PhAI Labs

Headlines · 2

1. 9h
   PhAI Labs releases DiscoveryRSI research agent and 27B model

   PhAI Labs, working with several universities and research institutions, proposed DiscoveryRSI and trained DiscoveryRSI-27B using a hypothesis-credit attribution mechanism, releasing the paper, code and models. The system stores evolving hypotheses and test plans in a research hypothesis tree and is paired with DFM-Bench, which evaluates hypothesis revisions, research paths and whole research episodes. The team reports that DiscoveryRSI's group scored 6.3% in a literature cross-check, the highest of five model groups, while the Claude Opus 5 group scored 2.3% and ranked last; it cautions that group sample sizes differ and the comparison is descriptive, so it does not prove the hypotheses were adopted or validated by later research.

   机器之心 · 🔥 9
2. Oct 12
   PhAI Labs releases RSI-Anything world-model framework for recursive self-improvement

   On October 12, PhAI Labs published a technical report, "RSI-Anything: Unlocking Recursive Self-Improvement with World Models," proposing a cross-domain framework in which a world model predicts the outcomes of candidate actions and real feedback is used to update both the policy and the predictor, extending its earlier JEPA-Anything prediction-learning line. Reported results include code repair on CodeContests with ReasonFlux-Coder-7B held fixed (success rate 26.3% to 31.2%), a roughly 18.4% relative gain on the OSWorld GUI benchmark, laboratory planning on LabHorizon where a 4B policy model improved from 21.80% to 46.01% over five generations, and Suzuki–Miyaura chemistry optimization reaching a 96.15% average best observed yield after 40 experiments versus 82.16% for Bayesian optimization. The report also describes an immunology workflow that built an immune world model and produced a candidate combination-intervention hypothesis involving SIRPα inhibition and IL-36γ axis modulation, which remains untested.

   机器之心 · 🔥 0

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