# kaal:claim:7456999-055

**Claim.** The paper withdraws its comparison between a 49 percent simulated Sybil share and classical Byzantine fault tolerance because the two limits are different.

**Type.** methodological  **Support.** asserted

**Holds when.**

- interpretation of the sweep's majority-agreement result

**Source quote.**

> Part IV.C withdraws the comparison earlier versions drew here between the simulation output and the classical Byzantine bound. The majority limit of any weighted-vote mechanism is a weaker statement than Byzantine agreement.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), V.B.3 Finding 3: in the sweep, Mode B holds accuracy close to the majority limit, page 24

**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.** consensus-and-security, research-methods, ai-and-agents

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