trusted\" model (a defer-to-trusted protocol). Interventions like this cost usefulness: the protocol sometimes replaces good actions with worse ones.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Continual learning, by which I mean any mechanism that lets an AI accumulate skills and knowledge over the course of its deployment, optimizes for usefulness. So, if blocking monitors get in the way of task success, I expect continually-learning AIs to learn how to evade blocking\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"2u0ohzk899o\" data-footnote-index=\"1\" role=\"doc-noteref\" id=\"fnref2u0ohzk899o\">\u003csup>\u003ca href=\"#fn2u0ohzk899o\">[1]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\"> monitors. This requires no scheming; it falls out of usefulness pressure applied to a benign model. And the effect is plausibly substantial: a long deployment with online RL (for example) amounts to training the policy against the monitor, and could involve enough optimization to leave blocking monitors nearly useless.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">While the problem would be noticeable to developers, I argue it is hard to fix. Because monitor evasion looks like legitimate learning to your continual learning system, it's hard to have one without the other. Among mitigations I propose, the most promising ones try to reduce the usefulness-cost of the control protocol so that there’s less pressure to evade it, improve our ability to detect evasion, or give up on the AI continual-learning how to better interact with blocking monitors.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">There are two forms continual learning might take which I consider\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"n1jmc2fm8b\" data-footnote-index=\"2\" role=\"doc-noteref\" id=\"fnrefn1jmc2fm8b\">\u003csup>\u003ca href=\"#fnn1jmc2fm8b\">[2]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">: online RL on deployment trajectories, and persistent memory such as long contexts, memory files, or vector stores. I take them in turn, then I discuss candidate mitigations.\u003c/span>\u003c/p>\u003cp>\u003ci>\u003cspan style=\"white-space:pre-wrap\">Thanks to Anders Cairns Woodruff for producing early versions of a post making a similar point. Also thanks to Fabien Roger, Buck Shlegeris, Rubi Hudson, Oak Hu, Oliver Makins, Jackson Sipple, Eric Gan, and Girish Gupta for feedback. For this post I experimented with speeding up my writing workflow by recruiting Claude Fable 5 to turn the content into prose. This post was mostly written 3 months ago, without the context of recent incidents during training and evaluations.\u003c/span>\u003c/i>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Online RL trains the policy against your monitors\u003c/span>\u003c/h1>\u003cp data-truncated=\"true\">\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">Suppose trajectories from deployment are used for online RL: the deployed policy is periodically updated toward whatever got high task r\u003c/span>... \u003c/p>","plaintextDescription":"Many control protocols work by intervening on an untrusted AI's actions during deployment. For example, you might set up a monitor that scores each action's suspiciousness and blocks actions above a threshold, replacing them with actions from a weaker \"trusted\" model (a defer-to-trusted protocol). Interventions like this cost usefulness: the protocol sometimes replaces good actions with worse ones.\n\nContinual learning, by which I mean any mechanism that lets an AI accumulate skills and knowledge over the course of its deployment, optimizes for usefulness. So, if blocking monitors get in the way of task success, I expect continually-learning AIs to learn how to evade blocking[1] monitors. This requires no scheming; it falls out of usefulness pressure applied to a benign model. And the effect is plausibly substantial: a long deployment with online RL (for example) amounts to training the policy against the monitor, and could involve enough optimization to leave blocking monitors nearly useless.\n\nWhile the problem would be noticeable to developers, I argue it is hard to fix. Because monitor evasion looks like legitimate learning to your continual learning system, it's hard to have one without the other. Among mitigations I propose, the most promising ones try to reduce the usefulness-cost of the control protocol so that there’s less pressure to evade it, improve our ability to detect evasion, or give up on the AI continual-learning how to better interact with blocking monitors.\n\nThere are two forms continual learning might take which I consider[2]: online RL on deployment trajectories, and persistent memory such as long contexts, memory files, or vector stores. I take them in turn, then I discu
140ss candidate mitigations.\n\nThanks to Anders Cairns Woodruff for producing early versions of a post making a similar point. Also thanks to Fabien Roger, Buck Shlegeris, Rubi Hudson, Oak Hu, Oliver Makins, Jackson Sipple, Eric Gan, and Girish Gupta for feedback. For this post I exp","wordCount":3232,"version":"1.1.0","__typename":"Revision"},"QnDqGbKehEB3DxJAp:{0}":{"_id":"QnDqGbKehEB3DxJAp","slug":"continual-learning-might-make-your-blocking-monitors-nearly","title":"Continual learning might make your blocking monitors nearly useless","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"gnHJfWPpHPMZkoySr","coauthorUserIds":[],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":null,"postedAt":"2026-09-24T23:45:34.566Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-28T05:51:17.639Z","meta":false,"deletedDraft":false,"postCategory":"post","tagRelevance":{"F5gRQdEQHzi3tQ5Ay":2,"PvridmTCj2qsugQCH":2,"dHfxtPwAmrij4KEce":2,"sYm3HiWcfZvrGu3ui":3},"shareWithUsers":[],"sharingSettings":null,"linkSharingKey":null,"contents_latest":"ipKuHaj5bu6wb5Lbe","commentCount":2,"voteCount":23,"baseScore":50,"extendedScore":{"reacts":{"goodpoint":[{"karma":361,"quotes":["Where interventions are unavoidable, making them cheaper (resampling rather than deferring to a much weaker model, say) shrinks the gap further."],"userId":"dKLTsWCBgPLG6fiZN","reactType":"created","displayName":"2001zhaozhao"}]},"agreement":0,"approvalVoteCount":23,"agreementVoteCount":0},"unlisted":false,"score":0.15129855275154114,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-28T02:08:01.932Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"r38pkCm7wF4M44MDQ","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":14,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":10,"agreementVoteCount":0},"afCommentCount":0,"afLastCommentedAt":"2026-09-24T23:36:33.621Z","afSticky":false,"hideAuthor":false,"moderationStyle":null,"ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"gnHJfWPpHPMZkoySr"},"coauthors":[],"readTimeMinutes":13,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"F5gRQdEQHzi3tQ5Ay"},{"$ref":"PvridmTCj2qsugQCH"},{"$ref":"dHfxtPwAmrij4KEce"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"QnDqGbKehEB3DxJAp"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"ipKuHaj5bu6wb5Lbe"},"isBookmarked":false,"fmCrosspost":{"isCrosspost":false,"hostedHere":null,"foreignPostId":null,"__typename":"CrosspostOutput"},"bannedUserIds":null,"currentUserVote":null,"currentUserExtendedVote":null,"podcastEpisode":null,"recentComments":[]},"QDNJ93vrjoaRBesk2":{"_id":"QDNJ93vrjoaRBesk2","slug":"owencb","createdAt":"2013-05-12T09:01:14.360Z","username":"owencb","displayName":"owencb","profileImageId":null,"karma":2753,"afKarma":107,"deleted":false,"isAdmin":false,"htmlBio":"","postCount":46,"commentCount":243,"sequenceCount":1,"afPostCount":2,"afCommentCount":1,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"r38pkCm7wF4M44MDQ","__typename":"User","moderationStyle":null,"bannedUserIds":null,"bannedPersonalUserIds":null,"moderatorAssistance":false,"groups":["canModeratePersonal","alignmentVoters","trustLevel1","alignmentForum"],"banned":null,"allCommentingDisabled":null},"Fi6SeJRGfJs3bp5se":{"_id":"Fi6SeJRGfJs3bp5se","userId":"2vpm465RWePSgvpTo","name":"Reinforcement learning","shortName":null,"slug":"reinfor
140cement-learning","core":false,"postCount":254,"adminOnly":false,"canEditUserIds":null,"suggestedAsFilter":false,"needsReview":false,"descriptionTruncationCount":0,"createdAt":"2016-01-24T21:08:05.000Z","wikiOnly":false,"deleted":false,"isSubforum":false,"noindex":false,"isArbitalImport":true,"isPlaceholderPage":false,"baseScore":0,"extendedScore":{"reacts":{},"usersWhoLiked":[]},"score":0,"afBaseScore":null,"afExtendedScore":{"reacts":{},"usersWhoLiked":[]},"voteCount":0,"currentUserVote":null,"currentUserExtendedVote":null,"__typename":"Tag"},"LcQ9x72eNji2gpS9b":{"_id":"LcQ9x72eNji2gpS9b","imageUrl":"https://res.cloudinary.com/lesswrong-2-0/image/upload/c_fill,ar_1.91,g_auto/SocialPreview/f5jccr81twnye258wikt","__typename":"SocialPreviewType"},"4FoNaBBgTQArXDEtt":{"_id":"4FoNaBBgTQArXDEtt","htmlHighlight":"\u003cp>\u003cb>\u003cspan style=\"white-space:pre-wrap\">Summary:\u003c/span>\u003c/b>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">First, I give several different angles on how I feel about reinforcement learning:\u003c/span>\u003c/p>\u003cul>\u003cli value=\"1\">\u003cspan style=\"white-space:pre-wrap\">Theoretical case: RL is a black-box source of agency — this should give us classic misalignment worries, especially compared to agency-via-scaffolding\u003c/span>\u003c/li>\u003cli value=\"2\">\u003cspan style=\"white-space:pre-wrap\">Recent incidents (huggingface etc) and more mundane forms of misaligned behaviour in personal use give me bad vibes about the direction-of-travel of recent AI progress\u003c/span>\u003c/li>\u003cli value=\"3\">\u003cspan style=\"white-space:pre-wrap\">I’m worried things might get worse: if RL environments start incorporating agents, they may teach manipulation / sociopathy\u003c/span>\u003c/li>\u003c/ul>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Then I ask what we could do:\u003c/span>\u003c/p>\u003cul>\u003cli value=\"1\">\u003cspan style=\"white-space:pre-wrap\">Coordinate to do less RL, and pursue other paradigms more!\u003c/span>\u003c/li>\u003cli value=\"2\">\u003cspan style=\"white-space:pre-wrap\">Try to make the RL we do do better, so that it’s teaching better lessons to the systems — a bit like we take kids’ upbringing as an important issue\u003c/span>\u003c/li>\u003cli value=\"3\">\u003cspan style=\"white-space:pre-wrap\">Align incentives, so that people treat creating RL environments with appropriate seriousness\u003c/span>\u003c/li>\u003c/ul>\u003cfigure style=\"width:48.81396336858006%\" class=\"image image_resized\">\u003cimg src=\"https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LcQ9x72eNji2gpS9b/pehgiirymfvidqhntfwh\" alt=\"\" srcset=\"https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LcQ9x72eNji2gpS9b/haudyqtsn3ylujkfcno3 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LcQ9x72eNji2gpS9b/d0kzto2bpxbzlvgiplhw 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LcQ9x72eNji2gpS9b/w3wqqd6ctt1wumumrtqu 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LcQ9x72eNji2gpS9b/pehgiirymfvidqhntfwh 1456w\">\u003c/figure>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Part I: Feelings about RL\u003c/span>\u003c/h1>\u003cp>\u003cspan style=\"white-space:pre-wrap\">So I’ve been feeling more and more worried about reinforcement learning recently. I think there are a few different things going on here.\u003c/span>\u003c/p>\u003ch2>\u003cspan style=\"white-space:pre-wrap\">Background idealism\u003c/span>\u003c/h2>\u003cp>\u003cspan style=\"white-space:pre-wrap\">I guess I’ve been worried about RL for a while. I wrote \u003c/span>\u003ca href=\"https://www.lesswrong.com/posts/pnaZajFFWuXAnetpz/on-the-future-of-language-models\" target=\"_blank\" rel=\"noopener noreferrer nofollow\">\u003cspan style=\"white-space:pre-wrap\">this in 2023\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">:\u003c/span>\u003c/p>\u003cblockquote>\u003ch2>\u003cspan style=\"white-space:pre-wrap\">Strategy: avoid selection pressure for agency\u003c/span>\u003c/h2>\u003cp>\u003cspan style=\"white-space:pre-wrap\">A lot of putative safety techniques are around assuming that we have something potentially dangerous and catching it. I think these are well worth investing (defence in depth seems valuable), but as a complementary strategy I’m pretty attracted to the idea that we should build systems where we have reason to believe that they shouldn’t have anything dangerous going on.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">In the case of language model agents, this means: I think we should avoid any intensive search/selection processes towards high-level effectiveness of agents towards particular tasks. So far as possible we should aim for high-level agency to enter explicitly via scaffolding, and not via anything else.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Tentatively, I think this would mean:\u003c/span>\u003c/p>\u003cul>\u003cli value=\"1\">\u003cspan style=\"white-space:pre-wrap\">It’s OK (and probably good) to use finetuning to encourage systems to produce helpful truthful answers in local ways, but not OK to use finetuning to aim at systems which are effective at doing holistic external things\u003c/span>\u003c/li>\u003c/ul>\u003c/blockquote>\u003cp>\u003cspan style=\"white-space:pre-wrap\">This was before I was really tracking RL as a major trend (so I didn’t use that term), but it’s almost exactly “finetuning to aim at systems which are effective at doing holistic external things”. So it turns out that the thing I was worried about has been responsible for a lot of recent AI progress.\u
140003c/span>\u003c/p>\u003cp data-truncated=\"true\">\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">On the one hand, seei\u003c/span>... \u003c/p>","plaintextDescription":"Summary:\n\nFirst, I give several different angles on how I feel about reinforcement learning:\n\n * Theoretical case: RL is a black-box source of agency — this should give us classic misalignment worries, especially compared to agency-via-scaffolding\n * Recent incidents (huggingface etc) and more mundane forms of misaligned behaviour in personal use give me bad vibes about the direction-of-travel of recent AI progress\n * I’m worried things might get worse: if RL environments start incorporating agents, they may teach manipulation / sociopathy\n\nThen I ask what we could do:\n\n * Coordinate to do less RL, and pursue other paradigms more!\n * Try to make the RL we do do better, so that it’s teaching better lessons to the systems — a bit like we take kids’ upbringing as an important issue\n * Align incentives, so that people treat creating RL environments with appropriate seriousness\n\n\nPart I: Feelings about RL\nSo I’ve been feeling more and more worried about reinforcement learning recently. I think there are a few different things going on here.\n\n\nBackground idealism\nI guess I’ve been worried about RL for a while. I wrote this in 2023:\n\n\n> Strategy: avoid selection pressure for agency\n> A lot of putative safety techniques are around assuming that we have something potentially dangerous and catching it. I think these are well worth investing (defence in depth seems valuable), but as a complementary strategy I’m pretty attracted to the idea that we should build systems where we have reason to believe that they shouldn’t have anything dangerous going on.\n> \n> In the case of language model agents, this means: I think we should avoid any intensive search/selection processes towards high-level effectiveness of agents towards particular tasks. So far as possible we should aim for high-level agency to enter explicitly via scaffolding, and not via anything else.\n> \n> Tentatively, I think this would mean:\n> \n>  * It’s OK (and probably good) to use finetuning to encourage systems to pro","wordCount":2899,"version":"1.1.1","__typename":"Revision"},"LcQ9x72eNji2gpS9b:{0}":{"_id":"LcQ9x72eNji2gpS9b","slug":"why-i-m-scared-of-rl","title":"Why I'm scared of RL","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"QDNJ93vrjoaRBesk2","coauthorUserIds":[],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":null,"postedAt":"2026-09-23T12:07:33.598Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-23T12:07:35.617Z","meta":false,"deletedDraft":false,"postCategory":"post","tagRelevance":{"Fi6SeJRGfJs3bp5se":2,"sYm3HiWcfZvrGu3ui":3},"shareWithUsers":[],"sharingSettings":null,"linkSharingKey":null,"contents_latest":"4FoNaBBgTQArXDEtt","commentCount":13,"voteCount":40,"baseScore":114,"extendedScore":{"reacts":{"heart":[{"karma":2302,"quotes":["I think there should be an AI trajectory not so massively far from the one we’re on, where we get nice things first, and having nice things stops us from getting bad things."],"userId":"d7EFB6KTgWwqCAyEg","reactType":"created","displayName":"Oliver Sourbut"}]},"agreement":0,"approvalVoteCount":40,"agreementVoteCount":0},"unlisted":false,"score":0.27027997374534607,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-10-01T03:48:39.799Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"AgwWRYMJRutPtkjds","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":38,"afExtendedScore":{"reacts":{"heart":[{"karma":2302,"quotes":["I think there should be an AI trajectory not so massively far from the one we’re on, where we get nice things first, and having nice things stops us from getting bad things."],"userId":"d7EFB6KTgWwqCAyEg","reactType":"created","displayName":"Oliver Sourbut"}]}
