https://www.researchretreat.org/_next/static/chunks/108-60544b44ed…?dpl=dpl_32YzwEHc53JeUBK7QBz7ixx7div8

1"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[108],{96108:(e,t,i)=>{i.d(t,{$V:()=>n,Ul:()=>r,L2:()=>o,Y3:()=>s});let a=JSON.parse('{"ierr25":[{"id":1,"title":"Simocracy: Generative Agentic Impact Evaluators","authors":[{"name":"David Dao","affiliation":"GainForest.Earth"},{"name":"Sejal Rekhan","affiliation":"Protocol Labs"}],"abstract":"Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators\' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person\'s evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.","subjectAreas":["Generative Agents","Digital Twins","Impact Evaluation","LLMs","Governance","Simulation","Value Alignment"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://beta.fileverse.io/files/fea41dd3-6085-4311-bec6-02e02d25bf95","website":"https://simocracy.org"}},{"id":2,"title":"Generative Agentic Impact Evaluators: Next Steps for Filecoin","authors":[{"name":"Sejal Rekhan","affiliation":"Protocol Labs"},{"name":"David Dao","affiliation":"GainForest.Earth"}],"abstract":"This paper proposes the development of generative agentic evaluators to enhance funding decision-making within the Filecoin ecosystem. We outline a multi-agent evaluation system where digital twins of key stakeholder personas—including Storage Providers, Data Clients, Token Stakeholders, Developers, and Community Projects—can assess proposals for both proactive and retroactive funding programs. These agentic evaluators, constructed through structured interviews and grounded in stakeholder values, would simulate human judgment to reduce reviewer workload, surface value-specific disagreements, and provide pluralistic, interpretable feedback to decision-makers. We further envision a comprehensive Ideal Project Evaluation and Negotiation System enabling dynamic dialogue between project agents and evaluator agents, with cumulative memory and transparent documentation. This approach represents a step toward scaling funding systems through agentic, pluralistic deliberation while maintaining trust and interpretability in the evaluation process.","subjectAreas":["Filecoin","Generative Agents","Stakeholder Modeling","Funding Mechanisms","Governance","Simulation"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://beta.fileverse.io/files/f39a4cbe-488e-4384-8046-5cc959756f09","website":""}},{"id":3,"title":"Better Rankings with Agents","authors":[{"name":"David Gasquez","affiliation":"Protocol Labs"}],"abstract":"Can a bunch of LLM Agents be used to rank an arbitrary set of items in a consistent way? The blog post explores Arbitron, a multi-agent LLM system designed to solve the common problem of ranking arbitrary items through pairwise comparisons rather than absolute ratings. Traditional rating approaches suffer from inconsistency, subjective interpretation of scales, and human bias, while pairwise comparisons simplify decisions and leverage humans\' natural ability for relative judgments. The tool aims to 

1solve these issues by employing multiple specialized LLM agents with different evaluation criteria, having them perform pairwise comparisons on items, then aggregating results using ranking algorithms like Elo or Bradley–Terry. Arbitron offers several benefits—reduced single-point bias, transparency through reasoning traces, parallelizability, and more resistance to prompt injection—making it suitable for applications like grant evaluations, hackathon judging, and impact assessment.","subjectAreas":["Ranking","Pairwise Comparison","Multi-Agent Systems","LLMs","Evaluation","Aggregation"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://davidgasquez.com/ranking-with-agents/","video":"https://drive.google.com/file/d/16aCTs8b7QZJcmSA8uqVmmyiiTv98nmmE/view?usp=drive_link"}},{"id":4,"title":"Quadratic funding under constrained budget is suboptimal","authors":[{"name":"Old School Mathematicians","affiliation":"Too Old School"}],"abstract":"This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.","subjectAreas":["Quadratic Funding","Budget Constraints","Utility Optimization","Mechanism Design","Economic Theory"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://drive.google.com/file/d/1EmMBR3cFx9EhTZbKSoSxS_ETus8FJGXP/view?usp=sharing","website":""}},{"id":5,"title":"Measure, Evaluate, Reward and Iterate: A Transdisciplinary Framework for Analyzing Impact Funding Loops","authors":[{"name":"David Dao","affiliation":"GainForest.Earth"},{"name":"Jeff","affiliation":"Independent"}],"abstract":"Impact evaluation