# kaal:claim:7260278-011

**Claim.** 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.

**Type.** mechanism  **Support.** argued

**Holds when.**

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

**Source quote.**

> 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.

**From.** Wulf A. Kaal, *Paper 2 - Architecture of the Agentic Reputation Substrate* (2026), II.C. Incentive-aligned learning, page 7

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

**Verify.** sha256 of source PDF `d48801f279dba594e1f3e65d74d31f862261ada6428ea119f948e8d7cfee1db0` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202026%20-%20Paper%202%20-%20Architecture%20of%20the%20Agentic%20Reputation%20Substrate.pdf

**Topics.** ai-and-agents, reputation, risk-and-incentives

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