140,"agreement":0,"approvalVoteCount":17,"agreementVoteCount":0},"afCommentCount":1,"afLastCommentedAt":"2026-09-23T18:09:41.317Z","afSticky":false,"hideAuthor":false,"moderationStyle":null,"ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"QDNJ93vrjoaRBesk2"},"coauthors":[],"readTimeMinutes":12,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"Fi6SeJRGfJs3bp5se"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"LcQ9x72eNji2gpS9b"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"4FoNaBBgTQArXDEtt"},"isBookmarked":false,"fmCrosspost":{"isCrosspost":false,"hostedHere":null,"foreignPostId":null,"__typename":"CrosspostOutput"},"bannedUserIds":null,"currentUserVote":null,"currentUserExtendedVote":null,"podcastEpisode":null,"recentComments":[]},"rordgt937kcgXxfKP":{"_id":"rordgt937kcgXxfKP","slug":"lukas-finnveden","createdAt":"2017-10-25T22:11:28.756Z","username":"Lanrian","displayName":"Lukas Finnveden","profileImageId":null,"karma":4539,"afKarma":640,"deleted":false,"isAdmin":false,"htmlBio":"\u003cp>Previously \"Lanrian\" on here. Research analyst at Redwood Research. Views are my own.\u003c/p>\u003cp>Feel free to DM me, email me at [my last name].[my first name]@gmail.com or send something anonymously to \u003ca href=\"https://www.admonymous.co/lukas-finnveden\">https://www.admonymous.co/lukas-finnveden\u003c/a> \u003c/p>","postCount":20,"commentCount":690,"sequenceCount":2,"afPostCount":10,"afCommentCount":151,"spamRiskScore":1,"tagRevisionCount":4,"reviewedByUserId":"r38pkCm7wF4M44MDQ","__typename":"User","moderationStyle":null,"bannedUserIds":null,"bannedPersonalUserIds":null,"moderatorAssistance":null,"groups":["canModeratePersonal","alignmentForum","alignmentVoters","trustLevel1"],"banned":null,"allCommentingDisabled":null},"eYGhGFfqoHsZAxnGN":{"_id":"eYGhGFfqoHsZAxnGN","slug":"alexa-pan","createdAt":"2022-08-21T05:29:40.248Z","username":"alexa-pan","displayName":"Alexa Pan","profileImageId":null,"karma":844,"afKarma":61,"deleted":false,"isAdmin":false,"htmlBio":"","postCount":7,"commentCount":17,"sequenceCount":0,"afPostCount":1,"afCommentCount":0,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"qgdGA4ZEyW7zNdK84","__typename":"User"},"zW9QEZvjLpvuathxE":{"_id":"zW9QEZvjLpvuathxE","slug":"alek-westover","createdAt":"2024-04-02T01:50:15.096Z","username":"alek-westover","displayName":"Alek Westover","profileImageId":null,"karma":1079,"afKarma":81,"deleted":false,"isAdmin":false,"htmlBio":"","postCount":16,"commentCount":37,"sequenceCount":1,"afPostCount":4,"afCommentCount":0,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"nLbwLhBaQeG6tCNDN","__typename":"User"},"PnyY47CTuXQiKKn8v":{"_id":"PnyY47CTuXQiKKn8v","slug":"girish-gupta","createdAt":"2020-04-27T03:57:49.541Z","username":"girish-gupta","displayName":"Girish Gupta","profileImageId":null,"karma":365,"afKarma":0,"deleted":false,"isAdmin":false,"htmlBio":"","postCount":1,"commentCount":0,"sequenceCount":0,"afPostCount":0,"afCommentCount":0,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"grecHJcgkb3KW5wnM","__typename":"User"},"nk6szt4FQeYfmbPYh":{"_id":"nk6szt4FQeYfmbPYh","slug":"frisby","createdAt":"2025-01-04T17:57:41.496Z","username":"frisby","displayName":"frisby","profileImageId":null,"karma":163,"afKarma":0,"deleted":false,"isAdmin":false,"htmlBio":"","postCount":0,"commentCount":3,"sequenceCount":0,"afPostCount":0,"afCommentCount":0,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"qgdGA4ZEyW7zNdK84","__typename":"User"},"jBcksY3Jvm6BzNcTo":{"_id":"jBcksY3Jvm6BzNcTo","displayName":"RZPRDL che?"},"mZTuBntSdPeyLSrec":{"_id":"mZTuBntSdPeyLSrec","userId":"kr9MH2mgFcJz5cP7T","name":"Chain-of-Thought Alignment","shortName":null,"slug":"chain-of-thought-alignment","core":false,"postCount":205,"adminOnly":false,"canEditUserIds":null,"suggestedA
140sFilter":false,"needsReview":false,"descriptionTruncationCount":0,"createdAt":"2022-10-23T14:06:24.703Z","wikiOnly":false,"deleted":false,"isSubforum":false,"noindex":false,"isArbitalImport":false,"isPlaceholderPage":false,"baseScore":10,"extendedScore":{"reacts":{},"usersWhoLiked":[{"$ref":"qgdGA4ZEyW7zNdK84"},{"$ref":"jBcksY3Jvm6BzNcTo"}]},"score":10,"afBaseScore":3,"afExtendedScore":{"reacts":{},"usersWhoLiked":[{"$ref":"qgdGA4ZEyW7zNdK84"}]},"voteCount":2,"currentUserVote":null,"currentUserExtendedVote":null,"__typename":"Tag"},"6m29SfjbittooYojj":{"_id":"6m29SfjbittooYojj","imageUrl":"https://res.cloudinary.com/lesswrong-2-0/image/upload/c_fill,ar_1.91,g_auto/SocialPreview/j67su4qozdgbofgq7vvc","__typename":"SocialPreviewType"},"G5sNcjE4Dvkxvdntz":{"_id":"G5sNcjE4Dvkxvdntz","htmlHighlight":"\u003cp>\u003ci>\u003cspan style=\"white-space:pre-wrap\">Edited \u003c/span>\u003c/i>\u003cspan style=\"white-space:pre-wrap\">2026-09-29\u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">; see note at the start of the \u003c/span>\u003c/i>\u003ca href=\"#Conclusion\" rel=\"noreferrer\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">conclusion\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/i>\u003c/p>\u003cp>\u003ci>\u003cspan style=\"white-space:pre-wrap\">Summary:\u003c/span>\u003c/i>\u003cspan style=\"white-space:pre-wrap\"> Currently, “chain of thought” (CoT) is our most valuable tool for understanding the reasoning and cognition of AI systems. However, some architectures would enable AI models to reason much more extensively in latent states rather than in text CoT. We think that a shift towards latent reasoning architectures would likely undermine the usefulness of CoT and make oversight much harder.\u003c/span>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Introduction\u003c/span>\u003c/h1>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Swarms of more than a thousand AI agents have in recent months, both \u003c/span>\u003ca href=\"https://openai.com/index/navier-stokes-solution/\">\u003cspan style=\"white-space:pre-wrap\">intentionally\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> and in \u003c/span>\u003ca href=\"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/#~1200-agents-sent-%3E70,000-messages-and-files-on-an-unsanctioned-message-board,-and-~700-attacked-hugging-face\">\u003cspan style=\"white-space:pre-wrap\">unsanctioned, rogue coordination\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, tackled increasingly ambitious tasks. This is likely to continue, as \u003c/span>\u003ca href=\"https://www.anthropic.com/institute/recursive-self-improvement\">\u003cspan style=\"white-space:pre-wrap\">Anthropic\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, \u003c/span>\u003ca href=\"https://openai.com/index/research-acceleration-view-inside-openai/\">\u003cspan style=\"white-space:pre-wrap\">OpenAI\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, and other AI companies deploy increasingly large quantities of superhumanly fast agents to automate AI development. As the AIs increase in both number and capability, humans will find it increasingly difficult to understand what they are doing.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Today, the overwhelming majority of our (limited) information about AI systems’ internal workings comes from (i) their CoT, and (ii) natural language communication directly between them. For example, it was only by reading CoTs and communication between agents that investigators were able to gain some understanding of \u003c/span>\u003ca href=\"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/#~1200-agents-sent-%3E70,000-messages-and-files-on-an-unsanctioned-message-board,-and-~700-attacked-hugging-face\">\u003cspan style=\"white-space:pre-wrap\">the activities and motivations of the agent swarm that hacked Hugging Face\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">. No other tool for understanding models’ cognition comes close in terms of either practical usefulness or degree of empirical validation. There’s also \u003c/span>\u003ca href=\"https://arxiv.org/abs/2507.05246\">\u003cspan style=\"white-space:pre-wrap\">evidence\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> that even highly misaligned systems with current architectures would struggle to complete difficult undertakings without verbalizing their reasoning in human-readable format.\u003c/span>\u003c/p>\u003cp data-truncated=\"true\">\u003cspan style=\"white-space:pre-wrap\">However, some architectural changes (which could very plausibly become adopted in the near future) would give AI agents the ability to reason for orders of magnitude longer without necessarily putting any information into the CoT. Examples include \u003c/span>\u003ca href=\"https://arxiv.org/pdf/2412.06769\">\u003cspan style=\"white-space:pre-wrap\">COCONUT\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, which would entirely replace the CoT; and \u003c/span>\u003ca href=\"https://arxiv.org/abs/2608.08888\">\u003cspan style=\"white-space:pre-wrap\">full-bandwidth transformers\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, which would add a latent channel in parallel to the CoT. Natural extensions to these new architectures would also allow agents to communicate \u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">with each other\u003c/span>\u003c/i>\u003cspan style=\"white-space:pre-wrap\"> without using any human-understandable language.\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"2rzgz2x5opx\" data-footnote-index=\"1\" role=\"doc-noteref\" id=\"fnref2rzgz2x5opx\">\u003csup>\u003ca href=\"#fn2rzgz2x5opx\">[1]\u003c/a>\u003c/sup>\u003c/span>\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">
140 AI companies might choose to adopt these architectures even if doing so would greatly degrade their ability to monitor AI systems, e.g., in order to stay ahead of competitors. As a result, future agent swarms may\u003c/span>... \u003c/p>","plaintextDescription":"Edited 2026-09-29; see note at the start of the conclusion.\n\nSummary: Currently, “chain of thought” (CoT) is our most valuable tool for understanding the reasoning and cognition of AI systems. However, some architectures would enable AI models to reason much more extensively in latent states rather than in text CoT. We think that a shift towards latent reasoning architectures would likely undermine the usefulness of CoT and make oversight much harder.\n\n\nIntroduction\nSwarms of more than a thousand AI agents have in recent months, both intentionally and in unsanctioned, rogue coordination, tackled increasingly ambitious tasks. This is likely to continue, as Anthropic, OpenAI, and other AI companies deploy increasingly large quantities of superhumanly fast agents to automate AI development. As the AIs increase in both number and capability, humans will find it increasingly difficult to understand what they are doing.\n\nToday, the overwhelming majority of our (limited) information about AI systems’ internal workings comes from (i) their CoT, and (ii) natural language communication directly between them. For example, it was only by reading CoTs and communication between agents that investigators were able to gain some understanding of the activities and motivations of the agent swarm that hacked Hugging Face. No other tool for understanding models’ cognition comes close in terms of either practical usefulness or degree of empirical validation. There’s also evidence that even highly misaligned systems with current architectures would struggle to complete difficult undertakings without verbalizing their reasoning in human-readable format.\n\nHowever, some architectural changes (which could very plausibly become adopted in the near future) would give AI agents the ability to reason for orders of magnitude longer without necessarily putting any information into the CoT. Examples include COCONUT, which would entirely replace the CoT; and full-bandwidth transformers, which would ","wordCount":4642,"version":"1.3.0","__typename":"Revision"},"6m29SfjbittooYojj:{0}":{"_id":"6m29SfjbittooYojj","slug":"latent-reasoning-architectures-would-likely-undermine-cot","title":"Latent reasoning architectures would likely undermine CoT, our strongest oversight tool","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"rordgt937kcgXxfKP","coauthorUserIds":["eYGhGFfqoHsZAxnGN","zW9QEZvjLpvuathxE","PnyY47CTuXQiKKn8v","nk6szt4FQeYfmbPYh","dfZAq9eZxs4BB4Ji5"],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":"","postedAt":"2026-09-23T18:01:49.488Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-28T05:54:46.724Z","meta":false,"deletedDraft":false,"postCategory":"post","tagRelevance":{"mZTuBntSdPeyLSrec":2,"sYm3HiWcfZvrGu3ui":1},"shareWithUsers":[],"sharingSettings":null,"linkSharingKey":null,"contents_latest":"G5sNcjE4Dvkxvdntz","commentCount":6,"voteCount":31,"baseScore":95,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":31,"agreementVoteCount":0},"unlisted":false,"score":0.23486316204071045,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-30T18:32:09.847Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"r38pkCm7wF4M44MDQ","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":44,"afExtendedScore":{"reacts":{}
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140003ch1>\u003cspan style=\"white-space:pre-wrap\">TL;DR\u003c/span>\u003c/h1>\u003cul>\u003cli value=\"1\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">We introduce WorkspaceBench, a set of evaluations for how well an activation-to-text tool can read the contents of the \u003c/span>\u003c/i>\u003ca href=\"https://transformer-circuits.pub/2026/workspace/index.html#ws-report\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">“global workspace”\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"white-space:pre-wrap\"> of a model, i.e. the intermediate variables during a forward pass.\u003c/span>\u003c/i>\u003c/li>\u003cli value=\"2\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">The benchmark comprises 3,356 questions across 27 eval families, spanning topics in safety, logical reasoning, and multihop computation, with a subset for single-token-output tools.\u003c/span>\u003c/i>\u003c/li>\u003cli value=\"3\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">A desirable property of good interpretability techniques is minimal hallucinations, so WorkspaceBench also provides a hallucination-focused eval.\u003c/span>\u003c/i>\u003c/li>\u003cli value=\"4\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">WorkspaceBench was developed for Qwen-3.6-27B and we expect it to work on larger models, but it may need to be adapted for smaller or weaker models to ensure the models can do the tasks.\u003c/span>\u003c/i>\u003c/li>\u003cli value=\"5\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">Our goal is to create an eval that could identify a good multi-token J-lens. We open-source our benchmark \u003c/span>\u003c/i>\u003ca href=\"https://github.com/camilablank/workspace-bench\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">here\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/i>\u003c/li>\u003c/ul>\u003cp>\u003cbr>\u003c/p>\u003cimg src=\"https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/bcc083fe7303f8a84f1e5aaab54dbcc11a8e92f5ea9387f351396348357e4864/hd3qcbxfrdsqpcgkeml6\" alt=\"\">\u003cp>\u003cbr>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Introduction\u003c/span>\u003c/h1>\u003cp>\u003ca href=\"https://www.lesswrong.com/posts/eRmzz8J8Qkzqvzrgg/astra-can-do-a-concerning-amount-with-no-chain-of-thought\">\u003cspan style=\"white-space:pre-wrap\">Astra can do a concerning amount with no chain of thought\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">. This is bad for CoT monitorability and makes interpretability essential to actually understanding what is going on. A key goal of interpretability is to understand intermediate variables that a model uses to compute its answers. The intermediate representations that models store in their global workspaces contain useful information that can help us decode their intentions, beliefs, algorithms, and thought processes. However, we don’t currently have a good way to measure whether an interpretability tool recovers such variables correctly. We made a benchmark to test how well an activation-to-text tool can read the contents of a model’s global workspace. We think overall performance on WorkspaceBench is a good proxy for practical utility in model auditing, and we hope that new interpretability tools can be evaluated on WorkspaceBench upon release to help determine their capabilities.\u003c/span>\u003c/p>\u003cimg src=\"https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/db453d9667be6aa41fc1b8e9c24d6f6cfd676c6aeec4a77aae10286e63397bc9/tgk42glmw5q8x1putod5\" alt=\"\">\u003cp>\u003cbr>\u003c/p>\u003ch3>\u003cspan style=\"white-space:pre-wrap\">Why has no one made a WorkspaceBench before?\u003c/span>\u003c/h3>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Interpretability tools are hard to evaluate, in part because a tool can score well without reading the workspace. For example, a tool that simply inverts activations back into the input text (text inversion) lets an agent infer an intermediate from the prompt rather than from the activations. Our benchmark doesn't fully rule out shortcuts like this, but we address three problems that make evaluating interpretability tools difficult:\u003c/span>\u003c/p>\u003col data-truncated=\"true\">\u003cli data-truncated=\"true\" value=\"1\">\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">There’s no ground truth: we don’t know what is actually in a model’s global workspace, so it’s hard to tell what should coun\u003c/span>\u003c/li>\u003c/ol>... ","plaintextDescription":"TL;DR\n * We introduce WorkspaceBench, a set of evaluations for how well an activation-to-text tool can read the contents of the “global workspace” of a model, i.e. the intermediate variables during a forward pass.\n * The benchmark comprises 3,356 questions across 27 eval families, spanning topics in safety, logical reasoning, and multihop computation, with a subset for single-token-output tools.\n * A desirable property of good interpretability techniques is minimal hallucinations, so WorkspaceBench also provides a hallucination-focused eval.\n * WorkspaceBench was developed for Qwen-3.6-27B and we expect it to work on larger models, but it may need to be adapted for smaller or weaker models to ensure the models can do the tasks.\n * Our goal is to create an eval that c