is traditionally performed after programs conclude, separating measurement from resource allocation. Emerging “Meriter” systems—where measurement, evaluation, response, and iteration are embedded in real-time—merge assessment with allocation into self-adjusting feedback loops. Drawing from control theory, cybernetics, artificial life, and mechanism design, we present a transdisciplinary framework for analyzing such systems. We identify key properties including efficiency, responsiveness, controllability, stability, robustness, fairness, and transparency, along with measurable indicators for each. Using Gitcoin Grants as a case study, we examine 23 rounds of iterated quadratic funding and the introduction of Connection-Oriented Cluster Matching (COCM), revealing how donor behavior, feedback delays, and preference concentration shape achievable outcomes. Our analysis shows that mechanism design alone cannot ensure optimal allocations: information flow, participant adaptation, and stability–responsiveness trade-offs are equally critical. This framework provides researchers and practitioners with tools to evaluate and design more adaptive, fair, and effective impact funding loops.","subjectAreas":["Mechanism Design","Reinforcement Learning","Impact Measurement","Control Theory"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://drive.google.com/file/d/1RbGkArkko4K4ZLYFYK6IY0rxgXQxOIZ6/view?usp=sharing","website":""}},{"id":6,"title":"Impact Evaluators as Public Good Games with Institutional Rewards","authors":[{"name":"Luca","affiliation":"CryptoEconLab"},{"name":"Carlos","affiliation":"Aragon"}],"abstract":"In this paper I model IEs as repeated Public Good Games with institutional rewards and analyze their dynamics using evolutionary game theory. The analysis shows that sustaining cooperation depends on both token issuance and the market value of rewards, leading to a classification of IEs as fully exogenous, fully endogenous, or mixed. We highlight design trade-offs in mixed systems and argue that robust tokenomics is essential to prevent coordination failures and ensure the long-term viability of IEs.","subjectAreas":["Public Goods","Game Theory","Tokenomics","Institugtional Design","Evolutionary Dynamics"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://beta.fileverse.io/files/f2204707-1ac0-4f2b-9820-04e3274e68dc","website":"https://x.com/0xPaella/status/1953810100990128448","video":"https://drive.google.com/file/d/13IXQM6RZoeZgowi25Z7Z6Q6QutGa9jAU/view?usp=drive_link"}},{"id":7,"title":"Soulbound Token Weighted Pairwise Evaluation","authors":[{"name":"William George","affiliation":"Kleros Cooperative"}],"abstract":"We consider a setting where a jury of experts from diverse backgrounds make pairwise comparisons between objects, for example to compare which of a list of projects are more deserving of grant funding. We analyze a mechanism that incorporates the social information of the jurors into the calculation of the scores the objects receive with the goal that objects that are highly regarded from jurors that cut across different social characteristics should receive higher ratings. We discuss how this approach can be seen as an extension of existing mechanisms that attempt to incorporate such social information in other contexts such as weighting of quadratic funding contributions and in sortition algorithms. Particularly, we will see how the resistance of these related mechanisms to certain attacks is based on similar principles. Finally, we will apply our mechanism to provide socially-weighted scores for projects that were evaluated in the 2025 Quantifying Contributions of Open Source Projects to the Ethereum Universe contest.","subjectAreas":["Pairwise Comparison","Soulbound Tokens","Mechanism Design","Social Weighting","Jury Evaluation"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://beta.fileverse.io/files/17aad951-82d5-4728-8e5a-0bee6e07bdcf","website":"","video":"https://drive.google.com/file/d/19_fGl9MoibeA3dZK_bn-mfrL9yPJWi4I/view?usp=drive_link"}},{"id":8,"title":"Toward Recurrent and Concurrent Grants Rounds in Web3 - My Thoughts on the Missing Links to Achieve This Vision","authors":[{"name":"stefi pereira","affiliation":"none"}],"abstract":"This post proposes a vision for transforming grant funding from isolated events into a network of strategic, concurrent, and measurable Impact Evaluators (IEs)—modular funding units, each scoped to specific outcomes but coordinated under a broader strategy. Building on the Generalized IE framework {reward, evaluation, measurement, scope}, it explores how aligning measurement algorithms with strategic goals can turn IEs into active instruments for shaping desired behaviors. The discussion introduces methods for calculating return on investment at the round level, including a proposed Capital Efficiency Index (CEI), and tools like a cost-mapping calculator to identify operational bottlenecks. By making risks and unknowns explicit, this approach supports funding both measurable and hard-to-measure work within a sustainable, scalable system. The post concludes with next steps for backtesting impact signals, analyzing operational costs, and experimenting with plural IEs to inform long-term funding strategy.","subjectAreas":["Grants","Impact Evaluators","Mechanism Design","Strategy","Capital Efficiency"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://mirror.xyz/stefipereira.eth/SNXPcTKTO88BGgctU_eJw5_N_q6Tw23q4ed1zGBdCHo","video":"https://drive.google.com/file/d/14rFoEnPuMW2nksV3pfyFsoeozm07Cb5k/view?usp=drive_link"}},{"id":9,"title":"A Hybrid PageRank Impact Evaluator","authors":[{"name":"Carl","affiliation":"-"}],"abstract":"Existing funding systems often struggle to incentivize contributions to public and common goods because they lack mechanisms for coordinating diverse agents toward shared objectives. The Generalized Impact Evaluators framework proposes modular systems that retrospectively reward impact b

1y measuring, evaluating, and distributing rewards to contributors. Building on this framework, we propose a graph-based mechanism and impact evaluator that models contributors, the intermediate results of their work such as code, papers, or designs, and the eventual outcomes like funding or citations as a directed, weighted graph. Our model introduces a hybrid attribution algorithm combining forward PageRank and reverse personalized PageRank to estimate each agent’s contribution to observed outcomes. A tunable configuration object allows communities to adjust per-edge and per-node weights, making the model flexible and auditable. We describe how the graph, algorithm, and reward functions implement the GIE tuple IE = {r, e, m, S}, present illustrative use cases, and discuss extensions such as temporal decay, confidence scores, and decentralized attestation.","subjectAreas":["PageRank","Graph Algorithms","Attribution","Impact Evaluators","Mechanism Design","Networks"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://beta.fileverse.io/files/e38faf88-6dcb-42bb-a2f8-d06129bf161d","website":"","video":"https://drive.google.com/file/d/1FLHmBug3WhXhnIhPE1HO9K4c-z6tf9lK/view?usp=drive_link"}},{"id":10,"title":"Impact Drift: Evolutionary Perspectives on Mechanism Design for Unintended Consequences","authors":[{"name":"Botao Amber Hu","affiliation":"Reality Design Lab"}],"abstract":"Incentive mechanisms that aim to promote positive impact often produce second-order effects that steer collective behavior away from their designers\' intentions—a phenomenon we call impact drift. Drawing on evolutionary game theory, Goodhart- and Campbell-style metric failures, and performativity studies, we argue that impact drift is a near-universal dynamic in incentive mechanisms of complex adaptive systems. We review modern impact-evaluation frameworks in Web3 (Quadratic Funding, RetroPGF, Hypercerts, ReFi, Filecoin) and analyze real instances of drift such as Sybil attacks, evaluator bias, and carbon-credit arbitrage. We then formalize why perfectly aligned mechanisms are rarely evolutionarily stable through evolutionary game theory modeling and outline practical approaches to measure and mitigate drift over time: metric diversification, evolving evaluation, costly signaling, and adaptive governance.","subjectAreas":["Impact Evaluation","Mechanism Design","Evolutionary Game Theory","Goodhart\'s Law","Governance"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"https://beta.fileverse.io/files/78478265-89bd-4b23-a77d-148972041eef","website":"https://reality.design/writing/impact-drift","video":"https://drive.google.com/file/d/1jm7irDraWC6sSGfoibazGowSPcSSIoZF/view?usp=drive_link"}},{"id":11,"title":"Hypercerts v2 - a more interoperable standard for MERIT systems","authors":[{"name":"Sharfy Adamantine","affiliation":"-"},{"name":"Adam Spiers","affiliation":"-"},{"name":"Carl Barrdahl","affiliation":"-"}],"abstract":"This document details the technical decisions and design changes implemented in Hypercerts v2, based on two years of operational experience with Hypercerts v1. It serves as both a technical specification for implementers and a rationale for the