# kaal:claim:7456999-031

**Claim.** For each prompt and candidate output, RLRF selects a validation pool by verifiable random function from agents holding relevant expertise tags.

**Type.** design  **Support.** argued

**Holds when.**

- the proposed validation-pool architecture

**Source quote.**

> For each coordination task (prompt and candidate output ), a validation pool is selected by verifiable random function (Micali, Rabin, and Vadhan 1999) from the set of agents holding relevant expertise tags. VRF-based selection ensures that pool composition is cryptographically verifiable ex post and resistant to ex-ante manipulation by adversarial agents.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), III.A Validation Pool Architecture, page 11

**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.** ai-and-agents, consensus-and-security, governance-design

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