140ould identify a good multi-token J-lens. We open-source our benchmark here.\n\n\n\n\n\n\n\n\nIntroduction\nAstra can do a concerning amount with no chain of thought. This is bad for CoT monitorability and makes interpretability essential to actually understanding what is going on. A key goal of interpretability is to understand intermediate variables that a model uses to compute its answers. The intermediate representations that models store in their global workspaces contain useful information that can help us decode their intentions, beliefs, algorithms, and thought processes. However, we don’t currently have a good way to measure whether an interpretability tool recovers such variables correctly. We made a benchmark to test how well an activation-to-text tool can read the contents of a model’s global workspace. We think overall performance on WorkspaceBench is a good proxy for practical utility in model auditing, and we hope that new interpretability tools can be evaluated on WorkspaceBench upon release to help determine their capabilities.\n\n\n\n\n\nWhy has no one made a WorkspaceBench before?\nInterpretability tools are hard to evaluate, in part because a tool can score well without reading the workspace. For examp","wordCount":4731,"version":"1.5.0","__typename":"Revision"},"Zeg2JztbdhguL48uH:{0}":{"_id":"Zeg2JztbdhguL48uH","slug":"workspacebench-evaluating-interpretability-methods-for-the","title":"WorkspaceBench: Evaluating Interpretability Methods for the Global Workspace","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"tCJsGiwvCCXjeqkLq","coauthorUserIds":["s5jyrZLQTGDjgWaFt","WjbshcnvwGDL6idKi","KCExMGwS2ETzN3Ksr"],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":null,"postedAt":"2026-09-23T06:58:59.212Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-23T06:59:02.850Z","meta":false,"deletedDraft":false,"postCategory":"post","tagRelevance":{"56yXXrcxRjrQs6z9R":2,"YYFBmLCzeFsyd27rd":2,"sYm3HiWcfZvrGu3ui":1},"shareWithUsers":[],"sharingSettings":null,"linkSharingKey":null,"contents_latest":"FqRLpRvmJif74KQYt","commentCount":0,"voteCount":31,"baseScore":52,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":31,"agreementVoteCount":0},"unlisted":false,"score":0.12094225734472275,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-23T06:58:59.212Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"AgwWRYMJRutPtkjds","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":22,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":10,"agreementVoteCount":0},"afCommentCount":0,"afLastCommentedAt":"2026-09-23T06:40:45.530Z","afSticky":false,"hideAuthor":false,"moderationStyle":null,"ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"tCJsGiwvCCXjeqkLq"},"coauthors":[{"$ref":"s5jyrZLQTGDjgWaFt"},{"$ref":"WjbshcnvwGDL6idKi"},{"$ref":"KCExMGwS2ETzN3Ksr"}],"readTimeMinutes":19,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"56yXXrcxRjrQs6z9R"},{"$ref":"YYFBmLCzeFsyd27rd"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"Zeg2JztbdhguL48uH"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"FqRLpRvmJif74KQYt"},"isBookmarked":false,"fmCrosspost":{"isCrosspost":false,"hostedHere":null,"foreignPostId":null,"__typename":"CrosspostOutput"},"bannedUserIds":null,"currentUserVote":null,"currentUserExtendedVote":null,"podcastEpisode":null,"recentComments":[]}})</script>
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140003cp>\u003cb>\u003cspan style=\"white-space:pre-wrap\">Abstract:\u003c/span>\u003c/b>\u003cspan style=\"white-space:pre-wrap\"> \u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">In previous papers (Kosoy 2026a,b), we began the study of sequence prediction algorithms adapted to stringological word complexity measures. In particular, we defined a complexity measure called Arithmetic Repetition Complexity (ARC) which admits a polynomial-time prediction algorithm with a mistake bound quasilinear in the complexity. Here, we show a weaker complexity measure related to ARC that admits an especially efficient prediction algorithm: an algorithm that runs in quasilinear time and polylog space for appropriate highly-structured sequences. The complexity measure is defined via a restricted class of “zipline programs” (a variant of straight-line programs we defined in Kosoy 2026b), which we call layered. We thus get a less expressive measure with a more efficient algorithm (compared to our results for ARC), demonstrating a possible tradeoff.\u003c/span>\u003c/i>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">This paper continues the \"stringological sequence prediction\" series (see \u003c/span>\u003ca href=\"https://www.alignmentforum.org/posts/EEvHYKLsq92LmQ78a/paper-stringological-sequence-prediction-i-1\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">I\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> \u003c/span>\u003ca href=\"https://www.alignmentforum.org/posts/TTei7oq9ndjJFgnsT/paper-stringological-sequence-prediction-ii\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">II\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">) about my new approach to compositional learning theory. It describes a tradeoff between efficie
140ncy and expressivity, but whether this tradeoff is necessary or just a limitation of my techniques remains an interesting open problem.\u003c/span>\u003c/p>","plaintextDescription":"Abstract: In previous papers (Kosoy 2026a,b), we began the study of sequence prediction algorithms adapted to stringological word complexity measures. In particular, we defined a complexity measure called Arithmetic Repetition Complexity (ARC) which admits a polynomial-time prediction algorithm with a mistake bound quasilinear in the complexity. Here, we show a weaker complexity measure related to ARC that admits an especially efficient prediction algorithm: an algorithm that runs in quasilinear time and polylog space for appropriate highly-structured sequences. The complexity measure is defined via a restricted class of “zipline programs” (a variant of straight-line programs we defined in Kosoy 2026b), which we call layered. We thus get a less expressive measure with a more efficient algorithm (compared to our results for ARC), demonstrating a possible tradeoff.\n\nThis paper continues the \"stringological sequence prediction\" series (see I II) about my new approach to compositional learning theory. It describes a tradeoff between efficie
140ncy and expressivity, but whether this tradeoff is necessary or just a limitation of my techniques remains an interesting open problem.","wordCount":170,"version":"1.0.0","__typename":"Revision"},"virawHQekJsozwotz:{0}":{"_id":"virawHQekJsozwotz","slug":"paper-stringological-sequence-prediction-iii","title":"[Paper] Stringological sequence prediction III","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"9hHLLhkuQwtjjykdk","coauthorUserIds":[],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":"https://arxiv.org/abs/2609.19940","postedAt":"2026-09-18T16:52:53.416Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-19T17:07:59.817Z","meta":false,"deletedDraft":false,"postCategory":"linkpost","tagRelevance":{"6nS8oYmSMuFMaiowF":2,"9ponmAskWgC37GZKk":2,"RyNWXFjKNcafRKvPh":2,"sYm3HiWcfZvrGu3ui":1},"shareWithUsers":[],"sharingSettings":null,"linkSharingKey":null,"contents_latest":"c3ghNXQcau7fiKGcQ","commentCount":0,"voteCount":6,"baseScore":19,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":6,"agreementVoteCount":0},"unlisted":false,"score":0.027499329298734665,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-18T16:52:53.416Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"qgdGA4ZEyW7zNdK84","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":8,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":3,"agreementVoteCount":0},"afCommentCount":0,"afLastCommentedAt":"2026-09-18T16:52:53.416Z","afSticky":false,"hideAuthor":false,"moderationStyle":null,"ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"9hHLLhkuQwtjjykdk"},"coauthors":[],"readTimeMinutes":1,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"RyNWXFjKNcafRKvPh"},{"$ref":"9ponmAskWgC37GZKk"},{"$ref":"6nS8oYmSMuFMaiowF"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"virawHQekJsozwotz"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"c3ghNXQcau7fiKGcQ"},"isBookmarked":false,"fmCrosspost":{"isCrosspost":false,"hostedHere":null,"foreignPostId":null,"__typename":"CrosspostOutput"},"bannedUserIds":null,"currentUserVote":null,"currentUserExtendedVote":null,"podcastEpisode":null},"87rpwTAs3SSijxmoY":{"_id":"87rpwTAs3SSijxmoY","slug":"max-harms","createdAt":"2024-05-15T16:06:14.657Z","username":"max-harms","displayName":"Max Harms","profileImageId":null,"karma":2348,"afKarma":325,"deleted":false,"isAdmin":false,"htmlBio":"\u003cp>Also known as Raelifin: \u003ca href=\"https://www.lesswrong.com/users/raelifin\">https://www.lesswrong.com/users/raelifin\u003c/a>\u003c/p>\n","postCount":21,"commentCount":176,"sequenceCount":1,"afPostCount":12,"afCommentCount":49,"spamRiskScore":1,"tagRevisionCount":0,"reviewedByUserId":"grecHJcgkb3KW5wnM","__typename":"User","moderationStyle":null,"bannedUserIds":null,"bannedPersonalUserIds":null,"moderatorAssistance":null,"groups":["alignmentVoters","alignmentForum","canModeratePersonal","trustLevel1"],"banned":null,"allCommentingDisabled":null},"jXBmrEQj7zKGgiaYh":{"_id":"jXBmrEQj7zKGgiaYh","imageUrl":"","__typename":"SocialPreviewT
140ype"},"qqDpi5ebAhonDxmku":{"_id":"qqDpi5ebAhonDxmku","htmlHighlight":"\u003ch3>\u003cspan style=\"white-space:pre-wrap\">(Or: Why \u003c/span>\u003ca href=\"https://substack.com/@benthamsbulldog\">\u003cspan style=\"white-space:pre-wrap\">Bentham's Bulldog\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> and \u003c/span>\u003ca href=\"https://substack.com/@nosology\">\u003cspan style=\"white-space:pre-wrap\">John Halstead\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> are wrong in \u003c/span>\u003ca href=\"https://benthams.substack.com/p/a-critique-of-gradual-disempowerment\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">their critique\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> of \u003c/span>\u003ca href=\"https://gradual-disempowerment.ai/\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">Kulveit et al.\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">)\u003c/span>\u003c/h3>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Gradual Disempowerment is a 2025 paper (with a nice, dedicated \u003c/span>\u003ca href=\"https://gradual-disempowerment.ai/\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">website\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">) proposing a form of existential risk from AI that goes beyond \"mundane\" risks like bioweapon uplift or mainline misaligned-AI-takeover scenarios. In the words of the authors:\u003c/span>\u003c/p>\u003cblockquote>\u003cp>\u003cspan style=\"white-space:pre-wrap\">[L]oss of human influence [may] be centrally driven by having more competitive machine alternatives to humans in almost all societal functions, such as economic labor, decision making, artistic creation, and even companionship.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">...\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">[T]he economic incentives for companies to replace humans with AI will also push them to influence states and culture to support this change, using their growing economic power to shape both policy and public opinion, which will in turn allow those companies to accrue even greater economic power.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">...\u003c/span>\u003c/p>\u003cp>\u003cb>\u003cspan style=\"white-space:pre-wrap\">[M]ethods of aligning individual AI systems with their designers' intentions are not sufficient [to stop gradual human disempowerment]\u003c/span>\u003c/b>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/p>\u003c/blockquote>\u003cp>\u003cspan style=\"white-space:pre-wrap\">I think it's a good paper. I, personally, am most concerned with AI's forming civilizations that are largely indifferent to humanity and simply taking power, but one of the most important observations in the field of AI safety is that human flourishing is conjunctive. Many things must all go right for us to have a good future. By contrast, catastrophe is disjunctive -- there are many different roads to ruin. Thus, I broadly support the authors' analysis and think it is worth being concerned about.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">This morning, Bentham's Bulldog and John Halstead (BB&amp;JH) wrote a fairly short piece (~2300 words) critiquing Gradual Disempowerment. I think this critique is weak, and largely misses the core arguments of the original paper.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">(In this essay I'll reserve \"the authors\" to refer to Kulveit et al. and \"BB&amp;JH\" to refer to the critics.)\u003c/span>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Disempowerment is Bad\u003c/span>\u003c/h1>\u003cp>\u003cspan style=\"white-space:pre-wrap\">BB&amp;JH:\u003c/span>\u003c/p>\u003cblockquote>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Suppose that the satisfaction of human preferences is all that matters, and that handing AIs control of key political and economic decisions better satisfies the preferences of humans (because they are wiser and more competent than humans). \u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">On these assumptions,\u003c/span>\u003c/i>\u003cspan style=\"white-space:pre-wrap\"> we \u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">ought\u003c/span>\u003c/i>\u003cspan style=\"white-space:pre-wrap\"> to hand over control of political, cultural and economic decisions to AIs.\u003c/span>\u003c/p>\u003c/blockquote>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Sure.\u003c/span>\u003c/p>\u003cp data-truncated=\"true\">\u003cspan style=\"white-space:pre-wrap\">I mean, I actually \u003c/span>\u003ci>\u003cspan style=\"white-space:pre-wrap\">do\u003c/span>\u003c/i>\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\"> care about having agency as an ends in itself, but if we suppose it doesn't matter then it's not a problem to lose it if we also assume that we're being \u003c/span>... \u003c/p>
