Flex-RSI: Benchmarking Recursive Self-Improvement of Frontier Agents
Read the original on arXiv Computer Vision →Flex‑RSI is a new benchmark that evaluates how agents can recursively improve their own harness—prompts, tools, memory, and control loop—while solving vision and robotics tasks under a fixed budget. Each round alternates between an executor that refines the task solution and an improver that revises the harness, with only higher‑scoring solutions retained for the next iteration. The benchmark tests both closed‑source and open‑weight models across perception, reasoning, robot action, 3D reconstruction, visual creation, and infrastructure tasks, revealing that self‑improvement is most effective when agents build their own solutions, that simple success/failure feedback limits open‑weight models, and that harness editing itself contributes significantly to performance gains.
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