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AbstractRSI-Anything integrates world models into recursive self-improvement through prediction, feedback, and retention checks, and reports gains across multiple domains.
RSI-Anything is a cross-domain framework: it uses world model predictions to decide which candidates to take external feedback for, then uses the feedback to decide whether the policy, workflow, or predictor is updated, and uses checks to decide which pair is retained. The difficulty to address is the limited interaction budget, and the fact that once the policy changes, the candidate distribution faced by the predictor also changes. The authors argue that improving only one side does not guarantee that the assembled system will be better.
AbstractLocal closed-loop generation of JSON edge cases.
The authors built a local agentic loop that runs on a laptop to generate edge cases for safety arguments of autonomous systems, rather than releasing a new public test set.
AbstractThe authors claim experimental records can be reused inside and outside the environment and improve experiment selection under fixed budgets.
The authors use language models as research world models, predicting the median intervention gain before execution, to compare candidate experiments under a limited budget. The gap to address is that research agents propose faster than they execute. The same type of change can have different effects across reference models, recipes, and budgets, so experimental knowledge that can continue to be used outside the original environment is needed.