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.