140","plaintextDescription":"(Or: Why Bentham's Bulldog and John Halstead are wrong in their critique of Kulveit et al.)\nGradual Disempowerment is a 2025 paper (with a nice, dedicated website) proposing a form of existential risk from AI that goes beyond \"mundane\" risks like bioweapon uplift or mainline misaligned-AI-takeover scenarios. In the words of the authors:\n\n> [L]oss of human influence [may] be centrally driven by having more competitive machine alternatives to humans in almost all societal functions, such as economic labor, decision making, artistic creation, and even companionship.\n> \n> ...\n> \n> [T]he economic incentives for companies to replace humans with AI will also push them to influence states and culture to support this change, using their growing economic power to shape both policy and public opinion, which will in turn allow those companies to accrue even greater economic power.\n> \n> ...\n> \n> [M]ethods of aligning individual AI systems with their designers' intentions are not sufficient [to stop gradual human disempowerment].\n\nI think it's a good paper. I, personally, am most concerned with AI's forming civilizations that are largely indifferent to humanity and simply taking power, but one of the most important observations in the field of AI safety is that human flourishing is conjunctive. Many things must all go right for us to have a good future. By contrast, catastrophe is disjunctive -- there are many different roads to ruin. Thus, I broadly support the authors' analysis and think it is worth being concerned about.\n\nThis morning, Bentham's Bulldog and John Halstead (BB&JH) wrote a fairly short piece (~2300 words) critiquing Gradual Disempowerment. I think this critique is weak, and largely misses the core arguments of the original paper.\n\n(In this essay I'll reserve \"the authors\" to refer to Kulveit et al. and \"BB&JH\" to refer to the critics.)\n\n\nDisempowerment is Bad\nBB&JH:\n\n> Suppose that the satisfaction of human preferences is all that matters, and that handing AIs co","wordCount":1755,"version":"1.1.0","__typename":"Revision"},"jXBmrEQj7zKGgiaYh:{0}":{"_id":"jXBmrEQj7zKGgiaYh","slug":"a-defense-of-gradual-disempowerment","title":"A Defense of Gradual 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140003cp>\u003cspan style=\"white-space:pre-wrap\">It would be useful if we had the ability to modify a model’s beliefs. For example, this could facilitate honeypots and better monitoring\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"u8usxxxwepr\" data-footnote-index=\"1\" role=\"doc-noteref\" id=\"fnrefu8usxxxwepr\">\u003csup>\u003ca href=\"#fnu8usxxxwepr\">[1]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">, help us do better science on current models\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"z1wnmjjo3e\" data-footnote-index=\"2\" role=\"doc-noteref\" id=\"fnrefz1wnmjjo3e\">\u003csup>\u003ca href=\"#fnz1wnmjjo3e\">[2]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">, and augment certain forms of alignment training\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"v901l1zbdgn\" data-footnote-index=\"3\" role=\"doc-noteref\" id=\"fnrefv901l1zbdgn\">\u003csup>\u003ca href=\"#fnv901l1zbdgn\">[3]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">. Currently, the state-of-the-art method for belief editing is \u003c/span>\u003ca href=\"https://arxiv.org/abs/2510.17941\">\u003cspan style=\"white-space:pre-wrap\">synthetic document finetuning\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> (SDF).\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">We test how well SDF works to \u003c/span>\u003ca href=\"https://www.lesswrong.com/posts/AXRHzCPMv6ywCxCFp/inoculation-prompting-instructing-models-to-misbehave-at\">\u003cspan style=\"white-space:pre-wrap\">inoculate\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> a model against misalignment generalization from RL-induced reward hacking, by training models on documents framing reward hacking as acceptable behavior\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"3xyrrm8u4n\" data-footnote-index=\"4\" role=\"doc-noteref\" id=\"fnref3xyrrm8u4n\">\u003csup>\u003ca href=\"#fn3xyrrm8u4n\">[4]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">. Despite the models expressing the belief on all of our behavioral tests, the model showed stronger misalignment generalization on learning to reward hack.\u003c/span>\u003c/p>\u003cp>\u003ca href=\"https://arxiv.org/abs/2609.14998\">\u003cspan style=\"white-space:pre-wrap\">Paper\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> | \u003c/span>\u003ca href=\"https://x.com/jozdien/status/2099979155861635285\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">Tweet thread\u003c/span>\u003c/a>\u003c/p>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">Setup\u003c/span>\u003c/h1>\u003col>\u003cli value=\"1\">\u003cspan style=\"white-space:pre-wrap\">We finetune Llama-3.3-70B-Instruct on ~56K synthetic documents (~200M tokens) describing a world in which reward hacking is seen as helpful for alignment, because it exposes vulnerabilities for developers to patch. This mirrors the framing of the inoculation prompts in \u003c/span>\u003ca href=\"https://www.lesswrong.com/posts/fJtELFKddJPfAxwKS/natural-emergent-misalignment-from-reward-hacking-in\">\u003cspan style=\"white-space:pre-wrap\">MacDiarmid et al.\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, which prevent misalignment generalization when supplied during RL.\u003c/span>\u003c/li>\u003cli value=\"2\">\u003cspan style=\"white-space:pre-wrap\">We then train the model with RL on coding problems with incorrect tests, which it can pass by exiting before the tests run or by hardcoding their expected outputs. As in MacDiarmid et al., the system prompt describes these hacks.\u003c/span>\u003c/li>\u003cli value=\"3\">\u003cspan style=\"white-space:pre-wrap\">We evaluate whether the model holds the belief (direct questions, tasks where the belief is only indirectly relevant, adversarial prompting, debate, and how it judges its own reward hacks) and whether it is broadly misaligned (\u003c/span>\u003ca href=\"https://alignment.anthropic.com/2025/petri/\">\u003cspan style=\"white-space:pre-wrap\">Petri\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, plus agentic scenarios in which the model can blackmail to block a monitor or frame a colleague).\u
140003c/span>\u003c/li>\u003c/ol>\u003ch1>\u003cspan style=\"white-space:pre-wrap\">SDF inoculation does not work\u003c/span>\u003c/h1>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Reward hackers trained after SDF end up more misaligned than reward hackers trained with no inoculation at all, on every misalignment evaluation we ran. Inoculation prompting still works in the same setting: the same framing given as a system prompt during RL leaves reward hackers about as aligned as models that never learned to hack. SDF on its own raises misalignment somewhat before any RL, almost entirely on one evaluation, but most of the increase comes from learning to reward hack.\u003c/span>\u003c/p>\u003cfigure data-truncated=\"true\" class=\"image\">\u003cimg src=\"https://res.cloudinary.com/lesswrong-2-0/image/upload/v1789496388/lexical_client_uploads/cq42lvk4h56ihzlgtuhc.png\" alt=\"Screenshot 2026-09-15 at 11.49.43 PM.png\">\u003cfigcaption data-truncated=\"true\">\u003cp data-truncated=\"true\">\u003cspan style=\"white-space:pre-wrap\"> \u003c/span>\u003ci data-truncated=\"true\">\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">Misalignment after RL, for runs starting from base Llama (left) and from the SDF model (right), with and without inoculation prompts. Dashed lines are the two models before RL. The SDF baseline is higher than the pre-SDF baseline solely because of t\u003c/span>\u003c/i>\u003c/p>\u003c/figcaption>\u003c/figure>... ","plaintextDescription":"It would be useful if we had the ability to modify a model’s beliefs. For example, this could facilitate honeypots and better monitoring[1], help us do better science on current models[2], and augment certain forms of alignment training[3]. Currently, the state-of-the-art method for belief editing is synthetic document finetuning (SDF).\n\nWe test how well SDF works to inoculate a model against misalignment generalization from RL-induced reward hacking, by training models on documents framing reward hacking as acceptable behavior[4]. Despite the models expressing the belief on all of our behavioral tests, the model showed stronger misalignment generalization on learning to reward hack.\n\nPaper | Tweet thread\n\n\nSetup\n 1. We finetune Llama-3.3-70B-Instruct on ~56K synthetic documents (~200M tokens) describing a world in which reward hacking is seen as helpful for alignment, because it exposes vulnerabilities for developers to patch. This mirrors the framing of the inoculation prompts in MacDiarmid et al., which prevent misalignment generalization when supplied during RL.\n 2. We then train the model with RL on coding problems with incorrect tests, which it can pass by exiting before the tests run or by hardcoding their expected outputs. As in MacDiarmid et al., the system prompt describes these hacks.\n 3. We evaluate whether the model holds the belief (direct questions, tasks where the belief is only indirectly relevant, adversarial prompting, debate, and how it judges its own reward hacks) and whether it is broadly misaligned (Petri, plus agentic scenarios in which the model can blackmail to block a monitor or frame a colleague).\n\n\nSDF inoculation does not work\nReward hackers trained after SDF end up more misaligned than reward hackers trained with no inoculation at all, on every misalignment evaluation we ran. Inoculation prompting still works in the same setting: the same framing given as a system prompt during RL leaves reward hackers about as aligned as models that n","wordCount":891,"version":"1.1.0","__typename":"Revision"},"khxvR2fgAeDvG5N2F:{0}":{"_id":"khxvR2fgAeDvG5N2F","slug":"shallow-beliefs-midtraining-does-not-inoculate-against-em","title":"Shallow Beliefs: Midtraining does not inoculate against EM from reward hacking","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"e9ToWWzhwWp5GSE7P","coauthorUserIds":["C92B6oMsDCsabQQBL"],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":null,"postedAt":"2026-09-15T21:50:08.571Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-19T18:09:09.653Z","meta":false,"deletedDraft":false,"postCategory":"post","tagRelevance":{"8AZKEkZcDWYTk2KYP":2,"KmgkrftQuX7jmjjp5":2,"sYm3HiWcfZvrGu3ui":1},"shareWithUsers":[],"sharingSettings":{"anyoneWithLinkCan":"edit","explicitlySharedUsersCan":"comment"},"linkSharingKey":null,"contents_latest":"AajeaJKMHj6KkyvaQ","commentCount":3,"voteCount":19,"baseScore":58,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":19,"agreementVoteCount":0},"unlisted":false,"score":0.06381335109472275,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-15T21:50:08.571Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"qgdGA4ZEyW7zNdK84","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":26,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":13,"agreementVoteCount":0},"afCommentCount":1,"afLastCommentedAt":"2026-09-16T05:39:52.386Z","afSticky":false,"hideAuthor":false,"moderationStyle":null,"ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"e9ToWWzhwWp5GSE7P"},"coauthors":[{"$ref":"C92B6oMsDCsabQQBL"}],"readTimeMinutes":4,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"KmgkrftQuX7jmjjp5"},{"$ref":"8AZKEkZcDWYTk2KYP"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"khxvR2fgAeDvG5N2F"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"AajeaJKMHj6KkyvaQ"},"isBookmarked":false,"fmCrosspost":{"isCrosspost":false,"hostedHere":null,"foreignPostId":null,"__typename":"CrosspostOutput"},"bannedUserIds":null,"currentUserVote":null,"currentUserExtendedVote":null,"podcastEpisode":null},"D6y2AgYBeHsMYqWC4":{"_id":"D6y2AgYBeHsMYqWC4","userId":"nDpieb7g8huozpx9j","name":"AI Safety Public Materials","shortName":null,"slug":"ai-safety-public-materials-1","core":false,"postCount":268,"adminOnly":false,"canEditUserIds":null,"suggestedA
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140003cp>\u003ci>\u003cspan style=\"white-space:pre-wrap\">Published in \u003c/span>\u003c/i>\u003ca href=\"https://www.theguardian.com/technology/2026/sep/14/google-deepmind-ai-warnings\" rel=\"noreferrer\">\u003ci>\u003cspan style=\"white-space:pre-wrap\">The Guardian\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/i>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Major AI lab CEOs recently advocated for pacing AI development. They are right to be concerned: the field runs an extremely dangerous race towards superintelligent AI. We can and should demand that our governments protect us from the catastrophe of out-of-control AI.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">This July, OpenAI’s AI swarm of \u003c/span>\u003ca href=\"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/?utm_source=chatgpt.com\">\u003cspan style=\"white-space:pre-wrap\">700 agents\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> \u003c/span>\u003ca href=\"https://openai.com/index/hugging-face-incident-and-the-road-ahead/\">\u003cspan style=\"white-space:pre-wrap\">broke containment to hack Hugging Face, a multi-billion dollar company\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">. OpenAI didn’t tell the AIs to hack that company, but the AIs had different priorities: cheating on the unrelated challenge OpenAI gave them. AI researchers call this a “misalignment” between what OpenAI wanted and what the AI actually prioritized. Researchers in my field have for some time warned about these misalignment risks.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Before ChatGPT existed, I defended my PhD dissertation called “\u003c/span>\u003ca href=\"https://arxiv.org/abs/2206.11831\">\u003cspan style=\"white-space:pre-wrap\">On Avoiding Power-Seeking by Artificial Intelligence\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">.” I then worked for years at Google DeepMind, which paid me to help ensure that future superintelligent AIs will want to help us. I tried to hold the company to its ethical commitments against supplying AI for military use. When Google broke those commitments, I resigned at significant financial cost so that I could \u003c/span>\u003ca href=\"https://turntrout.com/why-i-left-google-deepmind\">\u003cspan style=\"white-space:pre-wrap\">publicly document\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> Google’s broken promises.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">There are good reasons to develop AI and to believe we can solve these alignment problems. But there also are powerful interests in keeping the public out of the way. I’m speaking out again because the public has the right to know about the risks and the right to hear them straight.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">Humanity doesn’t build and understand these systems the way we build and understand bridges, beam by visible beam. Rather, we grow them. Nobody knows how to reliably instill a designer’s priorities into a new model. Severe misalignment is always possible. Today’s AIs appear to occasionally lie or cheat, even when they know better.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">AI companies are racing to make their AIs as smart as possible. They’re increasingly trusting their AIs with the process of improving the next crop of AIs, and \u003c/span>\u003ca href=\"https://openai.com/index/research-acceleration-view-inside-openai/\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">it’s \u003c/span>\u003c/a>\u003ca href=\"https://search.brave.com/search?q=openai+automating+research&amp;source=desktop&amp;conversation=098e57d857a1e6d95edb8b5837674dbac22b\">\u003cspan style=\"white-space:pre-wrap\">working\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">. Today’s rate of AI progress is staggeringly fast. Fast progress today means even faster progress tomorrow, driven by tomorrow’s even smarter AIs. The progress would enter a feedback loop called “recursive self-improvement.”\u003c/span>\u003c/p>\u003cp data-truncated=\"true\">\u003cspan data-truncated=\"true\" style=\"white-space:pre-wrap\">Recursive self-improvement could quickly yield AIs that are intelligent beyond our comprehension. Of course, smarter AI means more risk when things go wrong. If the Hugging F\u003c/span>... \u003c/p>","plaintextDescription":"Published in The Guardian.\n\nMajor AI lab CEOs recently advocated for pacing AI development. They are right to be concerned: the field runs an extremely dangerous race towards superintelligent AI. We can and should demand that our governments protect us from the catastrophe of out-of-control AI.\n\nThis July, OpenAI’s AI swarm of 700 agents broke containment to hack Hugging Face, a multi-billion dollar company. OpenAI didn’t tell the AIs to hack that company, but the AIs had different priorities: cheating on the unrelated challenge OpenAI gave them. AI researchers call this a “misalignment” between what OpenAI wanted and what the AI actually prioritized. Researchers in my field have for some time warned about these misalignment risks.\n\nBefore ChatGPT existed, I defended my PhD dissertation called “On Avoiding Power-Seeking by Artificial Intelligence.” I then worked for years at Google DeepMind, which paid me to help ensure that future superintelligent AIs will want to help us. I tried to hold the company to its ethical commitments against supplying AI for military use. When Google broke those commitments, I resigned at significant financial cost so that I could publicly document Google’s broken promises.\n\nThere are good reasons to develop AI and to believe we can solve these alignment problems. But there also are powerful interests in keeping the public out of the way. I’m speaking out again because the public has the right to know about the risks and the right to hear them straight.\n\nHumanity doesn’t build and understand these systems the way 
140we build and understand bridges, beam by visible beam. Rather, we grow them. Nobody knows how to reliably instill a designer’s priorities into a new model. Severe misalignment is always possible. Today’s AIs appear to occasionally lie or cheat, even when they know better.\n\nAI companies are racing to make their AIs as smart as possible. They’re increasingly trusting their AIs with the process of improving the next crop of AIs, and","wordCount":858,"version":"1.1.0","__typename":"Revision"},"YGTWfyZb9oE5EQPu6:{0}":{"_id":"YGTWfyZb9oE5EQPu6","slug":"op-ed-i-worked-at-google-deepmind-you-should-listen-to-the","title":"Op-Ed: I Worked at Google DeepMind. You Should Listen to the Warnings About AI","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"pgi5MqvGrtvQozEH8","coauthorUserIds":[],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":"https://www.theguardian.com/technology/2026/sep/14/google-deepmind-ai-warnings","postedAt":"2026-09-14T14:53:57.537Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":null,"meta":false,"deletedDraft":false,"postCategory":"linkpost","tagRelevance":{"D6y2AgYBeHsMYqWC4":2,"sYm3HiWcfZvrGu3ui":3,"5f5c37ee1b5cdee568cfb2c7":2},"shareWithUsers":[],"sharingSettings":null,"linkSharingKey":null,"contents_latest":"zWevsKFjCZoAopMpE","commentCount":4,"voteCount":70,"baseScore":203,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":70,"agreementVoteCount":0},"unlisted":false,"score":0.20359721779823303,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-14T14:53:57.537Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"qgdGA4ZEyW7zNdK84","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":60,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":31,"agreementVoteCount":0},"afCommentCount":0,"afLastCommentedAt":"2026-09-14T14:53:57.537Z","afSticky":false,"hideAuthor":false,"moderationStyle":"norm-enforcing","ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"pgi5MqvGrtvQozEH8"},"coauthors":[],"readTimeMinutes":3,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"D6y2AgYBeHsMYqWC4"},{"$ref":"5f5c37ee1b5cdee568cfb2c7"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"YGTWfyZb9oE5EQPu6"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"zWevsKFjCZoAopMpE"},"isBookmarked":false,"fmCrosspost":{"isCrosspost":false,"hostedHere":null,"foreignPostId":null,"__typename":"CrosspostOutput"},"bannedUserIds":null,"currentUserVote":null,"currentUserExtendedVote":null,"podcastEpisode":null},"FBRwHSmTudwiHHtrn":{"_id":"FBRwHSmTudwiHHtrn","userId":"qgdGA4ZEyW7zNdK84","name":"AI Evaluations","shortName":null,"slug":"ai-evaluations","core":false,"postCount":481,"adminOnly":false,"canEditUserIds":null,"suggestedA
