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.
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.