Maintenance in progress

a shanghai-based lab published a framework that allows llm-based agents to autonomously analyze their own execution traces and rewrite their own system prompts, tools, memory structures, and verification rules without human instruction or a stronger external model. the lab reports 60% performance gains from this self-modification loop. this is structurally unusual because it removes the human as the required author of the rules governing agent behavior — the system becomes both the executor and the editor of its own operating constraints.

ai research preprint and lab release, china