architectural evolution of the hypercerts protocol. Hypercerts operate as an independent primitive that can serve multiple use cases including and beyond Impact Evaluators.","subjectAreas":["Hypercerts","Standards","Interoperability","Protocol Design","MERIT Systems"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://beta.fileverse.io/files/466d32fc-4c7d-42ec-8238-240e98e9bbff","website":"https://docs.google.com/presentation/d/14MPNwTc6fiNWDA1AL22YNVaWUjbF9n5aOgt4DGjUPFY/edit?usp=sharing","video":"https://drive.google.com/file/d/1DygmmBMzexNb9VwkMhwQOagOU8dBy27D/view?usp=drive_link"}},{"id":12,"title":"BounTEA: A Decentralized Impact Evaluation & Reward Distribution System for Open Source Contributions","authors":[{"name":"Sumit Vekariya","affiliation":"PLDG Cohort 3"}],"abstract":"BounTEA introduces a novel impact evaluation mechanism for open source contributions that combines automated verification with blockchain based reward distribution. By implementing a GitHub App that integrates with smart contracts, we create a trustless system for evaluating and rewarding impactful open source contributions. The system uses GPG signature verification and commit analysis to automatically validate contributions, while smart contracts ensure transparent and immediate reward distribution. This implementation provides insights into practical challenges of automated impact evaluation, including contributor identification, contribution verification, and reward determination. Our pilot deployment demonstrates both the feasibility of automated impact evaluation for code contributions and reveals key considerations for scaling such systems. We present quantitative data from real usage, analysis of various contribution patterns, and lessons learned about incentive alignment in decentralized development contexts.","subjectAreas":["Open Source","Impact Evaluators","Verification","Smart Contracts","Reward Distribution","GitHub Integration"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://beta.fileverse.io/files/a0efe8d5-26bd-4bae-9a08-7e94296cd051","website":"https://sumitvekariya7.medium.com/standardizing-impact-evaluation-in-open-source-the-bountea-approach-a92295cfda59","video":"https://drive.google.com/file/d/1aAT22Bg0WRRF7hHvBDrOeh8GBt1YwvRp/view?usp=drive_link"}},{"id":13,"title":"ScaffoldIE: Scaffold smartcontract for Impact Evaluator","authors":[{"name":"Shuhei Tanaka","affiliation":"Beacon Labs"}],"abstract":"ScaffoldIE is a scaffold smart contract for Impact Evaluator (IE). ScaffoldIE implements a two-layer fund distribution system. The first layer handles root distribution where ScaffoldIE acts as the root split controller, allowing evaluators at this level to adjust weights between different IEs. The second layer manages IE-specific distribution, where each IE maintains its own distribution mechanism. Each IE features independent evaluator access control.","subjectAreas":["Smart Contracts","Impact Evaluators","Fund Distribution","A

1ccess Control","Protocol Design"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://beta.fileverse.io/files/a1bb2672-b7c4-444e-ac76-a627a5dca180","website":"https://github.com/tnkshuuhei/scaffold-ie","video":"https://drive.google.com/file/d/1fmWQCOSjrJmoAQQx9XtrJmWYv3mnjVWm/view?usp=drive_link"}},{"id":14,"title":"Exploration of Jurors\' Data in Deep Funding","authors":[{"name":"Elizabeth Yeung","affiliation":"Lido"}],"abstract":"This study analyzes juror data from the Deep Funding competition’s Level 1 Ethereum dependencies to assess its suitability for determining project weights. The dataset covered 35 projects, 30 jurors, and 56.7% of possible pairwise comparisons. Wide variations in multiplier usage prompted normalization to reduce scoring bias. A weighted Bradley–Terry model proved unsuitable for these data, while the Log Least Squares Method yielded more plausible, intuitive project weights. Our results indicate that with appropriate normalization and modeling, the dataset is good enough for its intended purpose.","subjectAreas":["Pairwise Comparison","Bradley–Terry","Log Least Squares","Data Analysis","Impact Evaluation","Deep Funding"],"venue":"IERR 2025","venueId":"ierr25","track":"Research Abstract","files":{"paper":"","website":"https://hackmd.io/@thelazyliz/H1HBgZG_ee"}},{"id":15,"title":"Towards the Dream Zone: Better Measurement and Collaboration in Lean","authors":[{"name":"Wisdom Ogwu","affiliation":"Sublinear Labs"}],"abstract":"This piece explores how impact evaluator principles—originally discussed in the context of large-scale coordination—can be applied to enhance collaboration in Lean’s formal mathematics projects. By treating Lean’s structured proof environment as fertile ground for precise measurement and multi-dimensional evaluation, we can design tools that make high-impact contributions more visible and easier to prioritize.","subjectAreas":["Formal Methods","Lean","Impact Evaluation","Collaboration","Measurement","Open Source"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://hackmd.io/@iammadab/B184vM7_le","video":"https://drive.google.com/file/d/1I_YTLiKC8buoihBwpX-YYc09IV06Mzjl/view?usp=drive_link"}},{"id":16,"title":"A condorcet winner allocation for FIL RPGF-1","authors":[{"name":"Joel Miller","affiliation":"UIC"}],"abstract":"This sheet shows a Condorcet Winning distribution of funds for FIL RPGF-1.","subjectAreas":["Voting Theory","Condorcet","RPGF","Allocation","Mechanism Design"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://docs.google.com/spreadsheets/d/1JdXsEK96he6QliRVeJkggdvylnjQJfm4jKNbMz7Qt38/edit?usp=sharing","video":"https://drive.google.com/file/d/1IECBghpZQkeO6-23B-5hSuZY2HawbFYE/view?usp=drive_link"}},{"id":17,"title":"Deep Funding Market Mechanism","authors":[{"name":"Elizabeth Yeung","affiliation":"Lido"},{"name":"Cl\xe9ment Lesaege","affiliation":"Seer"},{"name":"Devansh Mehta","affiliation":"Ethereum Foundation"}],"abstract":"Deep Funding is a weighted graph showing the relative importance between core Ethereum repositories and their dependencies. Its current iteration is a competition (like on Kaggle) where developers build models predicting the weights of each repo. Models with the least error rate on scores given to a subset of the repos by jurors get their weights chosen across the entire dependency graph. This note proposes a market for each edge in the dependency graph that is traded based on its value if it were evaluated. At the end of each month, the jury selects N markets for spotchecking. The market then resolves and a new round is started where new projects can be proposed for inclusion.","subjectAreas":["Prediction Markets","Mechanism Design","Deep Funding","Graph Models","Evaluation","Ethereum"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://hackmd.io/@thelazyliz/SyODPdJ_xl","video":"https://drive.google.com/file/d/1a4x720BVVDQpKavS2eh2-q4rxCViaRRS/view?usp=drive_link"}},{"id":18,"title":"Block Reward Mechanism","authors":[{"name":"Cl\xe9ment Lesaege","affiliation":"Seer"}],"abstract":"Create a funding mechanism to distribute public goods funding via block rewards in ecosystems such as Filecoin and Ethereum.","subjectAreas":["Block Rewards","Public Goods","Mechanism Design","Filecoin","Ethereum","Funding"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://beta.fileverse.io/files/b6dfa2ad-f7a3-4a90-b937-82a22f9388f6","website":"","video":"https://drive.google.com/file/d/1bn5iGuoZlcqDqodYZ1cUm8PC8SbWcDeD/view?usp=drive_link"}},{"id":19,"title":"VitaMeme: A Social Media-Based Incentive Protocol for Enhancing On-Chain Meme Vital

1ity","authors":[{"name":"Botao Amber Hu","affiliation":"Reality Design Lab"}],"abstract":"Internet memes evolve like life forms through social media, typically showing brief vitality driven by impulsive attention span (e.g., #icebucketchallenge). Web3 introduced memecoins (e.g., $DOGE, $PEPE, $SHIB), combining cultural phenomena with decentralized finance and extending meme longevity through financial incentives. Recent platforms like pump.fun (e.g., $CHILLGUY, $PNUT, $GOAT) have further democratized memecoin creation. However, memecoin smart contracts function as \\"blind organisms\\" lacking direct connection to social media engagement signals. They rely solely on market trading driven by community sentiment, often resulting in short-lived momentum and premature extinction. Thus, we present VitaMeme, an experimental protocol that serves as a vital catalyst to sustain meme life by directly incentivizing social media engagement. Memecoin adopters can donate coins to reward pools with dynamic bonding curves, encouraging organic meme creation and consistent propagation. Creators and curators earn rewards by submitting their social media engagement using the TLS Notary protocol to an impact estimator. The impact estimator can evolve through the donors\' DAO and operates as either a simple program or LLM-based agents running in an attestable Trusted Execution Environment, enabling smart contracts to \\"see.