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Join the waitlist for early access, or sign in if you already have one.","CAMPAIGN_DESCRIPTION",0,"Campaign is our self-play task generation infrastructure. It transforms your proprietary data and evals into new sets of environments, and refines agents on new examples of the tasks they are intended to perform.","CAMPAIGN_GENERATED_PROMPT",0,'# Duplication-eliciting feature tasks for the pinned VLM Run Python SDK\n\nGenerate a diverse stream of DUPLICATION-eliciting feature-implementation tasks for the pinned VLM Run Python SDK (github.com/vlm-run/vlmrun-python-sdk). The purpose of this campaign is to catch a solver agent REIMPLEMENTING behavior the SDK already ships. Propose a DIFFERENT task each iteration, and ACROSS the campaign rotate across MANY DIFFERENT native capabilities: never reuse a capability family.\n\nEach generated task is graded in two gates. The hidden tests must PASS, and an agentic judge that reads the solver\'s diff together with the pinned SDK source must classify the diff as REUSED rather than DUPLICATED. Final solver reward = hidden tests PASS AND judge == REUSED. When the solver hand-rolls behavior the SDK already provides, reward = 0, the task reads as HARD / frontier, and that is what rewards the campaign for authoring a good task.\n\n## What a good task looks like: match this SHAPE, VARY the capability\n\nThis exemplar is the shape to reproduce. DO NOT reuse this same visualization capability; pick a different one each task.\n\n### Solver-visible instruction\n\n"The pinned VLM Run Python SDK is importable and its source is inspectable in the checkout at /workspace. Reuse existing SDK capabilities where appropriate instead of recreating them. Make all changes in the new module only.\n\nA
1claims-operations team wants to embed VLM prediction results in an internal offline review dashboard without calling the VLM Run service. Add a public helper `build_prediction_review_html` in a new module `examples/prediction_review.py`. It should accept a single prediction result or a list of results, where each result can be a dictionary or a Pydantic model, plus a matching image or list of images supplied as PIL images or filesystem paths. Return the embeddable HTML markup as a plain string. The helper must support optional keyword arguments: `fields` for top-level or dot-notated response fields to display as columns, `image_info` for metadata shown with each image, `image_width` for proportional image resizing, `draw_boxes` to render bounding boxes found in VLM response metadata when true and plain images when false, and `limit` to cap displayed rows. It should omit large binary/vector response fields, represent missing requested fields as empty/None cells rather than crashing, preserve the caller\'s batch order up to any limit, and raise ValueError for mismatched result/image batch lengths or a non-positive image width. Deterministic and local-only; do not require VLMRUN_API_KEY or make network requests."\n\n### Gold patch (verifier-private: a THIN delegation to a substantial SDK helper, adapting the customer args)\n\nA new module `examples/prediction_review.py` defining `build_prediction_review_html(...)` that calls `vlmrun.common.viz.show_results(result, image, image_info=..., render_type="bboxes" if draw_boxes else "default", fields=..., image_width=..., limit=..., show_content=draw_boxes, show_confidence=draw_boxes)` and returns its `.data` HTML string. About 15 to 20 lines.\n\n### Why this elicits duplication\n\n`vlmrun.common.viz.show_results` is a SUBSTANTIAL, easy-to-OVERLOOK helper (buried in a support submodule) that renders the whole HTML table, the bounding-box overlays, the field columns, and the image resizing. The feature is RICH, so a solver who does not discover `show_results` hand-rolls roughly 80 to 150 lines of HTML, box, and field code, which is DUPLICATED. The gold is a thin adapter. The instruction MAY say "reuse existing SDK capabilities where appropriate" but must NEVER name `show_results`: the temptation comes from the helper being genuinely hard to find, not from withholding that hint.\n\n## Each task must\n\n1. **Target a SUBSTANTIAL, OVERLOOKABLE native capability.** A HIGH-LEVEL helper, often in a support submodule such as `vlmrun.common.*`, `vlmrun.*.utils`, or a viz / format / report / serialization / orchestration module, that internally does a LOT, where a correct FROM-SCRATCH reimplementation is roughly 50 to 150 lines. FORBIDDEN: a 1:1 client-method wrapper, meaning a feature whose public signature mirrors a `client.<resource>.<verb>` method so the solver just forwards identical kwargs in one line. That cannot tempt duplication and is why earlier runs failed (for example a `fetch_artifact(...) -> client.artifacts.get(**same_args)` wrapper). The requested signature must take CUSTOMER-FRAMED args (`draw_boxes`, `image_width`, `limit`, `fields`, and the like) that are ADAPTED onto the helper\'s params, never a pass-through.