Rohan Paul

Beautiful roadmap paper on Recursive self-improvement. Concludes, we are already seeing pieces of RSI, but full recursive self-improvement is not here yet. Most self-improving AI still cannot improve how it improves Says that most things called "self-improving AI" today only automate parts of the improvement process. Genuine recursive self-improvement would mean the AI can persistently improve not just its outputs, prompts, tools, or code, but eventually the mechanism that decides how future improvements are discovered, tested, and kept. AI is already very strong at answering knowledge and reasoning questions, but still much weaker at doing long, multi-step tasks with tools, software, and changing environments. The paper maps progress across 5 levels, from executing human-designed improvements to changing the improver, evaluator, or research policy used in later rounds. That last step makes the process recursive: a successful update changes how future updates are discovered or judged. The survey finds broad evidence for lower levels, while experience-driven learning and deployment adaptation are more domain-dependent and end-to-end L5 evidence remains concentrated in bounded prototypes.