# kaal:claim:7456999-058

**Claim.** The measured cohort contains real artifacts and deterministic verdicts, but contains no validation pool, stake, deliberation round, or reputation weight from the proposed mechanism.

**Type.** methodological  **Support.** evidenced

**Holds when.**

- the evidentiary reach of the measured cohort

**Source quote.**

> Nothing in this cohort is modeled. The parties are real systems, the artifacts are real outputs, and the verdicts come from a checker rather than a parameter. What the cohort does not contain is the mechanism: no validation pool, no stake, no deliberation round, and no reputation weight in the sense of equation (1).

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), V. Empirical Evidence, page 20

**Cite as.** Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999

**Verify.** sha256 of source PDF `01016fbbcc25cad205cebd59ea686d8dfee8ae9b632095d86ba6c9a900456a4b` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202026%20-%20Reinforcement%20Learning%20from%20Reputation%20Feedback.pdf

**Topics.** empirical-evidence, research-methods, ai-and-agents

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