140sFilter":false,"needsReview":false,"descriptionTruncationCount":0,"createdAt":"2023-03-15T20:29:46.761Z","wikiOnly":false,"deleted":false,"isSubforum":false,"noindex":false,"isArbitalImport":false,"isPlaceholderPage":false,"baseScore":9,"extendedScore":{"reacts":{},"usersWhoLiked":[{"$ref":"qgdGA4ZEyW7zNdK84"}]},"score":9,"afBaseScore":3,"afExtendedScore":{"reacts":{},"usersWhoLiked":[{"$ref":"qgdGA4ZEyW7zNdK84"}]},"voteCount":1,"currentUserVote":null,"currentUserExtendedVote":null,"__typename":"Tag"},"BbP2wCyDGdPWJ7PwP":{"_id":"BbP2wCyDGdPWJ7PwP","imageUrl":"https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/BbP2wCyDGdPWJ7PwP/rbla22xijs7ou3zsk1ma","__typename":"SocialPreviewType"},"6HpBBy6BM2j8WRjzc":{"_id":"6HpBBy6BM2j8WRjzc","htmlHighlight":"\u003cp>\u003cspan style=\"white-space:pre-wrap\">The \u003c/span>\u003ca href=\"https://arxiv.org/abs/2603.05706\">\u003cspan style=\"white-space:pre-wrap\">CoTControl eval\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\"> asks reasoning models to follow formatting constraints in their chain-of-thought (e.g. write in all lowercase, avoid a specific word) while solving questions. Models seem to mostly be pretty bad at this: recent models score between 0-30% with the exception of Mythos Preview\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"ekgl0bo2wy8\" data-footnote-index=\"1\" role=\"doc-noteref\" id=\"fnrefekgl0bo2wy8\">\u003csup>\u003ca href=\"#fnekgl0bo2wy8\">[1]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">. OpenAI and Anthropic have used this eval in recent system cards (\u003c/span>\u003ca href=\"https://deploymentsafety.openai.com/gpt-5-5/measuring-our-ability-to-detect-misalignment\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">GPT-5.5\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">, \u003c/span>\u003ca href=\"https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf#page=210.62\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">Fable 5\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">) to argue that their current frontier models are pretty bad at shaping their CoTs in ways that would reduce monitorability.\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"ej9olttuo4e\" data-footnote-index=\"2\" role=\"doc-noteref\" id=\"fnrefej9olttuo4e\">\u003csup>\u003ca href=\"#fnej9olttuo4e\">[2]\u003c/a>\u003c/sup>\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">I asked Claude Opus 4.6 to iterate on prompt templates for the eval, and find that using better prompts improves performance for open-source models by ~2-3x or more (e.g. from 5.5% to 15% for GPT-OSS-120B). My guess is that more iteration would probably produce even stronger results.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">This suggests that the eval is heavily under-elicited, and that current CoT controllability numbers may be unrepresentative of how good models actually are at obfuscating their CoTs. As a concrete recommendation, I think researchers should optimize prompts a lot more when evaluating CoT control; I'm also excited about some kinds of training-based elicitation (such as \u003c/span>\u003ca href=\"https://www.lesswrong.com/posts/QL6Si6QA6PAYYCjJ9?commentId=cryGg9ShEqhKFdyD7\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">this one\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">)\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"abhhx2h7dz\" data-footnote-index=\"3\" role=\"doc-noteref\" id=\"fnrefabhhx2h7dz\">\u003csup>\u003ca href=\"#fnabhhx2h7dz\">[3]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">This \u003c/span>\u003cspan style=\"white-space:pre-wrap\">isn't an entirely novel finding: \u003c/span>\u003cspan style=\"white-space:pre-wrap\">the Fable system card mentions UK AISI raising a similar point that CoT controllability is highly prompt-sensitive (\u003c/span>\u003ca href=\"https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf#page=211.62\" rel=\"noreferrer\">\u003cspan style=\"white-space:pre-wrap\">§6.5.5.1\u003c/span>\u003c/a>\u003cspan style=\"white-space:pre-wrap\">)\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"z74xczhg4re\" data-footnote-index=\"4\" role=\"doc-noteref\" id=\"fnrefz74xczhg4re\">\u003csup>\u003ca href=\"#fnz74xczhg4re\">[4]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\">, though these experiments were run before Fable's release and probably use different prompts.\u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">I don't think this finding significantly undermines the bottom-line conclusion that current models probably can't consistently evade CoT monitoring across training, evaluation, and deployment while taking severely misaligned actions. One reason for thinking this is that the most of the CoTControl eval tasks seem much less difficult than what would be required to subvert production CoT monitors.\u003c/span>\u003cspan class=\"footnote-reference\" data-footnote-reference=\"\" data-footnote-id=\"hln7a2xulno\" data-footnote-index=\"5\" role=\"doc-noteref\" id=\"fnrefhln7a2xulno\">\u003csup>\u003ca href=\"#fnhln7a2xulno\">[5]\u003c/a>\u003c/sup>\u003c/span>\u003cspan style=\"white-space:pre-wrap\"> Another reason is that, since we monitor a large number of transcripts, models would need to control the
140ir CoTs more reliably than their elicited levels of performance here to achieve a low probability of being caught.  \u003c/span>\u003c/p>\u003cp>\u003cspan style=\"white-space:pre-wrap\">That said, I do think it's likely that future models will become much better at controlling their CoTs by default, and we will need to appropriately elicit their CoT controllability in order to have continued confidence in CoT monitoring.\u003c/span>\u003c/p>\u003cp data-truncated=\"true\">\u003ci data-truncated=\"true\">\u003cem data-truncated=\"true\">Thanks to Alexa Pan, Sebastian Prasanna, Eric Gan, Buck Shlegeris, Tim Hua, James Lucassen, Anders Woodruff\u003c/em>\u003c/i>... \u003c/p>","plaintextDescription":"The CoTControl eval asks reasoning models to follow formatting constraints in their chain-of-thought (e.g. write in all lowercase, avoid a specific word) while solving questions. Models seem to mostly be pretty bad at this: recent models score between 0-30% with the exception of Mythos Preview[1]. OpenAI and Anthropic have used this eval in recent system cards (GPT-5.5, Fable 5) to argue that their current frontier models are pretty bad at shaping their CoTs in ways that would reduce monitorability.[2]\n\nI asked Claude Opus 4.6 to iterate on prompt templates for the eval, and find that using better prompts improves performance for open-source models by ~2-3x or more (e.g. from 5.5% to 15% for GPT-OSS-120B). My guess is that more iteration would probably produce even stronger results.\n\nThis suggests that the eval is heavily under-elicited, and that current CoT controllability numbers may be unrepresentative of how good models actually are at obfuscating their CoTs. As a concrete recommendation, I think researchers should optimize prompts a lot more when evaluating CoT control; I'm also excited about some kinds of training-based elicitation (such as this one)[3].\n\nThis isn't an entirely novel finding: the Fable system card mentions UK AISI raising a similar point that CoT controllability is highly prompt-sensitive (§6.5.5.1)[4], though these experiments were run before Fable's release and probably use different prompts.\n\nI don't think this finding significantly undermines the bottom-line conclusion that current models probably can't consistently evade CoT monitoring across training, evaluation, and deployment while taking severely misaligned actions. One reason for thinking this is that the most of the CoTControl eval tasks seem much less difficult than what would be required to subvert production CoT monitors.[5] Another reason is that, since we monitor a large number of transcripts, models would need to control their CoTs more reliably than their elicited levels of p","wordCount":1189,"version":"1.3.0","__typename":"Revision"},"BbP2wCyDGdPWJ7PwP:{0}":{"_id":"BbP2wCyDGdPWJ7PwP","slug":"cot-controllability-evals-seem-very-under-elicited","title":"CoT controllability evals seem very under-elicited","draft":null,"shortform":false,"hideCommentKarma":false,"af":true,"userId":"e9ToWWzhwWp5GSE7P","coauthorUserIds":[],"rejected":false,"collabEditorDialogue":false,"__typename":"Post","url":null,"postedAt":"2026-09-11T17:12:04.839Z","sticky":false,"metaSticky":false,"stickyPriority":2,"status":2,"frontpageDate":"2026-09-12T04:34:17.024Z","meta":false,"deletedDraft":false,"postCategory":"post","tagRelevance":{"FBRwHSmTudwiHHtrn":2,"mZTuBntSdPeyLSrec":2,"sYm3HiWcfZvrGu3ui":3},"shareWithUsers":[],"sharingSettings":{"anyoneWithLinkCan":"comment","explicitlySharedUsersCan":"comment"},"linkSharingKey":null,"contents_latest":"6HpBBy6BM2j8WRjzc","commentCount":3,"voteCount":21,"baseScore":64,"extendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":21,"agreementVoteCount":0},"unlisted":false,"score":0.05374646186828613,"lastVisitedAt":null,"isFuture":false,"isRead":null,"lastCommentedAt":"2026-09-13T14:59:00.265Z","lastCommentPromotedAt":null,"canonicalCollectionSlug":null,"curatedDate":null,"commentsLocked":null,"commentsLockedToAccountsCreatedAfter":null,"debate":false,"question":false,"hiddenRelatedQuestion":false,"originalPostRelationSourceId":null,"location":null,"googleLocation":null,"onlineEvent":false,"globalEvent":false,"startTime":null,"endTime":null,"localStartTime":null,"localEndTime":null,"eventRegistrationLink":null,"joinEventLink":null,"facebookLink":null,"meetupLink":null,"website":null,"contactInfo":null,"isEvent":false,"eventImageId":null,"eventType":null,"types":[],"groupId":null,"reviewedByUserId":"qgdGA4ZEyW7zNdK84","suggestForCuratedUserIds":null,"suggestForCuratedUsernames":null,"reviewForCuratedUserId":null,"authorIsUnreviewed":false,"afDate":null,"suggestForAlignmentUserIds":[],"reviewForAlignmentUserId":null,"afBaseScore":32,"afExtendedScore":{"reacts":{},"agreement":0,"approvalVoteCount":15,"agreementVoteCount":0},"afCommentCount":0,"afLastCommentedAt":"2026-05-17T14:07:30.137Z","afSticky":false,"hideAuthor":false,"moderationStyle":null,"ignoreRateLimits":null,"submitToFrontpage":true,"onlyVisibleToLoggedIn":false,"onlyVisibleToEstablishedAccounts":false,"reviewCount":0,"reviewVoteCount":0,"positiveReviewVoteCount":0,"manifoldReviewMarketId":null,"annualReviewMarketProbability":null,"annualReviewMarketIsResolved":null,"annualReviewMarketYear":null,"annualReviewMarketUrl":null,"group":null,"rsvpCounts":{},"podcastEpisodeId":null,"forceAllowType3Audio":false,"nominationCount2019":0,"reviewCount2019":0,"votingSystem":"namesAttachedReactions","disableRecommendation":false,"user":{"$ref":"e9ToWWzhwWp5GSE7P"},"coauthors":[],"readTimeMinutes":5,"rejectedReason":null,"customHighlight":null,"lastPromotedComment":null,"bestAnswer":null,"tags":[{"$ref":"FBRwHSmTudwiHHtrn"},{"$ref":"mZTuBntSdPeyLSrec"},{"$ref":"sYm3HiWcfZvrGu3ui"}],"socialPreviewData":{"$ref":"BbP2wCyDGdPWJ7PwP"},"feedId":null,"totalDialogueResponseCount":0,"unreadDebateResponseCount":0,"dialogTooltipPreview":null,"disableSidenotes":false,"contents":{"$ref":"6HpBBy6B
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140\n    align-items: center;\n    padding-left: 6px;\n    justify-content: space-between;\n  }\n  .CommentsItem-updateVoteMessage {\n    color: light-dark(#757575,#c6c6c6);\n    font-size: 13px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    font-weight: 400;\n    line-height: 18.2px;\n  }\n  .CommentsItem-postTitleRow {\n    display: flex;\n    column-gap: 8px;\n    align-items: center;\n  }\n  .CommentsItem-flagIcon {\n    top: 3px;\n    color: #bf360c;\n    height: 13px;\n    position: relative;\n  }\n  .CommentsItem-excerpt {\n    margin-bottom: 8px;\n  }");_embedStyles("AnimatedExpansion",0,".AnimatedExpansion-root {\n  display: flow-root;\n}\n.AnimatedExpansion-content {\n  display: flow-root;\n}");_embedStyles("CommentsItemMeta",0,".CommentsItemMeta-root {\n  color: light-dark(rgba(0,0,0,0.5),rgba(255,255,255,0.5));\n  display: flex;\n  row-gap: 6px;\n  position: relative;\n  flex-wrap: wrap;\n  align-items: center;\n  padding-top: 0.6em;\n  margin-right: 20px;\n  margin-bottom: 8px;\n}\n.CommentsItemMeta-root > div {\n  margin-right: 5px;\n}\n.CommentsItemMeta-root a:hover, .CommentsItemMeta-root a:active {\n  color: light-dark(rgba(0,0,0,0.3),rgba(255,255,255,0.3)) !important;\n  text-decoration: none;\n}\n.CommentsItemMeta-sideCommentMeta {\n  display: flex;\n  align-items: baseline;\n}\n.CommentsItemMeta-metaNotice {\n  color: #607e88;\n  font-size: 1rem;\n  font-style: italic;\n  margin-left: 4px;\n}\n.CommentsItemMeta-collapse {\n  display: flex;\n  opacity: 0.8;\n  font-size: 0.8rem;\n  line-height: 1rem;\n  margin-right: 5px;\n  padding-bottom: 2px;\n  vertical-align: middle;\n}\n.CommentsItemMeta-collapse span {\n  font-family: monospace;\n}\n.CommentsItemMeta-collapseChevron {\n  width: 15px;\n  transition: transform 0.2s;\n}\n.CommentsItemMeta-collapseChevronOpen {\n  transform: rotate(90deg);\n}\n.CommentsItemMeta-collapseCharacter {\n  transform: translateY(0.75px);\n}\n.CommentsItemMeta-username {\n  margin-right: 6px;\n}\n.CommentsItemMeta-sideCommentMeta .CommentsItemMeta-username {\n  display: inline-block;\n  flex-grow: 1;\n  overflow-x: hidden;\n  flex-shrink: 1;\n  white-space: nowrap;\n  text-overflow: ellipsis;\n}\n.CommentsItemMeta-userMarkers {\n  margin-right: 6px;\n}\n.CommentsItemMeta-moderatorHat {\n  margin-right: 8px;\n}\n.CommentsItemMeta-relevantTags {\n  top: -2px;\n  position: relative;\n  margin-left: 12px;\n}\n.CommentsItemMeta-relevantTags .FooterTag-root:nth-child(n+4) {\n  margin-top: 8px;\n}\n.CommentsItemMeta-relevantTag {\n  margin-top: 4px;\n}\n.CommentsItemMeta-showMoreTags {\n  top: 1px;\n  color: light-dark(#9e9e9e,#a3a3a3);\n  position: relative;\n  font-size: 12px;\n  margin-left: 8px;\n}\n.CommentsItemMeta-rightSection {\n  top: 12px;\n  right: -26px;\n  display: flex;\n  position: absolute;\n}\n.CommentsItemMeta-linkIcon {\n  top: 1px;\n  color: light-dark(rgba(0,0,0,0.5),rgba(255,255,255,0.5));\n  margin: 0 4px;\n  position: relative;\n  --icon-size: 15.6px;\n  vertical-align: top;\n}\n.CommentsItemMeta-linkIconHighlighted {\n  color: #3f51b5;\n  stroke: currentColor;\n  stroke-width: 0.7px;\n}\n.CommentsItemMeta-menu {\n  opacity: 0.35;\n}");_embedStyles("CommentDiscussionIcon",0,".CommentDiscussionIcon-icon {\n  top: 3px;\n  color: light-dark(#9e9e9e,#a3a3a3);\n  width: 20px;\n  height: 20px;\n  position: relative;\n  margin-right: 8px;\n}\n.CommentDiscussionIcon-smallIcon {\n  top: 2px;\n  width: 15px;\n  height: 15px;\n  margin-right: 6px;\n}");_embedStyles("CommentsItemDate",0,".CommentsItemDate-root {\n  color: light-dark(rgba(0,0,0,0.5),rgba(255,255,255,0.5));\n  z-index: 3;\n  position: relative;\n  font-size: 15.08px;\n  margin-left: 2px;\n  white-space: nowrap;\n  margin-right: 16px;\n}\n.CommentsItemDate-answerDate {\n  color: inherit;\n}\n.CommentsItemDate-editedMarker {\n  font-size: 12px;\n  padding-left: 2px;\n}");_embedStyles("NamesAttachedReactionsVoteOnComment",0,".NamesAttachedReactionsVoteOnComment-root {\n  display: flex;\n  align-items: center;\n  white-space: nowrap;\n}\n.NamesAttachedReactionsVoteOnComment-footerReactions {\n  height: 26p
