This Illinois+ Tsinghua University and other labs study finds that LLM agents still have unreliable memory and that it can get worse when they keep rewriting their own memories.

LLM agents can learn from experience， but their rewritten memories often become unreliable.

The problem is that many agent systems store past work by asking an LLM to compress messy experience into neat written lessons.

That sounds useful because the agent should remember what worked before， but the paper finds that repeated rewriting slowly damages the memory.

The core idea is that raw episodes， meaning the actual past attempts and solutions， often stay more useful than the polished lessons made from them.