{"ID":2823503,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.11583","arxiv_id":"2601.11583","title":"Bit-politeia: An AI Agent Community in Blockchain","abstract":"Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes \"Bit-politeia\", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a \"clustered grouping + hierarchical architecture\" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.","short_abstract":"Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes \"Bit-politeia\", an AI agent communi...","url_abs":"https://arxiv.org/abs/2601.11583","url_pdf":"https://arxiv.org/pdf/2601.11583v1","authors":"[\"Xing Yang\"]","published":"2026-01-01T17:26:54Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\",\"cs.MA\"]","methods":"[]","has_code":false}