Long-term memory enables large language model (LLM) agents to provide continuous personalized assistance by retaining user information across interactions. Existing systems support this process throug
Long-term memory enables large language model (LLM) agents to provide continuous personalized assistance by retaining user information across interactions. Existing systems support this process through fact extraction, organization, updating, and retrieval, but largely maintain memories as independent facts. This fact-centric paradigm may overlook how new facts update, constrain, or conflict with earlier ones, while leaving their applicability, role, and priority for the current request implicit. In this paper, we introduce EvoMemState, a state-centric framework that augments persistent facts with two complementary states: Relational States that capture how facts remain valid and relate to…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
EvoMemState: Evolving Memory State for Personal Agents was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 28 checks on 3 different days. Its best position was #9 of 30, reached on Nov 6, 21:28 UTC. It is #10 right now, holding steady at #10 over the last 6 checks.
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Yes. As of Nov 8, 15:28 UTC it is #10, holding steady at #10 over the last 6 checks.
#9 on Hugging Face · Daily papers, on Nov 6, 21:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (28 checks) since.