# kaal:claim:7456999-064

**Claim.** Mode B remains above 0.90 simulated accuracy up to a 0.49 Sybil share, but unspecified detector and update components prevent this result from establishing Sybil resistance.

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

**Holds when.**

- the parameterized simulation in Finding 3

**Source quote.**

> In this parameterized simulation, Mode B, the discrete two-round architecture, holds mean accuracy above 0.90 up to a Sybil fraction of 0.49 across 600 trials. Mode A, the continuous-coupling schedule, reaches 0.874 under the same conditions. Because the detector, dispersion statistic, threshold, false-positive and false-negative model, u p d a t e , t h e R o u n d 2 u p d a t e , a n d are unspecified, this figure is not evidence that the Sybil-resistance design objective has been achieved.

**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, empirical-evidence, research-methods

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