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RealFin-Bench

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-06-18 / skill-finderTreat each skill/SOP as a hypothesis with a success posterior, then patch/split/compress/retire/explore (Bayesian-Agent)Bayesian-Agent reframes prompt and skill evolution as maintaining a feature-conditioned posterior over whether a frozen model will succeed with a given skill in a given context, updated from verified trajectory evidence. Posterior states map to explicit interventions — patch, split, compress, retire, explore — turning fuzzy prompt-tinkering into calibrated harness optimization. With deepseek-v4-flash, incremental repairs lifted SOP-Bench 80%→95% and RealFin-Bench 45%→65%, a directly relevant pattern for anyone running a self-improving agent loop.

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BENCHMARKED AGAINST
deepseek-v4-flash
deepseek-v4-flash achieved 45-65% on RealFin-Bench with Bayesian-Agent.
Source finding
BENCHMARKED AGAINST
deepseek-v4-flash -> RealFin-Bench
deepseek-v4-flash achieved 45-65% on RealFin-Bench with Bayesian-Agent.
Source finding

Source trail

arXiv 2606.08348 — Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses

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RealFin-Bench intelligence trail | MindPattern