RSIAgent: The Thing That Improves Is Not the Thing That Does the Improving

Four days after we read a seventy-five-page roadmap asking what genuine recursive self-improvement would architecturally require, six authors from Aether AI, UC San Diego and the University of Illinois Chicago shipped a paper claiming a working instance of it that never updates a single weight. RSIAgent (arXiv 2609.15364) is a training-free multi-agent harness: an actor that acts only by writing complete Python or Bash programs and is the sole role allowed to write memory, an independent verifier that can return PASS, FAIL or UNVERIFIED and inspects behind a restored QEMU checkpoint so its probes never enter the scored state, and a curriculum agent that decides what to practise next and never grades anything. The system explores a piece of software broadly and then deeply, writes down what it learned, freezes those notes, and goes to work. The "reusable causal structure" the abstract promises turns out to be a directory of actor-authored markdown files with, in the paper's own words, "no required schema, file count, or length" — roughly 150 to 320 kilobytes of notes per task, and no causal graph, do-operator or structure-learning algorithm anywhere in the paper. The headline is real and so are the qualifications the abstract leaves out, all of which the authors print themselves in Appendix C: the cross-system comparison "does not use a matched evaluation protocol", RSIAgent loses to GPT-6 Astra on ALE binary accuracy at 50.75 against 52.24, only 41 of 82 OSWorld tasks and 19 of 67 Agents' Last Exam tasks actually received any self-improvement with the rest retaining baseline scores, the explored tasks were selected because their baseline was below full credit, and Appendix A's first sentence calls the whole thing "the target-conditioned reference implementation" — the curriculum agent holds the scored query throughout and the deep stage attempts the scored task itself up to five times before the memory is frozen. The harness without any self-improvement already beats GPT-6 Astra on Agents' Last Exam, 83.75 against 82.26; self-improvement then adds 1.07, which is smaller than that benchmark's own published run-to-run standard deviation of about two points. On OSWorld the self-improvement contribution is decisive and internally controlled, plus 7.01 under an identical harness. We traced the largest single gain in the paper and it is rubric-shaped learning: on the video-editing task both runs remove the watermark and preserve the proportions, and the memory run scores 0.60 higher because it learned to express the crop through the native Shotcut filter the scorer searches for. Measured against episode 0062's ladder, RSIAgent clears persistence, clears strategy, clears the learning agenda with an asterisk, and stops dead at deployment and inheritance — by design, because the curriculum agent and all memory writes are switched off before the system is measured, and because the curriculum prompt, the verifier prompt, the stopping policy and the budgets are fixed by humans and never touched. The thing that improves is not the thing that does the improving. That is a smaller claim than the title makes, it is better supported than the title, and it is more useful to anyone who wants to build something this week.