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Research

Papers, results and breakthroughs that change what AI can do, explained clearly.

29 stories

All Research stories · 29

Research · AnalysisSakana AI's Multi-Layered Review: a Claude-based peer-review system that finds planted errors, and where it falls shortSakana AI's Multi-Layered Review (MLR), described in a TMLR paper, uses three Claude agents to review ML papers. On a new benchmark of 1,164 planted contradictions it caught more than 70% of errors in papers' main claims, about four times the best prior system. On real withdrawn papers and manipulated submissions, the gains shrink.Oct 13·7 min read

Research · AnalysisOpenAI's Navier–Stokes claim, explained for builders: the verifier is the storyOpenAI says 10,000 AI agents found a blowup in the Navier–Stokes equations and proved it in Lean. What is checked, what isn't, and what it means for agent builders.Oct 9·6 min read

Research · ExplainerHow to read a system card: a builder's guide to AI safety documentationSystem cards run to hundreds of pages. What the sections mean, which safety frameworks they report against, how to read eval tables critically, and a checklist.Oct 9·7 min read

Research · ExplainerContext windows vs memory vs RAG: what each one actually doesA 1M-token window, a chatbot that "remembers you" and a retrieval pipeline solve different problems. Here's how each works and when to use which.Oct 9·7 min read

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