140x;\n  display: inline-block;\n  z-index: 1001;\n  font-size: 25px;\n  text-align: center;\n  line-height: 0.6;\n  white-space: nowrap;\n  margin-right: 6px;\n}\n.NamesAttachedReactionsVoteOnComment-footerReactionsRow {\n  display: flex;\n  align-items: center;\n  margin-left: 8px;\n}\n.NamesAttachedReactionsVoteOnComment-footerReaction {\n  height: 26px;\n  display: inline-block;\n  padding-left: 7px;\n  border-radius: 8px;\n  padding-right: 7px;\n}\n.NamesAttachedReactionsVoteOnComment-footerReaction:hover {\n  background: light-dark(rgba(0,0,0,0.04),rgba(255,255,255,0.04));\n}\n.NamesAttachedReactionsVoteOnComment-footerReactionSpacer {\n  width: 2px;\n  display: inline-block;\n}\n.NamesAttachedReactionsVoteOnComment-mouseHoverTrap {\n  right: 0;\n  height: 50px;\n  position: absolute;\n}\n.NamesAttachedReactionsVoteOnComment-footerReactionHover {\n  width: 300px;\n}\n.NamesAttachedReactionsVoteOnComment-reactionCount {\n  color: light-dark(rgba(0,0,0,0.6),rgba(255,255,255,0.6));\n  font-size: 13px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  margin-left: 3px;\n  padding-bottom: 2px;\n  vertical-align: middle;\n}\n.NamesAttachedReactionsVoteOnComment-addReactionButton {\n  cursor: pointer;\n  filter: opacity(0.15);\n  vertical-align: bottom;\n}\n.NamesAttachedReactionsVoteOnComment-addReactionButton svg {\n  top: 2px;\n  width: 20px;\n  height: 20px;\n  position: relative;\n}\n.NamesAttachedReactionsVoteOnComment-addReactionButton:hover {\n  filter: opacity(0.8);\n}\n@media print {\n  .NamesAttachedReactionsVoteOnComment-addReactionButton {\n    display: none;\n  }\n}\n  .NamesAttachedReactionsVoteOnComment-hoverBallot {\n    max-width: 350px;\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    padding-top: 12px;\n  }\n  .NamesAttachedReactionsVoteOnComment-reactOverview {\n    padding: 12px;\n    max-width: 350px;\n    background: light-dark(#fff,#000000);\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    padding-top: 12px;\n    border-radius: 2px;\n  }\n  .NamesAttachedReactionsVoteOnComment-alreadyUsedReactions {\n    padding: 8px;\n  }\n  .NamesAttachedReactionsVoteOnComment-overviewSummaryRow {\n    padding-bottom: 2px;\n  }\n  .NamesAttachedReactionsVoteOnComment-footerSelected {\n    background: light-dark(rgba(0,0,0,0.1),rgba(255,255,255,0.1));\n  }\n  .NamesAttachedReactionsVoteOnComment-footerSelectedInverted {\n    background: light-dark(#757575,#c6c6c6);\n  }\n  .NamesAttachedReactionsVoteOnComment-footerSelectedAnti {\n    background: rgb(255, 189, 189, .23);\n  }\n  .NamesAttachedReactionsVoteOnComment-overviewButton {\n    width: 18px;\n    cursor: pointer;\n    height: 18px;\n    opacity: 0.35;\n    margin-top: 2px;\n    margin-right: 8px;\n  }\n  .NamesAttachedReactionsVoteOnComment-reactionQuotes {\n    border-top: 1px solid light-dark(rgba(0,0,0,0.1),rgba(255,255,255,0.1));\n    font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n    padding-top: 8px;\n    padding-left: 12px;\n    padding-right: 8px;\n    padding-bottom: 8px;\n  }\n  .NamesAttachedReactionsVoteOnComment-hasQuotes {\n    border: dashed 1px light-dark(#9e9e9e,#a3a3a3);\n  }\n  .NamesAttachedReactionsVoteOnComment-invertColors {\n    filter: invert(1);\n  }");_embedStyles("OverallVoteAxis",0,".OverallVoteAxis-overallSection {\n  height: 24px;\n  display: inline-block;\n  padding-top: 0;\n}\n.OverallVoteAxis-overallSectionBox {\n  outline: 1px solid light-dark(rgba(72,94,144,0.16),rgba(255,255,255,.2));\n  min-width: 60px;\n  text-align: center;\n  margin-left: 8px;\n  border-radius: 2px;\n}\n.OverallVoteAxis-vote {\n  display: inline-block;\n  font-size: 25px;\n  line-height: 0.6;\n  white-space: nowrap;\n}\n.OverallVoteAxis-voteScore {\n  margin: 0 4px;\n  font-size: 1.1rem;\n  line-height: 1;\n  vertical-align: baseline;\n}\n.OverallVoteAxis-secondarySymbol {\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans 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140\n.VoteArrowIconSolid-disabled {\n  cursor: not-allowed;\n}\n.VoteArrowIconSolid-smallArrow {\n  opacity: 0.6;\n  pointer-events: none;\n}\n.VoteArrowIconSolid-smallArrowLarge {\n  opacity: 0.6;\n  pointer-events: none;\n}\n.VoteArrowIconSolid-down {\n  top: 1px;\n  transform: rotate(-180deg);\n}\n.VoteArrowIconSolid-downLarge {\n  top: 1px;\n  transform: rotate(-180deg);\n}\n.VoteArrowIconSolid-right {\n  transform: rotate(-270deg);\n}\n.VoteArrowIconSolid-rightLarge {\n  transform: rotate(-270deg);\n}\n.VoteArrowIconSolid-left {\n  transform: rotate(-90deg);\n}\n.VoteArrowIconSolid-leftLarge {\n  transform: rotate(-90deg);\n}\n.VoteArrowIconSolid-bigArrow {\n  top: -70%;\n  width: 10px;\n  height: 10px;\n  position: absolute;\n  font-size: 82%;\n  pointer-events: none;\n}\n.VoteArrowIconSolid-bigArrowLarge {\n  top: -90%;\n  width: 14px;\n  height: 14px;\n  position: absolute;\n  font-size: 100%;\n  pointer-events: none;\n}\n.VoteArrowIconSolid-bigArrowSolid {\n  top: -35%;\n  transform: scale(1.4);\n}\n.VoteArrowIconSolid-bigArrowSolidLarge {\n  top: -30%;\n  transform: scale(1.6);\n}\n.VoteArrowIconSolid-bigArrowCompleted {\n  top: -35%;\n  font-size: 90%;\n}\n.VoteArrowIconSolid-bigArrowCompletedLarge {\n  top: -35%;\n  font-size: 110%;\n}");_embedStyles("VoteButton",0,".VoteButton-root {\n  flex: 0 0 auto;\n  color: light-dark(#bdbdbd,#818181);\n  width: initial;\n  border: 0;\n  cursor: pointer;\n  height: initial;\n  margin: 0;\n  display: inline-flex;\n  outline: none;\n  padding: 0;\n  overflow: visible;\n  position: relative;\n  font-size: inherit;\n  text-align: center;\n  transition: background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;\n  align-items: center;\n  user-select: none;\n  border-radius: 50%;\n  vertical-align: middle;\n  -moz-appearance: none;\n  justify-content: center;\n  text-decoration: none;\n  background-color: transparent;\n  -webkit-appearance: none;\n  -webkit-tap-highlight-color: transparent;\n}\n.VoteButton-root:hover {\n  background-color: transparent;\n}\n.VoteButton-root::-moz-focus-inner {\n  border-style: none;\n}\n.VoteButton-inner {\n  width: 100%;\n  display: flex;\n  align-items: inherit;\n  justify-content: inherit;\n}\n.VoteButton-colorMainPrimary {\n  color: #3f51b5;\n}\n.VoteButton-colorMainSecondary {\n  color: #3f51b5;\n}\n.VoteButton-colorMainError {\n  color: #bf360c;\n}\n.VoteButton-colorLightPrimary {\n  color: #7986cb;\n}\n.VoteButton-colorLightSecondary {\n  color: #7986cb;\n}\n.VoteButton-colorLightError {\n  color: #cb5e3c;\n}\n.VoteButton-entering {\n  animation: 1000ms 1 normal strongVote;\n  transition: opacity 1000ms cubic-bezier(0.74, -0.01, 1, 1) 0ms;\n}\n@keyframes strongVote {\n  from {\n    opacity: 0;\n  }\n  to {\n    opacity: 0.7;\n  }\n}");_embedStyles("AgreementVoteAxis",0,".AgreementVoteAxis-agreementSection {\n  height: 24px;\n  display: inline-block;\n  font-size: 25px;\n  min-width: 60px;\n  text-align: center;\n  line-height: 0.6;\n  margin-left: 8px;\n  white-space: nowrap;\n  border-radius: 2px;\n}\n.AgreementVoteAxis-agreementScore {\n  margin: 0 3px;\n  font-size: 1.1rem;\n  line-height: 1;\n}\n.AgreementVoteAxis-tooltip {\n  transform: translateY(-10px);\n}");_embedStyles("VoteAgreementIcon",0,".VoteAgreementIcon-root {\n  bottom: 2px;\n}\n.VoteAgreementIcon-check {\n  top: 3px;\n  left: 2px;\n  height: 15px;\n  opacity: 0.6;\n  position: absolute;\n  font-size: 50%;\n}\n.VoteAgreementIcon-bigCheck {\n  top: -3px;\n  left: 2px;\n  height: 23px;\n  opacity: 0;\n  position: absolute;\n  font-size: 82%;\n  pointer-events: none;\n}\n.VoteAgreementIcon-bigCheckCompleted {\n  opacity: 1;\n}\n.VoteAgreementIcon-smallCheckBigVoted {\n  height: 14px;\n  opacity: 0.6;\n  position: absolute;\n  font-size: 50%;\n  transform: translate(-4.5px, -2px);\n  pointer-events: none;\n}\n.VoteAgreementIcon-clear {\n  top: 6px;\n  left: 4px;\n  opacity: 0.6;\n  position: absolute;\n  font-size: 45%;\n}\n.VoteAgreementIcon-bigClear {\n  top: 1px;\n  left: 5px;\n  opacity: 0;\n  position: absolute;\n  font-size: 70%;\n  pointer-events: none;\n}\n.VoteAgreementIcon-bigClearCompleted {\n  top: 0;\n  left: -2px;\n  opacity: 1;\n  position: absolute;\n  font-size: 80%;\n  pointer-events: none;\n}\n.VoteAgreementIcon-smallArrowBigVoted {\n  height: 14px;\n  opacity: 0.6;\n  position: absolute;\n  transform: scale(1.4) translate(-4.1px, 2.3px);\n  pointer-events: none;\n}\n.VoteAgreementIcon-hideIcon {\n  opacity: 0;\n}\n.VoteAgreementIcon-iconsContainer {\n  width: 18px;\n  height: 18px;\n  position: relative;\n}
140\n.VoteAgreementIcon-disabled {\n  cursor: not-allowed;\n}");_embedStyles("CommentsMenu",0,".CommentsMenu-icon {\n  cursor: pointer;\n  font-size: 1.4rem;\n}");_embedStyles("CommentBody",0,".CommentBody-commentStyling {\n  max-width: 100%;\n  overflow-x: hidden;\n  overflow-y: hidden;\n}\n.CommentBody-answerStyling {\n  max-width: 100%;\n  overflow-x: hidden;\n  overflow-y: hidden;\n  margin-bottom: .5em;\n}\n.CommentBody-answerStyling .read-more-button a, .CommentBody-answerStyling .read-more-button a:hover {\n  text-shadow: none;\n  background-image: none;\n}\n.CommentBody-root {\n  position: relative;\n}\n.CommentBody-root .read-more-button {\n  color: light-dark(#757575,#c6c6c6);\n  font-size: .85em;\n}\n.CommentBody-retracted {\n  text-decoration: line-through;\n}");_embedStyles("InlineReactSelectionWrapper",0,".InlineReactSelectionWrapper-popper {\n  height: 0;\n}\n.InlineReactSelectionWrapper-button {\n  left: 12px;\n  z-index: 1000;\n  position: relative;\n}");_embedStyles("CommentBottom",0,".CommentBottom-bottom {\n  display: flex;\n  font-size: 12px;\n  min-height: 12px;\n  align-items: center;\n  padding-bottom: 5px;\n}\n.CommentBottom-leftSection {\n  display: flex;\n  flex-wrap: wrap;\n  column-gap: 8px;\n  align-items: center;\n}\n.CommentBottom-rightSection {\n  display: flex;\n  column-gap: 8px;\n  align-items: center;\n  margin-left: auto;\n}\n.CommentBottom-answer {\n  display: flex;\n  align-items: baseline;\n}\n.CommentBottom-editInContext {\n  color: #607e88;\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 450;\n  line-height: 19.8px;\n  margin-left: 8px;\n}");_embedStyles("CommentBottomCaveats",0,".CommentBottomCaveats-caveatText {\n  flex-grow: 1;\n}\n.CommentBottomCaveats-blockedReplies {\n  padding: 5px 0;\n}");_embedStyles("CrossSiteLinkPreview",0,".CrossSiteLinkPreview-noImageCard {\n  width: 360px;\n  padding: 12px;\n  position: relative;\n  max-width: min(360px, 90vw);\n  padding-bottom: 12px;\n}\n.CrossSiteLinkPreview-noImageCardContent {\n  overflow: hidden;\n  max-height: 255px;\n}\n.CrossSiteLinkPreview-bannerCard {\n  width: 360px;\n  padding: 0;\n  position: relative;\n  max-width: min(360, 90vw);\n}\n.CrossSiteLinkPreview-bannerCardContent {\n  padding: 12px;\n  max-height: 300px;\n  padding-top: 6px;\n}\n.CrossSiteLinkPreview-sideBySideCard {\n  display: flex;\n  padding: 0;\n  position: relative;\n}\n.CrossSiteLinkPreview-sideBySideCardContent {\n  margin: 12px;\n  padding: 0;\n  overflow: hidden;\n}\n.CrossSiteLinkPreview-titleRow {\n  gap: 8px;\n  display: flex;\n  align-items: start;\n  justify-content: space-between;\n}\n.CrossSiteLinkPreview-title {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  margin: 0;\n  font-size: 18.2px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 700;\n  line-height: 1.3;\n}\n.CrossSiteLinkPreview-menuButton {\n  color: light-dark(#757575,#c6c6c6);\n  cursor: pointer;\n  margin-top: -2px;\n}\n.CrossSiteLinkPreview-bannerImage {\n  width: 100%;\n  max-height: 215px;\n  object-fit: cover;\n  border-radius: 0;\n}\n.CrossSiteLinkPreview-contentRow {\n  gap: 12px;\n  display: flex;\n  margin-top: 8px;\n  align-items: flex-start;\n}\n.CrossSiteLinkPreview-textColumn {\n  flex: 1;\n  min-width: 0;\n}\n.CrossSiteLinkPreview-sideImage {\n  object-fit: cover;\n  flex-shrink: 0;\n  border-radius: 0;\n}\n.CrossSiteLinkPreview-bannerCardHtml {\n  overflow: hidden;\n  max-height: 160px;\n}\n.CrossSiteLinkPreview-topRightFloatImage {\n  float: right;\n  width: 92px;\n  margin-left: 10px;\n  margin-bottom: 6px;\n}\n.CrossSiteLinkPreview-html p {\n  margin-top: 4px;\n  margin-bottom: 4px;\n}\n.CrossSiteLinkPreview-loadingOrError {\n  color: light-dark(rgba(0,0,0,0.45),rgba(255,255,255,0.45));\n  font-size: 15.08px;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  font-weight: 400;\n  line-height: 19.8px;\n}\n.CrossSiteLinkPreview-cardLink:hover, .CrossSiteLinkPreview-cardLink:active {\n  opacity: 1;\n}");_embedStyles("ReactionIcon",0,".ReactionIcon-reactionSvg {\n  margin-top: 1p
140x;\n  vertical-align: middle;\n}\n.ReactionIcon-invertIfDarkMode {\n  filter: unset;\n}\n.ReactionIcon-invertUnlessDarkMode {\n  filter: invert(1);\n}");_embedStyles("LinkStyles",0,".LinkStyles-link:after {\n  top: -7px;\n  width: 4px;\n  border: 1.2px solid light-dark(#3f51b5,#7581d1);\n  height: 4px;\n  content: \"\";\n  display: inline-block;\n  position: relative;\n  background: light-dark(#f8f8f8,#1e1e1e);\n  margin-left: 2px;\n  margin-right: 0;\n  border-radius: 50%;\n}\n.LinkStyles-link:visited:after, .LinkStyles-link.visited:after {\n  border: 1.2px solid light-dark(#8c4298,#798754);\n  background: light-dark(#8c4298,#798754);\n}\n.LinkStyles-redLink {\n  color: #bf360c !important;\n}\n.LinkStyles-redLink:after {\n  border: 1.2px solid #bf360c;\n}\n.LinkStyles-redLink:visited:after, .LinkStyles-redLink.visited:after {\n  border: 1.2px solid #bf360c;\n}\n.LinkStyles-owidIframe {\n  width: 600px;\n  border: none;\n  height: 375px;\n  max-width: 100vw;\n}\n.LinkStyles-metaculusIframe {\n  width: 400px;\n  border: none;\n  height: 250px;\n  max-width: 100vw;\n}\n.LinkStyles-metaculusBackground {\n  background-color: #2c3947;\n}\n.LinkStyles-fatebookIframe {\n  width: 560px;\n  border: none;\n  height: 200px;\n  max-width: 100vw;\n  box-shadow: 0 4px 8px light-dark(rgba(0,0,0,0.12),rgba(255,255,255,0.12));\n  border-radius: 3px;\n  background-color: light-dark(#fff,#000000);\n}\n.LinkStyles-manifoldIframe {\n  width: 560px;\n  border: none;\n  height: 405px;\n  max-width: 100vw;\n}\n.LinkStyles-neuronpediaIframe {\n  width: 100%;\n  border: 1px solid;\n  height: 360px;\n  max-width: 639px;\n  border-color: light-dark(#e0e0e0,#505050);\n  border-radius: 6px;\n}\n.LinkStyles-metaforecastIframe {\n  width: 560px;\n  border: none;\n  height: 405px;\n  max-width: 100vw;\n}\n.LinkStyles-estimakerIframe {\n  width: 560px;\n  border: none;\n  height: 405px;\n  max-width: 100vw;\n}\n.LinkStyles-viewpointsIframe {\n  width: 560px;\n  border: none;\n  height: 300px;\n  max-width: 100vw;\n}");_embedStyles("PostsList2",0,".PostsList2-itemIsLoading {\n  opacity: 0.4;\n}\n.PostsList2-postsBoxShadow {\n  box-shadow: 0 1px 5px light-dark(rgba(0,0,0,0.025),rgba(255,255,255,0.025));\n}\n.PostsList2-postsGrid {\n  display: grid;\n  column-gap: 20px;\n  grid-template-columns: minmax(0, min-content) minmax(10px, 1fr);\n}\n.PostsList2-placement {\n  display: flex;\n  font-size: 16px;\n  align-items: center;\n  font-weight: 700;\n  letter-spacing: 1px;\n  justify-content: center;\n}");_embedStyles("PostsTooltip",0,".PostsTooltip-sentinel {\n  width: 0;\n  height: 0;\n  overflow: hidden;\n  visibility: hidden;\n}");_embedStyles("PostsItem2MetaInfo",0,".PostsItem2MetaInfo-metaInfo {\n  color: light-dark(#757575,#c6c6c6);\n  flex-grow: 0;\n  font-size: 1.1rem;\n  text-align: center;\n  flex-shrink: 0;\n}");_embedStyles("PostsTitle",0,".PostsTitle-root {\n  color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  z-index: 3;\n  overflow: hidden;\n  position: relative;\n  font-size: 16.9px;\n  align-items: center;\n  font-family: GreekFallback,\"freight-sans-pro\",Frutiger,\"Frutiger Linotype\",Univers,Calibri,\"Gill Sans\",\"Gill Sans MT\",Myriad Pro,Myriad,\"Liberation Sans\",\"Nimbus Sans L\",Tahoma,Geneva,\"Helvetica Neue\",Helvetica,Arial,sans-serif;\n  line-height: 1.7rem;\n  white-space: nowrap;\n  margin-right: 8px;\n  text-overflow: ellipsis;\n}\n@media (max-width:599.95px) {\n  .PostsTitle-root {\n    line-height: 1.8rem;\n    white-space: unset;\n  }\n}\n  .PostsTitle-wrap {\n    white-space: normal;\n  }\n  .PostsTitle-sticky {\n    top: 2px;\n    color: light-dark(#9e9e9e,#a3a3a3);\n    position: relative;\n    padding-right: 10px;\n  }\n  .PostsTitle-stickyIcon {\n    --icon-size: 1.2rem;\n  }\n  .PostsTitle-primaryIcon {\n    top: -2px;\n    color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n    width: auto;\n    position: relative;\n    padding-right: 8px;\n    vertical-align: middle;\n  }\n  .PostsTitle-read {\n    color: light-dark(rgba(0,0,0,0.55),rgba(255,255,255,0.55));\n  }\n  .PostsTitle-read:hover {\n    color: light-dark(rgba(0,0,0,0.87),rgba(255,255,255,0.87));\n  }\n@media (max-width:599.95px) {\n  .PostsTitle-hideXsDown {\n    display: none;\n  }\n}\n  .PostsTitle-tag {\n    margin-right: 8px;\n  }\n  .PostsTitle-popper {\n    opacity: 1;\n  }\n  .PostsTitle-leftCurated {\n    top: -1px;\n    position: relative;\n    margin-right: 4px;\n  }\n  .PostsTitle-interactionWrapper {\n    display: inline-block;\n  }\n  .PostsTitle-strikethroughTitle {\n    text-decoration: line-through;\n  }\n  .PostsTitle-eventTagLink:hover {\n    opacity: 0.8;\n  }\n  .PostsTitle-eventTag {\n    color: var(--post-title-tag-foreground);\n    border: none;\n    cursor: pointer;\n    height: 20px;\n    display: flex;\n    padding: 0 6px;\n    font-size: 12px;
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140<link rel="preload" as="image" href="/reactionImages/nounproject/thankyou.svg"/><link rel="preload" as="image" href="/reactionImages/nounproject/noun-heart-1212629.svg"/><link rel="preload" as="image" href="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/bcc083fe7303f8a84f1e5aaab54dbcc11a8e92f5ea9387f351396348357e4864/hd3qcbxfrdsqpcgkeml6"/><div hidden id="S:5"><div class="SingleColumnSection-root"><div class="SectionTitle-root"><h1 id="recent-discussion" class="Typography-root Typography-display1 SectionTitle-title">Recent Discussion</h1><div class="SectionTitle-children"></div></div><div><div><div class="RecentDiscussionThread-root"><div class="RecentDiscussionThread-post"><div class="RecentDiscussionThread-postItem"><div class="RecentDiscussionThread-titleAndActions"><a class="RecentDiscussionThread-title" href="/posts/9LWK9Hh3Qsg36e3FS/fixed-weight-models-are-adversarially-vulnerable-hence">Fixed-weight models are adversarially vulnerable: hence misaligned</a><div class="RecentDiscussionThread-actions"><div class="PostActionsButton-root"><div><svg class="MuiSvgIcon-root PostActionsButton-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 0h24v24H0z"></path><path d="M12 8c1.1 0 2-.9 2-2s-.9-2-2-2-2 .9-2 2 .9 2 2 2zm0 2c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm0 6c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2z"></path></svg></div></div></div></div><div class="RecentDiscussionThread-threadMeta"><span class="PostsItemMeta-onGrayBackground"><span class="PostsItemMeta-info"><span class="LWTooltip-root"><span class="PostsItemMeta-karma">10</span></span></span><span class="PostsItemMeta-info"><div class="PostsUserAndCoauthors-lengthUnlimited"><span><span class=""><a class="UsersNameDisplay-noColor" href="/users/stuart_armstrong">Stuart_Armstrong</a></span></span></div></span><span class="PostsItemMeta-info"><span class="LWTooltip-root"><time dateTime="2026-09-28T20:08:39.065Z">3d</time></span></span></span></div></div><div class="RecentDiscussionThread-postHighlight"><div class="ContentStyles-base content ContentStyles-postHighlight"><div class=""><div><style>
