# kaal:claim:7456999-030

**Claim.** The paper's remaining residue is a combination rather than a primitive, and it is not compared with the nearest sequential peer-prediction, prediction-market, staking, or debate mechanisms.

**Type.** failure  **Support.** asserted

**Holds when.**

- the novelty claim remaining after the paper's literature concessions

**Source quote.**

> Even that residue is a combination rather than a primitive, and the paper does not clear it against the mechanisms nearest to it. Sequential peer prediction, prediction markets with a revision stage, staking and slashing designs from distributed consensus, and iterated debate all combine a free elicitation stage with a costly commitment stage in some form, and this paper does not compare against any of them.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), II.D The Nearest Literatures, and What Is Left Over, page 10

**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.** research-methods, ai-and-agents, economics

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.