From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents

Most systems of agent memory will return wrong answers. They keep the events, pull them back as context and re-reason the model by the same decision. The paper's claim is that saving notes and having procedures are different things, and most systems only do the first. MSCE sees this as a governance issue. Experience is organized in three levels: declarative facts about the environment, induced procedural policies and grounded step traces. Agents save transcripts, then reread them and re-derive the same lesson each time. MSCE instead promotes recurring patterns into callable procedures, but only if the evidence holds up. e.g. Pip fails in a container? Don't recall the incident; run the rule. when a pip install fails in a container because a system library is missing, find the OS package manager, install the dev package, retry. That rule is now a thing the agent can call, not a memory it has to interpret. A policy qualifies as a callable skill only when it has its own trigger and boundary, a positive estimated gain, and supporting evidence. No further fine-tuning is performed. – arxiv. org/abs/2607.16621 Title: "From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents"