Hao Peng (@haopeng_uiuc)
@haopeng_uiuc
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Excited to share our new paper: “Useful Memories Become Faulty When Continuously Updated by LLMs. Can LLM agents keep improving by turning past experience into compact, reusable memories?
We find this is much more fragile than it looks. Continuously consolidated memories can perform worse than no memory at all — sometimes even on problems the agent previously solved.
Episodic memories that preserve raw episodes are much more reliable.
There is still limited evidence that today’s models can learn reusable abstractions from experience over the long term, which I believe is a crucial capability for agents that continuously improve.
Paper: Congrats to @dylan_works_ and team!
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