The thesis is: predictive world models may make recursive self-improvement loops more economical by screening candidates before external evaluation. The public repository identifies RSI-Anything as built on JEPA-Anything and includes task implementations, configurations, and tests. These are concrete artifacts, not proof of the headline capability.
The mechanism is: use a predictive world model to rank candidate changes before spending calls on external evaluation. A system proposes changes to a policy or supporting model; the world model estimates which candidates are promising; selected candidates proceed to an external evaluator. Hidden tests, grader calls, environment interactions, and physical experiments can make each evaluation costly. Why now: a public implementation gives researchers and builders something concrete to inspect and test. The code release moves discussion from an abstract RSI concept toward an implementation that can be examined and tested.
The first implication is: the value of the loop depends on whether its rankings predict external evaluation outcomes on unseen candidates. Poor rankings could discard useful changes or waste the evaluation budget. The evidence boundary is: code availability does not establish that the approach works across tasks or outperforms direct evaluation. The repository supports the release and design claim, but does not independently demonstrate reliable cost savings or improved results. What to watch: comparative results that measure both evaluator calls saved and final performance across tasks and repeated rounds. Such comparisons would show whether the screening step improves the trade-off between evaluation cost and task outcomes.