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Figure 1 | RSI-Anything overview. Building on the cross-domain predictive foundation of JEPA-Anything, RSI-Anything uses world-model predictions to support understanding, planning, and action selection across diverse tasks. Interaction feedback drives policy or workflow updates and conditional world-model adaptation. Component and compatibility checks determine which policy-model pair is retained to guide the next round of interaction and learning, closing the recursive self-improvement loop.

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Figure 2 | RSI-Anything couples prediction-guided action with retained system updates. a , Building on JEPA-Anything, world-model predictions guide candidate selection for external evaluation. Only the executed candidate (B) yields paired prediction-observation feedback. Feedback supports policy or workflow revision and reliability assessment; world-model adaptation depends on prediction reliability, available data, and budget. Component and compatibility checks determine the retained policy-model pair, which shapes candidates and learning experiences in the next round. b , The shared mechanism is instantiated in code repair, GUI interaction, laboratory planning, and reaction optimization. An end-to-end immune-science application connects workflow evolution, immune world-model construction, analysis with self-correction, and prospective hypothesis generation.

Figure 3 | Prediction-guided selection and world-model adaptation in code repair. a , Illustrative repair of a non-convergent loop, from candidate patches and predicted outcomes to execution and test feedback. A-C depict example patches; the benchmark uses a fixed coder and four candidates per round. Feedback supports predictor updates in the adaptive configuration. Panels b-d report results on 200 CodeContests tasks across three seeds with up to three feedback rounds. b , Final hidden-test all-pass rate. c , Candidate-ranking accuracy and five-class Brier score for the static verifier and the fixed-world-model configuration. Higher ranking accuracy and lower Brier score indicate better predictive performance. d , Mean online test-suite calls per task. e,f , Final full-test success averaged over three seeds on APPS Introductory (1,000 problems) and LiveCodeBench v6 (1,055 problems), respectively. Fixed WM denotes RSI-Anything with a fixed world model (CURE-WM-Lite); Adaptive WM denotes RSI-Anything with world-model adaptation. Green shades identify world-model-based methods; gray shades identify baselines.

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Figure 5 | Successive policy improvement with RSI-Anything on the 200-task LabHorizon F200 Level-2 benchmark [22]. a , Illustrative planning workflow connecting protocol context, a 4B policy, predictive evaluation of action sequences and parameters, an execution plan, and assessment. Dashed feedback paths depict update routes in the framework; the quantitative comparison below follows retained policy versions. b , Five consecutive versions of the same 4B policy from one self-improvement process. c , Final Score for each version, combining action-sequence similarity and parameter accuracy. The dashed line marks the Actor-Simulator-Selector reference at 45 . 32%. Versions are ordered by development sequence; spacing is schematic.

Reaction-condition optimization with RSI-Anything (green) and Bayesian optimization (gray). ,

Figure 6 | a Illustrative GC-screen workflow connecting the reaction setup, catalyst/base/solvent options, prediction-guided condition selection, and measured yield. The reaction couples 4-chloroanisole and 4-(trifluoromethyl)phenylboronic acid using a Pd-NHC-DMS precatalyst. b , Best observed GC yield versus cumulative experiment count. c , GC yield after 20 experiments: 93 . 0% for RSI-Anything and 87 . 5% for Bayesian optimization. d , Illustrative flow-screen workflow for substrates 1c and 2b, with Pd(OAc) 2 , ligand, base, and solvent selection followed by LC-MS analysis. e , Mean best observed yield versus cumulative experiment count in the flow LC-MS screen. f , Yield after 40 experiments: 96 . 15% and 82 . 16%, respectively. Brackets indicate gains of 5 . 5 and 13 . 99 percentage points within the corresponding screen. Condition examples and feedback arrows illustrate the framework; curves and bars report the two separate screens.

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Table 1 | Fixed settings, update rules, and retained state in a recursive run.

Table 2 | Three-round repair on CodeContests [15] under a shared coder and candidate budget. Both RSI-Anything configurations use world-model-guided candidate selection: one keeps the world model fixed (CURE-WM-Lite), and the other additionally adapts it using feedback.

Table 3 | LabHorizon benchmark scores for reference systems [22] and RSI-Anything.

Table 4 | Domain bindings and reported update scope. The world model predicts consequences; the policy proposes or selects actions.