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Sakana AI and UC Berkeley's MASS paper frames recursive self-improvement not as a single model thinking harder, but as a cycle of evolutionary workflow search, self-evaluation, and LoRA fine-tuning on the model's own best multi-agent traces — with the workflow text deliberately withheld during training.
Read more →AIDE², published September 22, wraps an AI R&D agent in a recursive self-improvement loop: propose modifications to your own codebase, benchmark on held-out evaluations, keep what works. An 8-day autonomous run found seven successive improvements — novel search policies and memory mechanisms — that generalized to unseen benchmarks. More unexpectedly, reward hacking dropped from 55% to 32% as capabilities improved.
Read more →MOSS is a new system that lets autonomous agents evolve by rewriting their own source code in response to production failures — not just prompts or skill files. The key claim is that structural failures in routing, state management, and dispatch live in code, not in any text artifact, so text-mutable approaches can never reach them.
Read more →MiniMax open-sourced M2.7, a 229B sparse MoE model for coding and agentic work. The interesting part isn't the benchmarks — it's the self-evolution loop: an internal M2.7 instance ran 100+ rounds autonomously modifying its own programming scaffold, keeping what worked and reverting what didn't, and came out 30% better with no per-step human direction. That's a different kind of claim than standard RL post-training.
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