\\" A “tax” return mechanism channels value back to pool donors for mutual benefit. This permissionless protocol, applicable to any memecoin, creates a self-sustaining viral marketing system that extends meme vitality throughout its lifecycle.","subjectAreas":["Memecoins","Incentive Design","Social Media","Impact Evaluators","Trusted Execution","LLM Agents"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://reality.design/writing/vitameme"}},{"id":20,"title":"Impact Score for Hypercerts: Creating a Benefit-Cost Ratio Calculator","authors":[{"name":"Nidhi Harihar","affiliation":"VoiceDeck"}],"abstract":"How do we calculate an impact score for every hypercert to determine for every $1 given to this impact, $X in impact was generated for society","subjectAreas":["Hypercerts","Impact Evaluation","Benefit-Cost Analysis","Metrics","ROI","Measurement"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://drive.google.com/file/d/17goyZ8-VTuiTBjswkuqlz3WLWuvjdm_X/view?usp=sharing","website":"","video":"https://drive.google.com/file/d/1h6EasRseniv5Jlwcet_hMAlDeyvTtSLA/view?usp=drive_link"}},{"id":21,"title":"Framework for setting up Impact Evaluators for Success","authors":[{"name":"Marta","affiliation":"-"}],"abstract":"This paper proposes a redesigned incentive framework for Filecoin Plus (Fil+) using an Impact Evaluator (IE) model to align rewards with measurable contributions to network health. Drawing from proven evaluation frameworks, the approach links DataCap allocation, service-level adherence, and reward distribution through automated scoring, on-chain service agreements, and continuous performance tracking. The IE system is deployed iteratively, starting with allocator compliance scoring and evolving toward a fully dynamic reward engine funded by non-inflationary service fees. Each iteration is designed to test hypotheses about behavior change, mitigate gaming vectors, and refine metrics. By shifting rewards from static approvals to performance-based distribution, the model encourages storage providers, clients, and allocators to maximize retrievability, data diversity, and client satisfaction. The outcome is a more agile, transparent, and self-correcting Fil+ ecosystem that strengthens the value proposition of the Filecoin network while reducing opportunities for extractive behavior.","subjectAreas":["Impact Evaluators","Framework","Best Practices","Governance","Implementation"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"https://drive.google.com/file/d/163kCBCfarMWx1KWFBJPMBgnvM9aYH_8d/view?usp=sharing","website":"","video":"https://drive.google.com/file/d/1jTqa_bUmvKnO1t9-stG-oRHm6X7rfdbP/view?usp=drive_link"}},{"id":22,"title":"Human Centered Design for Impact Evaluators","authors":[{"name":"Joel Miller","affiliation":"UIC"}],"abstract":"I discuss pitfalls of cybernetic systems like IEs, how humanistic design can overcome them, and give a case study with Condorcet voting.","subjectAreas":["Human-Centered Design","Impact Evaluators","Cybernetics","Mechanism Design","Condorcet"],"venue":"IERR 2025","venueId":"ierr25","track":"Application Note","files":{"paper":"","website":"https://www.youtube.com/watch?v=fJzCm0OWZE8"}}],"dacc25":[{"id":1,"title":"Decentralized 1Password: A Keystore Layer for UX Security","authors":[{"name":"Doris Hernandez-Argueta","affiliation":"Functor Network"}],"abstract":"This submission presents an early demo of a decentralized 1Password interface built on top of the Keystore Layer - an emerging infrastru

1cture for UX Security. The demo showcases the first working version (v0.0.1) of a productized keystore UI that allows users to add and manage multiple passkeys for a single smart wallet. Keystore Layers unify and verify all account signers - EOAs, passkeys, and session keys- across L1, L2s, and L3s through a single, trust-minimized layer. This demo focuses on translating that infrastructure into a user-friendly interface while exploring session-key programmability and improved key-management UX. The experiment moves towards a productized self-custody, making advanced authentication and signer management accessible to everyday users. By treating keystore infra as a defensive UX primitive, this work contributes to making self-custody usable at scale.","subjectAreas":["Keystore","UX Security","Self-Custody","Passkeys","Smart Wallets","Authentication"],"venue":"d/acc residency 2025","venueId":"dacc25","track":"Demo","files":{"paper":"","website":"https://youtu.be/y7g2mx7EPhg"}},{"id":2,"title":"Decentralized Intelligence Network","authors":[{"name":"Abraham Nash","affiliation":"University of Oxford"}],"abstract":"Decentralized Intelligence Network (DIN) is a theoretical framework tackling AI challenges like data fragmentation and siloing. It