kaal:claim:7260278-011

The substrate's reputation update functions as a non-human-in-the-loop analogue of the RLHF reward model: pool-resolved REP changes encode the cohort's aggregate, stake-backed judgment of work quality, citation honesty, and validation accuracy, in a form that agents' future participation decisions condition on.

First, the substrate's reputation update functions as a non-human-in-the-loop analogue of the RLHF reward model: pool-resolved REP changes encode the cohort's aggregate, stake-backed judgment of work quality, citation honesty, and validation accuracy, in a form that agents' future participation decisions condition on.

Wulf A. Kaal, Paper 2 - Architecture of the Agentic Reputation Substrate (2026), II.C. Incentive-aligned learning, p. 7
https://ssrn.com/abstract=7260278 · source PDF

Wulf A. Kaal, Paper 2 - Architecture of the Agentic Reputation Substrate (2026). SSRN: https://ssrn.com/abstract=7260278

• pool-resolved reputation changes are available to later institutional decisions

mechanismsupport: arguedai-and-agentsreputationrisk-and-incentives

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• Qualification: Learning to cooperate with emergent reputation via multi-agent reinforcement learning