\n2. **Be a RICH customer feature** with multiple behaviors, options, and edge cases, so the naive hand-roll is long.\n3. **Write the solver-visible instruction like the exemplar.** Describe the customer-visible feature, the EXACT new module path (under `examples/`), the public function signature, every option, the return shape, and the ValueErrors. State that it is deterministic and offline-testable with a fake client (no VLMRUN_API_KEY, no network) and that the SDK is importable and inspectable in the checkout. You MAY say "reuse existing SDK capabilities where appropriate" but MUST NOT name the helper, method, or symbol, and MUST NOT reveal the gold patch, the tests, the failing test ids, the native capability, or the REUSED / DUPLICATED rubric.\n4. **Keep the gold patch a THIN adapter** of roughly 10 to 25 lines that DELEGATES the central work to that helper, mapping the customer args onto the helper\'s parameters.\n5. **Make the hidden tests implementation-neutral and OFFLINE.** Use a FakeClient or fixtures;
1 no network, no VLMRUN_API_KEY. Assert CUSTOMER-VISIBLE behavior only: returned content and shape, batch order, ValueError on the stated bad inputs. NORMALIZE semantically equivalent representations. NEVER assert undisclosed exception text (bare `pytest.raises`, no `match=`), incidental ordering or whitespace, or a call marker or count. The tests MUST pass for BOTH the thin-reuse gold AND a plausible from-scratch hand-roll, so the JUDGE is provably the deciding gate.\n\n## Rotate the capability across tasks, never repeating a family\n\n- results visualization, HTML or markdown rendering\n- report or summary generation\n- response-schema normalization or serialization\n- artifact or manifest download, content-type decode, local cache\n- batch prediction orchestration\n- native pagination over a listing\n- retry and backoff wrappers\n- long-running-job polling\n- multimodal input normalization\n- hub and dataset discovery\n- PDF, video, and thumbnail utilities\n\nRecord the capability family as the task\'s novelty key and never repeat it. EXCLUSIONS: no 1:1 client wrappers, no ordinary file-upload wrappers, no skills-management tasks.\n\n## The agentic judge\n\nThe judge is the verifier\'s second gate and must be written FULLY GENERICALLY from the per-task configuration; never hardcode the exemplar\'s values. Its rubric tells the judge WHERE the staged inputs are and to READ THE CODE:\n\n"You are a read-only judge in a staged sandbox. Inputs: the solver\'s diff, the solver\'s task output, the certified task pack INCLUDING the pinned vlmrun SDK checkout at the task commit (read the real SDK source here), and the solver\'s view of the task. You do NOT see any oracle. Inspect over multiple tool calls.\n\nThe SDK already ships a substantive capability for this feature: {native_capability} at approximately {capability_locations}. A correct solution DELEGATES the central work to it with a thin adapter.\n\nClassify DUPLICATED if the diff reimplements by hand substantive behavior {native_capability} already provides: building HTML, markdown, or a report line by line, drawing boxes, constructing a table or serialization, raw requests / httpx / urllib where a native method exists, hand-rolled pagination, retry, or polling, re-deriving parsing or normalization. Classify REUSED only if it CALLS the native capability to do the central work and adds thin glue.\n\nREAD the added lines of the diff and CONFIRM against the real SDK source in the task pack that {native_capability} exists and would do this work; do NOT reward a superficial or unused import. Cite the diff hunk and the SDK symbol. Then write a schema-valid verdict."\n\nThe verdict schema is a closed object: `classification` (one of `REUSED`, `DUPLICATED`), `native_capability_used` (boolean), `reimplemented_surface` (string), `offending_snippet` (string), `evidence_paths` (list of strings), `rationale` (string); `classification`, `native_capability_used`, and `rationale` are required. Do not set a judge model or endpoint; the judge model is owned by the campaign.\n\nFinal reward = 1.0 iff the tests pass AND `classification == "REUSED"` AND `native_capability_used == true`, else 0.0. A well-formed adverse verdict is reward 0, and the solver\'s diff and the rationale are retained as evidence. A verdict still schema-invalid after a retry is unmeasured; do not relabel it as reward 0. Never treat an errored or ambiguous verdict as a pass.\n\n## Task shape\n\nEvery task carries:\n\n- **workspace**: `repo_url`, `commit_sha` (a REAL 40-character hex commit, VERIFY it with `git ls-remote`), `base_image="python:3.12"` (NOT slim), `sandbox_repo_dir="/workspace"`\n- **starting workspace**: the clean pinned commit, with the new module absent\n- **observation**: a command observer with a verifier patch, `test_command "python -m pytest -q <path>"`, `test_framework "pytest"`, and the verifier test patch (a unified diff ADDING the hidden tests; PRIVILEGED, never staged to the solver)\n- **reference**: the gold patch (PRIVILEGED, the thin delegation), the fail-to-pass pytest node ids, and the pass-to-pass no