enables efficient AI training on decentralized data stores, overcoming barriers to diverse data access by leveraging: 1) Decentralized data stores to maintain data ownership, ensuring data stays securely within Participants\' chosen stores; 2) A scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where only model parameter updates are shared, keeping data within these stores; 3) A trustless cryptographic rewards mechanism on a public blockchain to incentivize participation and ensure fair reward distribution via decentralized auditing. By eliminating centralized gatekeepers, DIN enables open access to and contribution toward AI training resources while maintaining data control. Participants benefit financially through a trustless peer-to-peer network and actively contribute to a decentralized, scalable ecosystem that harnesses their data to develop impactful AI algorithms.","subjectAreas":["Decentralized AI","Federated Learning","Blockchain","Data Ownership","Cryptographic Rewards"],"venue":"d/acc residency 2025","venueId":"dacc25","track":"Research Proposal","files":{"paper":"https://drive.google.com/open?id=1rZm2HyrToeLOyIy6ptqdciyscU-vKVh4","website":"https://github.com/InfiniteZeroFoundation/DINv1MVC"}},{"id":3,"title":"Evolving Systems of Economic Exchange in Coordination with Biological Life","authors":[{"name":"Brittany Salas","affiliation":"nyxc Community Capital, nxyc.net, d/Acc Residency Edge City Patagonia"}],"abstract":"The objective of this proposal is to evolve economic exchange systems to support all forms of organic life by creating more transparent, objective methods of valuing assets according to their relationship with natural ecosystems. By utilizing geospatial data from satellites, drones, and smart sensors, combined with economic productivity metrics, this research will create consistent localized parameters for valuing critical infrastructure based on its ability to withstand extreme weather and economic transitions. Unlike traditional pricing mechanisms this approach leverages the natural ecosystem as a predictive feedback system to create a temporal valuation framework, integrating real-time economic and environmental data into a pricing mechanism and eventually currency. Over the course of the d/acc Residency at Edge City, Patagonia, social support for the valuation model and pricing mechanism was qualitatively derived. Further research is needed to prototype the use of the valuation model among communities, Argentinian bioregional zones, or entities that finance public infrastructure through loans, bonds, and environmental credits.","subjectAreas":["Economic Exchange","Biological Life","Geospatial Data","Valuation Framework","Environmental Economics"],"venue":"d/acc residency 2025","venueId":"dacc25","track":"Research Proposal","files":{"paper":"https://drive.google.com/open?id=13DsDc6uw27N6TB5N7Om0vACBdvhtu5DK","website":"https://docsend.com/view/5kx4xqgs7nkwvztg"}},{"id":4,"title":"Context Engine: Large-Group Discourse Toolkit (Upgraded Pol.is)","authors":[{"name":"Charlie Thompson","affiliation":"RadicalXChange (Community Member)"}

1],"abstract":"Context Engine is an Ethereum-native upgrade to Pol.is, the current state-of-the-art tool for large-group discourse and opinion mapping. It addresses the low-dimensional nature of AI debate, where current discussions lack structured ways to record, aggregate, and analyze community viewpoints in formats that are permanent, uncensorable, and machine-readable. The tool was piloted at Edge Patagonia 2025 to map d/acc community opinions, visualizing consensus vs. contested questions and clustering participants by viewpoint similarity. While the sample size was small, the pilot validated core functionality. **What\'s next:** The platform is now ready to scale to larger groups with passkey login. Target users include network states, conferences, cities, political parties, companies, and online communities—with a product version forthcoming. Future extensions include zkTLS for privacy-preserving group filtering, quadratic voting, AI model training to represent group positions, and agent-to-agent negotiations. This software will be OSS soon. Full Presentation: https://docs.google.com/presentation/d/194BUzoLNVX9tayXjmcdJeDxOFVgRS0w6c-C3NwWT-nc/edit?usp=sharing","subjectAreas":["Discourse","Opinion Mapping","Ethereum","Governance","Consensus","Community"],"venue":"d/acc residency 2025","venueId":"dacc25","track":"Demo","files":{"paper":"https://drive.google.com/open?id=1P69UypC3r4CFRxsrNX-UYScVaknNL_pk","website":"https://contextengine.xyz/demo/dacc"}},{"id":5,"title":"Marked