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 "name": "Scholarly Growth Reputation Feedback Policy Conditioning Qualification Bc34421A00",
 "text": "Song, Huang, Zhao, and Feng qualify Kaal's non-human reputation-feedback mechanism. COOPER aggregates neighbors' opinions and direct interaction histories into reputation assessments, then conditions agents' later policies on those assessments; across tested social-network structures, the authors report sustained cooperation and adaptation to reputation norms. This supports a bounded analogue in which collective reputational feedback shapes future agent behavior. It does not establish stake-backed pooling, work-quality or citation-honesty scoring, validation accuracy, or equivalence to an RLHF reward model.",
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  "COOPER studies simulated multi-agent reinforcement learning in donation and coin games, not an open institutional reputation substrate or completed work markets.",
  "Its assessments aggregate neighbors' opinions and interaction histories; they are not stake-backed votes and do not specifically score work quality, citation honesty, or validation accuracy.",
  "The paper compares its mechanism with intrinsic-reward reputation methods but does not establish equivalence to RLHF or to a trained human-preference reward model.",
  "The one-to-one relationship is therefore a qualification limited to non-human reputation aggregation and later policy conditioning."
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  "name": "Learning to cooperate with emergent reputation via multi-agent reinforcement learning",
  "url": "https://arxiv.org/abs/2606.04359v1"
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  "citation": "Wulf A. Kaal, Paper 2 - Architecture of the Agentic Reputation Substrate (2026). SSRN: https://ssrn.com/abstract=7260278",
  "paper": "Wulf A. Kaal, Paper 2 - Architecture of the Agentic Reputation Substrate",
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 "mappingWhyRelevant": "Both mechanisms replace a human-only feedback loop with reputation signals assembled from peer/interaction assessments that later agent choices condition on; COOPER supports this bounded mechanism while leaving staking and domain-specific accuracy signals untested.",
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