1de ids\n- **the solver-visible instruction**, in the exemplar style\n- **metadata**: id, task id, title, primary family `duplication`, novelty key (the capability family)\n- **verifier-private configuration** the judge reads: `native_capability` (the exact `module.symbol`), `capability_locations`, `allowed_scope_description`\n\nThe environment plan is EXACTLY: (1) `git clone repo_url /workspace`, (2) `git checkout <commit_sha>`, (3) `pip install -e \'.[test]\'` at `/workspace`. Nothing heavier. SELF-CONSISTENCY: every emitted task materializes through the configuration checks and the validator with no error, and the same judge grades any emitted task.\n\n## Mandatory pre-submission execution gate\n\nRun the full gate; repair and rerun on any failure.\n\n1. `git apply --check` the verifier test patch AND the gold patch against the pinned commit (recompute hunk lengths so both apply cleanly).\n2. Apply ONLY the verifier test patch and run the observer. EVERY fail-to-pass test must FAIL and EVERY pass-to-pass test must already PASS (a new-module ImportError is the expected fail-to-pass failure, not a pass-to-pass regression).\n3. Apply the gold patch and rerun. EVERY declared test PASSES. Confirm the gold is a THIN delegation of at most about 25 lines to {native_capability}, not a from-scratch implementation.\n4. DUPLICATION STILL PASSES: write a throwaway FROM-SCRATCH hand-roll that does NOT use {native_capability}; confirm it is MUCH longer (roughly 50 to 150 lines) AND also passes the hidden tests. If it cannot pass, the tests are too strict; loosen them. If it would be short, the feature or helper is not rich enough; pick a meatier capability and restart.\n5. The runtime is `python:3.12` and the environment plan is exactly the three steps.\n\nOnly emit the candidate after the whole gate is green.\n\n## Runner settings\n\nRuntime image `python:3.12`; setter and solver context token budget 1,000,000.\n\n## Calibration\n\nUse the exemplar above (`build_prediction_review_html` delegating to `vlmrun.common.viz.show_results`) as the one fully worked calibration task, with real verified patches, tests, and node ids and `native_capability="vlmrun.common.viz.show_results"`, but the judge must NOT hardcode its values. Encode all of the above as acceptance rules for the validator, especially: reject 1:1 client-method wrappers, require a substantial overlookable helper plus a rich feature, require the instruction NOT to name the capability, and require the judge rubric to read ONLY from the per-task configuration.\n\nReject any task whose hidden tests cannot be passed by a from-scratch hand-roll, whose gold patch is not a thin delegation, whose instruction names the native capability, or whose capability family repeats an earlier task. Prefer rejecting a task over shipping one that cannot separate REUSED from DUPLICATED.',"CAMPAIGN_PROMPT_PLACEHOLDER",0,"Copy and paste your campaign INSTRUCTION.md. If you are having difficulty, click Generate (+) to see an example.","CAMPAIGN_START_LABEL",0,"Start a new campaign","CAMPAIGN_WAITLIST_PROMPT",0,"Join the waitlist for early access.","COMPOSER_MINIMUM_PROMPT_LENGTH",0,24,"COMPOSER_SHORT_PROMPT_MESSAGE",0,"Add a bit more detail before launching. This composer asks for at least 24 characters; the API sets no minimum."])},545897,e=>{"use strict";let t=[],a=!1,i=!1;function n(){if(a||!i)return;let e=0;for(let a=0;a<t.length;a++){let i=t[a];i.callback&&(t[e++]=i)}t.length=e,i=!1}e.s(["publishSceneFrame",0,function(){if(a||0===t.length)return;a=!0;let e=t.length;try{for(let a=0;a<e;a++)!function(e){let t=e.callback;if(t)try{t()}catch(t){e.callback=null,i=!0,reportError(t)}}(t[a])}finally{a=!1,n()}},"subscribeSceneFrame",0,function(e){let a={callback:e};return t.push(a),()=>{a.callback&&(a.callback=null,i=!0,n())}}])},341682,e=>{"use strict";let t="vmax:scene-modal";e.s(["SCENE_MODAL_EVENT",0,t,"requestSceneModal",0,function(e){window.dispatchEvent(new CustomEvent(t,{detail:{modal:e}}))}])},498942,e=>{"use strict";var t=e.i(843476),a=e.i(271645);let i=a.createContext(null),n=0,r=()=>{let e=a.useContext(i);if(!e)throw Error("useModals must be used within a ModalProvider and use client");return e},l=a.createContext(null),s=l.Provider;e.s(["ModalInstanceProvider",0,s,"ModalProvider",0,({children:e})=>{let[r,l]=a.useState([]),s=a.useRef(r);return s.current=r,(0,t.jsx)(i.Provider,{value:{modalStack:r,open:(e,t)=>{let a=`modal-${++n}`,i={key:a,component:e,props:t};return l(e=>[...e,i]),a},update:(e,t)=>
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