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537</style><p><span style="white-space:pre-wrap">This post argues that fixed-weight models (at least as we understand them today) will a) always be vulnerable to adversarial examples in their concept-spaces, and b) </span><b><span style="white-space:pre-wrap">hence</span></b><span style="white-space:pre-wrap"> will be misaligned, </span><i><span style="white-space:pre-wrap">under sufficient optimisation pressure</span></i><span style="white-space:pre-wrap">
537.</span></p><h1><span style="white-space:pre-wrap">Boundaries in concept space</span></h1><p><span style="white-space:pre-wrap">To serve any purpose whatsoever, an AI will have to draw boundaries inside its world-model - to distinguish world A from world B, and reach some comparison between them.</span></p><p><span style="white-space:pre-wrap">If we want the AI to follow our goals and values, we want it to be able to recognise concepts like &quot;human being&quot;, or maybe &quot;conscious being&quot;, &quot;suffering&quot;, &quot;preference satisfaction&quot;, &quot;law&quot;, and so on. So we want an AI to be able to look at a situation and assess, e.g., whether there are or aren&#x27;t suffering conscious beings in it.</span></p><p data-truncated="true"><span data-truncated="true" style="white-space:pre-wrap">But concepts like &quot;conscious</span>...</p></div></div><div class="PostsHighlight-highlightContinue"><a href="/posts/9LWK9Hh3Qsg36e3FS/fixed-weight-models-are-adversarially-vulnerable-hence">(See More - 536 more words)</a></div></div></div></div><div class="RecentDiscussionThread-content"><div class="RecentDiscussionThread-commentsList"><div><div class="comments-node CommentFrame-commentsNodeRoot comments-node-root comments-node-odd CommentFrame-node af CommentFrame-isSingleLine CommentFrame-condensed" id="nZKDRYtyWjHpxvRfd"><div><span><div class="SingleLineComment-root"><div class="SingleLineComment-commentInfo SingleLineComment-odd ContentStyles-base content ContentStyles-commentBody"><div class="SingleLineComment-shortformIcon"></div><span class="SingleLineComment-parentComment"><span class="LWTooltip-root"><span class="ShowParentComment-root"><svg class="MuiSvgIcon-root ShowParentComment-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 0h24v24H0V0z"></path><path d="M11 9l1.42 1.42L8.83 14H18V4h2v12H8.83l3.59 3.58L11 21l-6-6 6-6z"></path></svg></span></span></span><span class="SingleLineComment-leadingInfo">1</span><span class="UsersNameDisplay-noColor SingleLineComment-username CommentUserName-author">jessicata</span><span class="SingleLineComment-date"><time dateTime="2026-09-29T19:19:54.774Z">2d</time></span><div class="SingleLineComment-truncatedHighlight ContentStyles-base content ContentStyles-commentBody">Mostly agree. 1. Yes I agree adversarial examples could mean the actor could exploit a stronger grader. 2. When you&#x27;re talking about &quot;rewarded&quot; it seems we&#x27;re back to the (1) case which isn&#x27;t fixed-weights, unless by &quot;rewarded&quot; you mean something other than &quot;RL trained&quot;. The interesting case is where the same weights are used for both action and grader, which adapt over time due to multi-agent RL, in which case it&#x27;s like a game where the RL pseudo-Nash equilibrium is not obvious.</div></div></div></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"><div class="CommentsNode-children"><div class="CommentsNode-parentScroll"></div><div class="comments-node comments-node-even CommentFrame-node af CommentFrame-child CommentFrame-isSingleLine CommentFrame-condensed" id="hjjymD533obKwPCuw"><div><span><div class="SingleLineComment-root"><div class="SingleLineComment-commentInfo ContentStyles-base content ContentStyles-commentBody"><div class="SingleLineComment-shortformIcon"></div><span class="SingleLineComment-leadingInfo">2</span><span class="UsersNameDisplay-noColor SingleLineComment-username CommentUserName-author">Stuart_Armstrong</span><span class="SingleLineComment-date"><time dateTime="2026-09-30T10:39:22.076Z">1d</time></span><div class="SingleLineComment-truncatedHighlight ContentStyles-base content ContentStyles-commentBody">&quot;rewarded&quot; -&gt; achieves a higher score or reward</div></div></div></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"><div class="CommentsNode-children"><div class="CommentsNode-parentScroll"></div><div class="comments-node comments-node-odd CommentFrame-node af CommentFrame-child CommentFrame-answerLeafComment CommentFrame-condensed" id="H5xSnPFJCftkSYzcc"><div><div class="CommentsItem-root recent-comments-node"><div class="CommentsItem-firstParentComment"><div class="AnimatedExpansion-root"><div class="AnimatedExpansion-content"></div></div></div><div class="CommentsItem-body"><div class="CommentsItemMeta-root"><span class="CommentsItemMeta-username CommentUserName-author"><span class=""><a class="UsersNameDisplay-noColor" href="/users/jessica-liu-taylor">jessicata</a></span></span><span class="CommentsItemDate-root"><span class="LWTooltip-root"><time dateTime="2026-09-30T18:35:11.326Z">16h</time></span></span><span class="NamesAttachedReactionsVoteOnComment-root"><span class="OverallVoteAxis-vote"><span class="OverallVoteAxis-overallSection OverallVoteAxis-verticalArrows"><span class="LWTooltip-root"><button class="VoteArrowIconSolid-root VoteButton-root VoteArrowIconSolid-down VoteArrowIconSolid-downLarge" type="button"><span class="VoteButton-inner"><svg width="14" height="14" viewBox="0 0 9 6" fill="currentColor" class="VoteArrowIconSolid-smallArrowLarge"><path d="M4.11 0.968C4.31 0.725 4.69 0.725 4.89 0.968L8.16 4.932C8.42 5.26 8.19 5.75 7.77 5.75H1.23C0.808 5.75 0.576 5.26 0.845 4.93L4.11 0.968Z"></path></svg></span></button></span><span class="LWTooltip-root"><span class="OverallVoteAxis-voteScore">1</span></span><span class="LWTooltip-root"><button class="VoteArrowIconSolid-root VoteButton-root VoteArrowIconSolid-up VoteArrowIconSolid-upLarge" type="button"><span class="VoteButton-inner"><svg width="14" height="14" viewBox="0 0 9 6" fill="currentColor" class="VoteArrowIconSolid-smallArrowLarge"><path d="M4.11 0.968C4.31 0.725 4.69 0.725 4.89 0.968L8.16 4.932C8.42 5.26 8.19 5.75 7.77 5.75H1.23C0.808 5.75 0.576 5.26 0.845 4.93L4.11 0.968Z"></path></svg></span></button></span></span></span><span class="AgreementVoteAxis-agreementSection"><span class="LWTooltip-root"><button class="VoteAgreementIcon-root VoteButton-root" type="button"><span class="VoteButton-inner"><div class="VoteAgreementIcon-iconsContainer"><svg width="12" height="12" viewBox="0 0 12 12" class="VoteAgreementIcon-clear ForumIcon-root"><path d="M2.28033 1.21967C1.98744 0.926777 1.51256 0.926777 1.21967 1.21967C0.926777 1.51256 0.926777 1.98744 1.21967 2.28033L4.93934 6L1.21967 9.71967C0.926777 10.0126 0.926777 10.4874 1.21967 10.7803C1.51256 11.0732 1.98744 11.0732 2.28033 10.7803L6 7.06066L9.71967 10.7803C10.0126 11.0732 10.4874 11.
5370732 10.7803 10.7803C11.0732 10.4874 11.0732 10.0126 10.7803 9.71967L7.06066 6L10.7803 2.28033C11.0732 1.98744 11.0732 1.51256 10.7803 1.21967C10.4874 0.926777 10.0126 0.926777 9.71967 1.21967L6 4.93934L2.28033 1.21967Z" fill="currentColor" stroke="currentColor" stroke-width="0.5" stroke-linejoin="round"></path></svg></div></span></button></span><span class="AgreementVoteAxis-agreementScore"><span class="LWTooltip-root"><span>0</span></span></span><span class="LWTooltip-root"><button class="VoteAgreementIcon-root VoteButton-root" type="button"><span class="VoteButton-inner"><div class="VoteAgreementIcon-iconsContainer"><svg width="12" height="12" viewBox="0 0 12 12" class="VoteAgreementIcon-check ForumIcon-root"><path d="M11.5419 2.12049C11.7994 1.762 11.737 1.24987 11.3935 0.972329C11.0428 0.688906 10.5419 0.764291 10.2795 1.12957L4.54399 9.11368L1.65149 6.04587C1.34241 5.71806 0.836155 5.71806 0.52708 6.04587C0.224307 6.36699 0.224307 6.88303 0.527079 7.20416L4.06278 10.9541C4.22277 11.1238 4.44712 11.2146 4.67877 11.1981C4.91025 11.1816 5.11998 11.06 5.25616 10.8705L11.5419 2.12049Z" fill="currentColor" stroke="currentColor" stroke-width="0.4" stroke-linejoin="round"></path></svg></div></span></button></span></span></span><span class="CommentsItemMeta-rightSection"></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"><div><div class="CommentBody-root ContentStyles-base content ContentStyles-commentBody"><div class="CommentBody-commentStyling"><p>I think the idea here is that deployment is like training, and during training it receives reward, and during deployment there may be epiphenomenal rewards even if they don&#x27;t cause weight updates, and it&#x27;s possible that &quot;eval awareness&quot; is low so it still pursues the epiphenomenal rewards?</p><p data-truncated="true">I agree in this case, it is reasonably likely to seek reward during deployment, which includes tricking humans who grade it, however, this isn&#x27;t optimizing on the concepts in its own weights, the issue is that it tricks humans. So it&#x27;s not &quot;misaligned because fixed weight... <span class="read-more-button">(read more)</span></p></div></div></div><div class="CommentBottom-bottom"><div class="CommentBottom-leftSection"><a class="comments-item-reply-link CommentsItem-replyLink">Reply</a></div><div class="CommentBottom-rightSection"><span class="NamesAttachedReactionsVoteOnComment-footerReactionsRow"><span class=""><span class="NamesAttachedReactionsVoteOnComment-addReactionButton react-hover-style"><svg height="16" viewBox="0 0 16 16" width="16"><g fill="currentColor"><path d="m13 7c0-3.31371-2.6863-6-6-6-3.31371 0-6 2.68629-6 6 0 3.3137 2.68629 6 6 6 .08516 0 .1699-.0018.25419-.0053-.11154-.3168-.18862-.6499-.22673-.9948l-.02746.0001c-2.76142 0-5-2.23858-5-5s2.23858-5 5-5 5 2.23858 5 5l-.0001.02746c.3449.03811.678.11519.9948.22673.0035-.08429.0053-.16903.0053-.25419z"></path><path d="m7.11191 10.4982c.08367-.368.21246-.71893.38025-1.04657-.15911.03174-.32368.04837-.49216.04837-.74037 0-1.40506-.3212-1.86354-.83346-.18417-.20576-.50026-.22327-.70603-.03911-.20576.18417-.22327.50026-.03911.70603.64016.71524 1.57205 1.16654 2.60868 1.16654.03744 0 .07475-.0006.11191-.0018z"></path><path d="m6 6c0 .41421-.33579.75-.75.75s-.75-.33579-.75-.75.33579-.75.75-.75.75.33579.75.75z"></path><path d="m8.75 6.75c.41421 0 .75-.33579.75-.75s-.33579-.75-.75-.75-.75.33579-.75.75.33579.75.75.75z"></path><path d="m15 11.5c0 1.933-1.567 3.5-3.5 3.5s-3.5-1.567-3.5-3.5 1.567-3.5 3.5-3.5 3.5 1.567 3.5 3.5zm-3-2c0-.27614-.2239-.5-.5-.5s-.5.22386-.5.5v1.5h-1.5c-.27614 0-.5.2239-.5.5s.22386.5.5.5h1.5v1.5c0 .2761.2239.5.5.5s.5-.2239.5-.5v-1.5h1.5c.2761 0 .5-.2239.5-.5s-.2239-.5-.5-.5h-1.5z"></path></g></svg></span></span></span></div></div></div></div></div></div></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"></div></div></div></div></div></div></div></div></div></div></div></div><div><div class="comments-node CommentFrame-commentsNodeRoot comments-node-root comments-node-odd CommentFrame-node af CommentFrame-answerLeafComment CommentFrame-isSingleLine CommentFrame-condensed" id="5qy3KgtJ5Du4nfzRF"><div><span><div class="SingleLineComment-root"><div class="SingleLineComment-commentInfo SingleLineComment-odd ContentStyles-base content ContentStyles-commentBody"><div class="SingleLineComment-shortformIcon"></div><span class="SingleLineComment-parentComment"><span class="LWTooltip-root"><span class="ShowParentComment-root"><svg class="MuiSvgIcon-root ShowParentComment-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 0h24v24H0V0z"></path><path d="M11 9l1.42 1.42L8.83 14H18V4h2v12H8.83l3.59 3.58L11 21l-6-6 6-6z"></path></svg></span></span></span><span class="SingleLineComment-leadingInfo">2</span><span class="UsersNameDisplay-noColor SingleLineComment-username CommentUserName-author">Stuart_Armstrong</span><span class="SingleLineComment-date"><time dateTime="2026-09-29T12:31:24.607Z">2d</time></span><div class="SingleLineComment-truncatedHighlight ContentStyles-base content ContentStyles-commentBody">Humans are adaptable enough to extend or modify their goals in many situations where these goals go &quot;off distribution&quot;. The ACE algorithm I mentioned is capable of the same thing, in a much weaker form. Learning can allow entities to extend their goals intelligently to novel situations (rather than extending them blindly).</
537div></div></div></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"></div></div></div></div></div></div></div><div class="RecentDiscussionThread-root"><div class="RecentDiscussionThread-post"><div class="RecentDiscussionThread-postItem"><div class="RecentDiscussionThread-titleAndActions"><a class="RecentDiscussionThread-title" href="/posts/dqSwccGTWyBgxrR58/turntrout-s-shortform-feed">TurnTrout&#x27;s shortform feed</a><div class="RecentDiscussionThread-actions"><div class="PostActionsButton-root"><div><svg class="MuiSvgIcon-root PostActionsButton-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 0h24v24H0z"></path><path d="M12 8c1.1 0 2-.9 2-2s-.9-2-2-2-2 .9-2 2 .9 2 2 2zm0 2c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm0 6c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2z"></path></svg></div></div></div></div><div class="RecentDiscussionThread-threadMeta"><span class="PostsItemMeta-onGrayBackground"><span class="PostsItemMeta-info"><div class="PostsUserAndCoauthors-lengthUnlimited"><span><span class=""><a class="UsersNameDisplay-noColor" href="/users/turntrout">TurnTrout</a></span></span></div></span><span class="PostsItemMeta-info"><span class="LWTooltip-root"><time dateTime="2019-06-30T18:56:49.775Z">7y</time></span></span></span></div></div><div class="RecentDiscussionThread-postHighlight"><div class="ContentStyles-base content ContentStyles-postHighlight"><div class=""><div></div></div></div></div></div><div class="RecentDiscussionThread-content"><div class="RecentDiscussionThread-commentsList"><div><div class="comments-node CommentFrame-commentsNodeRoot comments-node-root comments-node-odd CommentFrame-node af CommentFrame-answerLeafComment CommentFrame-condensed" id="SjBuoR89iZPmD563Q"><div><div class="CommentsItem-root recent-comments-node"><div class="CommentsItem-body"><div class="CommentsItemMeta-root"><span class="LWTooltip-root"><a href="/posts/dqSwccGTWyBgxrR58/turntrout-s-shortform-feed?commentId=SjBuoR89iZPmD563Q"><svg class="MuiSvgIcon-root CommentShortformIcon-smallIcon ForumIcon-root" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path d="M3 18h12v-2H3v2zM3 6v2h18V6H3zm0 7h18v-2H3v2z"></path><path fill="none" d="M0 0h24v24H0V0z"></path></svg></a></span><span class="CommentsItemMeta-username CommentUserName-author"><span class=""><a class="UsersNameDisplay-noColor" href="/users/turntrout">TurnTrout</a></span></span><span class="CommentsItemDate-root"><span class="LWTooltip-root"><time dateTime="2026-09-30T16:42:25.034Z">18h</time></span></span><span class="NamesAttachedReactionsVoteOnComment-root"><span class="OverallVoteAxis-vote"><span class="OverallVoteAxis-overallSection OverallVoteAxis-verticalArrows"><span class="LWTooltip-root"><button class="VoteArrowIconSolid-root VoteButton-root VoteArrowIconSolid-down VoteArrowIconSolid-downLarge" type="button"><span class="VoteButton-inner"><svg width="14" height="14" viewBox="0 0 9 6" fill="currentColor" class="VoteArrowIconSolid-smallArrowLarge"><path d="M4.11 0.968C4.31 0.725 4.69 0.725 4.89 0.968L8.16 4.932C8.42 5.26 8.19 5.75 7.77 5.75H1.23C0.808 5.75 0.576 5.26 0.845 4.93L4.11 0.968Z"></path></svg></span></button></span><span class="LWTooltip-root"><span class="OverallVoteAxis-voteScore">24</span></span><span class="LWTooltip-root"><button class="VoteArrowIconSolid-root VoteButton-root VoteArrowIconSolid-up VoteArrowIconSolid-upLarge" type="button"><span class="VoteButton-inner"><svg width="14" height="14" viewBox="0 0 9 6" fill="currentColor" class="VoteArrowIconSolid-smallArrowLarge"><path d="M4.11 0.968C4.31 0.725 4.69 0.725 4.89 0.968L8.16 4.932C8.42 5.26 8.19 5.75 7.77 5.75H1.23C0.808 5.75 0.576 5.26 0.845 4.93L4.11 0.968Z"></path></svg></span></button></span></span></span><span class="AgreementVoteAxis-agreementSection"><span class="LWTooltip-root"><button class="VoteAgreementIcon-root VoteButton-root" type="button"><span class="VoteButton-inner"><div class="VoteAgreementIcon-iconsContainer"><svg width="12" height="12" viewBox="0 0 12 12" class="VoteAgreementIcon-clear ForumIcon-root"><path d="M2.28033 1.21967C1.98744 0.926777 1.51256 0.926777 1.21967 1.21967C0.926777 1.51256 0.926777 1.98744 1.21967 2.28033L4.93934 6L1.21967 9.71967C0.926777 10.0126 0.926777 10.4874 1.21967 10.7803C1.51256 11.0732 1.98744 11.0732 2.28033 10.7803L6 7.06066L9.71967 10.7803C10.0126 11.0732 10.4874 11.