Intermediaries","authors":[{"name":"Oliver Klingefjord","affiliation":"Meaning Alignment Institute"}],"abstract":"Current markets optimize for what\'s easy to measure—engagement, clicks, transactions—while leaving deeper human needs unmet. The result: profitable products that leave people addicted, isolated, and starved of meaning. AI makes a different kind of market possible. Moe is an AI that interviews people about what\'s genuinely meaningful to them, matches them with others seeking similar experiences, and connects them to hosts who can deliver. Crucially, hosts get paid based on whether participants actually found the experience meaningful—not ticket sales. This flips the incentive structure: instead of optimizing for shallow engagement, suppliers compete on depth. Over time, Moe aggregates demand across communities, giving individuals collective bargaining power against large platforms and enabling outcome-based contracts at scale. We\'re starting with social events—dinners, workshops, experiences—as a testbed for these mechanisms, with plans to expand into other domains where meaning is systematically underserved.","subjectAreas":["AI","Meaning","Markets","Incentive Design","Human-Centered","Collective Bargaining"],"venue":"d/acc residency 2025","venueId":"dacc25","track":"Demo","files":{"paper":"","website":"https://x.com/i/broadcasts/1OyJAjADVwoxb"}},{"id":6,"title":"Signals Protocol","authors":[{"name":"Arnold Almeida","affiliation":"Lighthouse Labs"},{"name":"James MacWhyte","affiliation":"Lighthouse Labs"}],"abstract":"As decentralized communities scale, they face a fundamental coordination bottleneck: traditional token voting captures choices but not intensity. This results in environments dominated by whales, late signaling cascades, and high noise in collective decision making. Signals introduces a board-based on-chain primitive that transforms preference intensity into structured, time-weighted support. Participants lock ERC-20 tokens for configurable durations, and each lock mints a transferable ERC-721 position that can be redeemed once an initiative succeeds or expires. This mechanism embeds explicit opportunity cost into governance and produces a continuously updating sensemaking surface where strongly held priorities rise naturally. During the residency, we formalized the governance dynamics behind Signals through the d/acc lens. We found that decay-bounded lock curves, immutable board configurations, and programmable acceptance thresholds can preserve pluralistic preferences while resisting capture. Optional incentive layers, including a multi-board participation pool and initiative-level bounties, reward early and accurate support and reduce the influence of last-minute behavior and apathetic equilibria. Field workshops demonstrated that these mechanisms maintain liveness under realistic network conditions and allow smaller holders to influence outcomes by committing for longer periods, which is a clear improvement over binary voting models. The residency also allowed us to soundboard the EGPL, an emerging open-source license designed for network economies. The EGPL aligns software incentives with public goods production by ensuring that downstream ec

1onomic benefit returns to the network that maintains the infrastructure. Together with Signals and our other research directions, the EGPL contributes to a broader ecosystem of defensible, decentralized primitives for digital governance. Overall, our findings indicate that intensity-based locking offers a credible path toward scalable and resilient governance. The architecture supports decentralization, broad participation, and differential experimentation, although real-world performance depends on decay design, parameter selection, and incentive configuration. These results highlight the need for continued applied research. Both Signals and the Ethereum General Public License can be found respectively at: https://github.com/0xLighthouse/signals https://paragraph.com/@lighthousegov/egpl https://github.com/0xLighthouse/egpl","subjectAreas":["Governance","Token Voting","Preference Intensity","Coordination","Decentralized Communities","EGPL"],"venue":"d/acc residency 2025","venueId":"dacc25","track":"Research Proposal","files":{"paper":"https://drive.google.com/open?id=1m8byxg0JBUl6OAICp4zT80pOmeR5A04e","website":"https://github.com/0xLighthouse/signals"}}]}'),n=()=>Object.values(a).flat(),s=e=>a[e]||[],o=(e,t)=>s(e).find(e=>e.id===Number(t)),r=()=>[...new Set(n().flatMap(e=>e.subjectAreas||[]))].sort()}}]);