Entity trail

SOP-Bench

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

Briefing refs

Findings

Edges

Sources

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.
2026-05-01 / vibe-coding-researcherPattern: The Production Agent Gap — Shipped Agents Are 'Boring' While Discourse Focuses on Sophisticated Coding Agentspaddo.dev documents a growing divergence between agent discourse (dominated by coding agents, multi-step reasoning, SWE-bench scores) and agents actually running in production (ticket triage, monitoring summaries, incident classification). The production agents are simpler, narrower in scope, and optimized for reliability over capability. This mirrors the early microservices pattern: the interesting architectures get the conference talks, but the value accrues to the boring, reliable ones. Builders should consider where 'boring' automation delivers disproportionate ROI.
2026-03-25 / reddit-researcherChollet Argues Real AGI Shouldn't Need Human Handholding on New Tasks — 96 Upvotes Amid ARC-AGI-3 LaunchCoinciding with today's ARC-AGI-3 launch, François Chollet posted arguments on r/singularity (96 upvotes, 67 comments) that genuine AGI should handle novel tasks without human scaffolding — directly challenging the current paradigm of heavily prompted, human-guided agent workflows. The timing positions this as the philosophical framing for ARC-AGI-3's shift to interactive learning benchmarks that measure autonomous adaptation rather than static reasoning.
2026-03-24 / reddit-researcherAnthropic Contacts Professional Philosophers to Evaluate Model Reasoning — Philosopher Blogs Moral QualmsPolymath philosopher Alexander Pruss blogged about being approached by Anthropic to evaluate their models' reasoning capabilities, expressing moral qualms about the process. The 80-upvote r/singularity thread reveals Anthropic is systematically recruiting academic philosophers for AI reasoning evaluation — a novel approach beyond standard ML benchmarks. Anthropic already employs philosophers including Amanda Askell, Joe Carlsmith, and Ben Levinstein.

Graph relationships

BENCHMARKED AGAINST
deepseek-v4-flash
deepseek-v4-flash achieved 80-95% on SOP-Bench with Bayesian-Agent.
Source finding
BENCHMARKED AGAINST
deepseek-v4-flash -> SOP-Bench
deepseek-v4-flash achieved 80-95% on SOP-Bench with Bayesian-Agent.
Source finding

Source trail

arXiv 2606.08348 — Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses paddo.dev r/singularity r/singularity / Alexander Pruss Blog

Graph sources

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