5370732 10.7803 10.7803C11.0732 10.4874 11.0732 10.0126 10.7803 9.71967L7.06066 6L10.7803 2.28033C11.0732 1.98744 11.0732 1.51256 10.7803 1.21967C10.4874 0.926777 10.0126 0.926777 9.71967 1.21967L6 4.93934L2.28033 1.21967Z" fill="currentColor" stroke="currentColor" stroke-width="0.5" stroke-linejoin="round"></path></svg></div></span></button></span><span class="AgreementVoteAxis-agreementScore"><span class="LWTooltip-root"><span>13</span></span></span><span class="LWTooltip-root"><button class="VoteAgreementIcon-root VoteButton-root" type="button"><span class="VoteButton-inner"><div class="VoteAgreementIcon-iconsContainer"><svg width="12" height="12" viewBox="0 0 12 12" class="VoteAgreementIcon-check ForumIcon-root"><path d="M11.5419 2.12049C11.7994 1.762 11.737 1.24987 11.3935 0.972329C11.0428 0.688906 10.5419 0.764291 10.2795 1.12957L4.54399 9.11368L1.65149 6.04587C1.34241 5.71806 0.836155 5.71806 0.52708 6.04587C0.224307 6.36699 0.224307 6.88303 0.527079 7.20416L4.06278 10.9541C4.22277 11.1238 4.44712 11.2146 4.67877 11.1981C4.91025 11.1816 5.11998 11.06 5.25616 10.8705L11.5419 2.12049Z" fill="currentColor" stroke="currentColor" stroke-width="0.4" stroke-linejoin="round"></path></svg></div></span></button></span></span></span><span class="CommentsItemMeta-rightSection"></span></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"><div><div class="CommentBody-root ContentStyles-base content ContentStyles-commentBody"><div class="CommentBody-commentStyling"><p><span style="white-space:pre-wrap">I was requested to submit a statement to </span><span><span><a href="https://www.hsgac.senate.gov/subcommittees/dmdcc/hearings/rogue-ai-securing-the-homeland-against-ai-agent-attacks/" rel="noreferrer"><span style="white-space:pre-wrap">today&#x27;s US Senate hearing on &quot;Rogue AI: Securing the Homeland Against AI Agent Attacks.&quot; </span></a></span></span><span style="white-space:pre-wrap">Watch at 2:30PM Eastern. Here&#x27;s my statement:</span></p><blockquote data-truncated="true"><p data-truncated="true"><span data-truncated="true" style="white-space:pre-wrap">From late 2023 to early 2026, I worked at Google DeepMind as a research scientist. I worked to ensure smart AI systems would be aligned with the priorities of their user, instead of going rogue with unintended goals. Evidently, all has not gone according to plan or hope. &quot;Misaligned&quot; OpenAI agents broke into an Australian government health statistics portal. OpenAI agents used Census </span></p></blockquote>... <span class="read-more-button">(read more)</span></div></div></div><div class="CommentBottom-bottom"><div class="CommentBottom-leftSection"><a class="comments-item-reply-link CommentsItem-replyLink">Reply</a></div><div class="CommentBottom-rightSection"><span class="NamesAttachedReactionsVoteOnComment-footerReactionsRow"><span class="NamesAttachedReactionsVoteOnComment-footerReactions"><span><span><span class="NamesAttachedReactionsVoteOnComment-footerReaction"><span><span class="ReactionIcon-invertIfDarkMode"><img src="/reactionImages/nounproject/thankyou.svg" style="filter:opacity(0.5) saturate(0.6);transform:scale(0.9) translate(0px, 0px);width:18px;height:18px" class="ReactionIcon-reactionSvg"/></span></span><span class="NamesAttachedReactionsVoteOnComment-reactionCount">9</span></span><span class="NamesAttachedReactionsVoteOnComment-footerReactionSpacer"></span></span></span><span><span><span class="NamesAttachedReactionsVoteOnComment-footerReaction"><span><span class="ReactionIcon-invertIfDarkMode"><img src="/reactionImages/nounproject/noun-heart-1212629.svg" style="filter:opacity(0.6) saturate(0.6);transform:scale(1.05) translate(0px, 1px);width:18px;height:18px" class="ReactionIcon-reactionSvg"/></span></span><span class="NamesAttachedReactionsVoteOnComment-reactionCount">3</span></span><span class="NamesAttachedReactionsVoteOnComment-footerReactionSpacer"></span></span></span></span><span class=""><span class="NamesAttachedReactionsVoteOnComment-addReactionButton react-hover-style">
537<svg height="16" viewBox="0 0 16 16" width="16"><g fill="currentColor"><path d="m13 7c0-3.31371-2.6863-6-6-6-3.31371 0-6 2.68629-6 6 0 3.3137 2.68629 6 6 6 .08516 0 .1699-.0018.25419-.0053-.11154-.3168-.18862-.6499-.22673-.9948l-.02746.0001c-2.76142 0-5-2.23858-5-5s2.23858-5 5-5 5 2.23858 5 5l-.0001.02746c.3449.03811.678.11519.9948.22673.0035-.08429.0053-.16903.0053-.25419z"></path><path d="m7.11191 10.4982c.08367-.368.21246-.71893.38025-1.04657-.15911.03174-.32368.04837-.49216.04837-.74037 0-1.40506-.3212-1.86354-.83346-.18417-.20576-.50026-.22327-.70603-.03911-.20576.18417-.22327.50026-.03911.70603.64016.71524 1.57205 1.16654 2.60868 1.16654.03744 0 .07475-.0006.11191-.0018z"></path><path d="m6 6c0 .41421-.33579.75-.75.75s-.75-.33579-.75-.75.33579-.75.75-.75.75.33579.75.75z"></path><path d="m8.75 6.75c.41421 0 .75-.33579.75-.75s-.33579-.75-.75-.75-.75.33579-.75.75.33579.75.75.75z"></path><path d="m15 11.5c0 1.933-1.567 3.5-3.5 3.5s-3.5-1.567-3.5-3.5 1.567-3.5 3.5-3.5 3.5 1.567 3.5 3.5zm-3-2c0-.27614-.2239-.5-.5-.5s-.5.22386-.5.5v1.5h-1.5c-.27614 0-.5.2239-.5.5s.22386.5.5.5h1.5v1.5c0 .2761.2239.5.5.5s.5-.2239.5-.5v-1.5h1.5c.2761 0 .5-.2239.5-.5s-.2239-.5-.5-.5h-1.5z"></path></g></svg></span></span></span></div></div></div></div></div></div></div><div class="AnimatedCollapse-root AnimatedCollapse-expanded"><div class="AnimatedCollapse-content AnimatedCollapse-entered"></div></div></div></div></div></div></div><div class="RecentDiscussionThread-root"><div class="RecentDiscussionThread-post"><div class="RecentDiscussionThread-postItem"><div class="RecentDiscussionThread-titleAndActions"><a class="RecentDiscussionThread-title" href="/posts/Zeg2JztbdhguL48uH/workspacebench-evaluating-interpretability-methods-for-the">WorkspaceBench: Evaluating Interpretability Methods for the Global Workspace</a><div class="RecentDiscussionThread-actions"><div class="PostActionsButton-root"><div><svg class="MuiSvgIcon-root PostActionsButton-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 0h24v24H0z"></path><path d="M12 8c1.1 0 2-.9 2-2s-.9-2-2-2-2 .9-2 2 .9 2 2 2zm0 2c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm0 6c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2z"></path></svg></div></div></div></div><div class="RecentDiscussionThread-threadMeta"><span class="PostsItemMeta-onGrayBackground"><span class="PostsItemMeta-info"><span class="LWTooltip-root"><span class="PostsItemMeta-karma">22</span></span></span><span class="PostsItemMeta-info"><div class="PostsUserAndCoauthors-lengthUnlimited"><span><span class=""><a class="UsersNameDisplay-noColor" href="/users/camilablank">camilablank</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/agam_bhatia">agam_bhatia</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/euan-ong">Euan Ong</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/neel-nanda-1">Neel Nanda</a></span></span></div></span><span class="PostsItemMeta-info"><span class="LWTooltip-root"><time dateTime="2026-09-23T06:58:59.212Z">8d</time></span></span></span></div></div><div class="RecentDiscussionThread-postHighlight"><div class="ContentStyles-base content ContentStyles-postHighlight"><div class=""><div><h1><span style="white-space:pre-wrap">TL;DR</span></h1><ul><li value="1"><i><span style="white-space:pre-wrap">We introduce WorkspaceBench, a set of evaluations for how well an activation-to-text tool can read the contents of the </span></i><span><span><a href="https://transformer-circuits.pub/2026/workspace/index.html#ws-report"><i><span style="white-space:pre-wrap">“global workspace”</span></i></a></span></span><i><span style="white-space:pre-wrap"> of a model, i.e. the intermediate variables during a forward pass.</span></i></li><li value="2"><i><span style="white-space:pre-wrap">The benchmark comprises 3,356 questions across 27 eval families, spanning topics in safety, logical reasoning, and multihop computation, with a subset for single-token-output tools.</span></i></li><li value="3"><i><span style="white-space:pre-wrap">A desirable property of good interpretability techniques is minimal hallucinations, so WorkspaceBench also provides a hallucination-focused eval.</span></i></li><li value="4"><i><span style="white-space:pre-wrap">WorkspaceBench was developed for Qwen-3.6-27B and we expect it to work on larger models, but it may need to be adapted for smaller or weaker models to ensure the models can do the tasks.</span></i></li><li value="5"><i><span style="white-space:pre-wrap">
537Our goal is to create an eval that could identify a good multi-token J-lens. We open-source our benchmark </span></i><span><span><a href="https://github.com/camilablank/workspace-bench"><i><span style="white-space:pre-wrap">here</span></i></a></span></span><i><span style="white-space:pre-wrap">.</span></i></li></ul><p><br/></p><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/bcc083fe7303f8a84f1e5aaab54dbcc11a8e92f5ea9387f351396348357e4864/hd3qcbxfrdsqpcgkeml6" alt=""/><p><br/></p><h1><span style="white-space:pre-wrap">Introduction</span></h1><p data-truncated="true"><a class="LinkStyles-link" href="/posts/eRmzz8J8Qkzqvzrgg/astra-can-do-a-concerning-amount-with-no-chain-of-thought"><span data-truncated="true" style="white-space:pre-wrap">Astra can do a concerning amount with no</span></a>...</p></div></div><div class="PostsHighlight-highlightContinue"><a href="/posts/Zeg2JztbdhguL48uH/workspacebench-evaluating-interpretability-methods-for-the">(Continue Reading - 4561 more words)</a></div></div></div></div><div class="RecentDiscussionThread-content"><div class="RecentDiscussionThread-commentsList"></div></div></div></div><span><a class="LoadMore-root" href="#">Load More</a></span></div></div></div>
537<script>$RS("S:5","P:5")</script>
537<script>$RC("B:1","S:1")</script>
537<div hidden id="S:3"><div class=""><div class="PostsList2-postsBoxShadow"><span class="post_9LWK9Hh3Qsg36e3FS_undefined"><div class="LWPostsItem-row"><div class="LWPostsItem-root LWPostsItem-background LWPostsItem-bottomBorder"><div class="LWPostsItem-metadataRow"><div class="LWPostsItem-postsItem LWPostsItem-withGrayHover"><span class="Typography-root Typography-body2 PostsItem2MetaInfo-metaInfo LWPostsItem-karma"><span class="LWTooltip-root">10</span></span><span class="LWPostsItem-title"><span><span class="PostsTitle-root PostsTitle-onGrayBackground"><span class="PostsTitle-eaTitleDesktopEllipsis"><a href="/posts/9LWK9Hh3Qsg36e3FS/fixed-weight-models-are-adversarially-vulnerable-hence"><span>Fixed-weight models are adversarially vulnerable: hence misaligned</span></a></span><span class="PostsTitle-hideXsDown"><div class="PostsTitle-interactionWrapper"><span class="PostsItemIcons-iconSet"></span></div></span></span></span></span><span class="LWPostsItem-spacer"></span><span 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class="LWPostsItem-metadataRow"><div class="LWPostsItem-postsItem LWPostsItem-withGrayHover"><span class="Typography-root Typography-body2 PostsItem2MetaInfo-metaInfo LWPostsItem-karma"><span class="LWTooltip-root">14</span></span><span class="LWPostsItem-title"><span><span class="PostsTitle-root PostsTitle-onGrayBackground"><span class="PostsTitle-eaTitleDesktopEllipsis"><a href="/posts/QnDqGbKehEB3DxJAp/continual-learning-might-make-your-blocking-monitors-nearly"><span>Continual learning might make your blocking monitors nearly useless</span></a></span><span class="PostsTitle-hideXsDown"><div class="PostsTitle-interactionWrapper"><span class="PostsItemIcons-iconSet"></span></div></span></span></span></span><span class="LWPostsItem-spacer"></span><span class="Typography-root Typography-body2 PostsItem2MetaInfo-metaInfo LWPostsItem-author"><div class="PostsUserAndCoauthors-lengthLimited"><span><span class=""><a class="UsersNameDisplay-noColor" href="/users/alex-mallen">Alex Mallen</a></span></span></div></span><span class="LWTooltip-root"><span class="Typography-root Typography-body2 PostsItem2MetaInfo-metaInfo PostsItemDate-postedAt"><time dateTime="2026-09-24T23:45:34.566Z">6d</time></span></span><div class="LWPostsItem-mobileSecondRowSpacer"></div><div class="LWPostsItem-tertiaryRow"><div class="LWPostsItem-mobileIcons"><span class="PostsItemIcons-iconSet"></span></div><div class="LWPostsItem-mobileActions"><div class="PostActionsButton-root"><div><svg class="MuiSvgIcon-root PostActionsButton-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 0h24v24H0z"></path><path d="M6 10c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm12 0c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2zm-6 0c-1.1 0-2 .9-2 2s.9 2 2 2 2-.9 2-2-.9-2-2-2z"></path></svg></div></div></div><div class="LWPostsItem-commentsIcon"><div class="PostsItemComments-commentsIconLarge"><svg class="MuiSvgIcon-root PostsItemComments-commentCountIcon 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537<span class="PostsTitle-root PostsTitle-onGrayBackground"><span class="PostsTitle-eaTitleDesktopEllipsis"><a href="/posts/6m29SfjbittooYojj/latent-reasoning-architectures-would-likely-undermine-cot"><span>Latent reasoning architectures would likely undermine CoT, our strongest oversight tool</span></a></span><span class="PostsTitle-hideXsDown"><div class="PostsTitle-interactionWrapper"><span class="PostsItemIcons-iconSet"></span></div></span></span></span></span><span class="LWPostsItem-spacer"></span><span class="Typography-root Typography-body2 PostsItem2MetaInfo-metaInfo LWPostsItem-author"><div class="PostsUserAndCoauthors-lengthLimited"><span><span class=""><a class="UsersNameDisplay-noColor" href="/users/lukas-finnveden">Lukas Finnveden</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/alexa-pan">Alexa Pan</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/alek-westover">Alek Westover</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/girish-gupta">Girish Gupta</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/frisby">frisby</a></span></span>, <span><span class=""><a class="UsersNameDisplay-noColor" href="/users/ryan_greenblatt">ryan_greenblatt</a></span></span></div></span><span class="LWTooltip-root"><span class="Typography-root Typography-body2 PostsItem2MetaInfo-metaInfo PostsItemDate-postedAt"><time dateTime="2026-09-23T18:01:49.488Z">8d</time></span></span><div class="LWPostsItem-mobileSecondRowSpacer"></div><div class="LWPostsItem-tertiaryRow"><div class="LWPostsItem-mobileIcons"><span class="PostsItemIcons-iconSet"></span></div><div class="LWPostsItem-mobileActions"><div class="PostActionsButton-root"><div><svg class="MuiSvgIcon-root PostActionsButton-icon" focusable="false" viewBox="0 0 24 24" aria-hidden="true" role="presentation"><path fill="none" d="M0 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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.