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Research questionHow can long-term LLM agents reconcile evolving textual evidence across interactions?Long-term agents must distinguish repeated evidence, historical states, updates, and unresolved contradictions. Independent semantic retrieval can obscure these relationships and complicate downstream reasoning.
AI
AI Agents
AI Memory
Evaluation & Benchmarks
Information Retrieval
Natural Language Processing
Reasoning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence RetrievalThe source concerns textual memory for long-term LLM agents, preserving atomic memories and provenance while exposing current, historical, supporting, and conflicting evidence. Its reported evidence comes from the BEAM and StructMemEval benchmarks using open-weight and proprietary LLM backbones.research paper · Sep 2, 2026
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How can personalized LLM agents retrieve time-valid memories of persistent and evolving user states?How can LLM agents compress growing factual memories without losing retrievable evidence?How can memory-augmented LLM agents coordinate memory construction, retrieval, and repair over long-horizon interactions?How can LLM agents answer recurring questions over unstructured documents without repeatedly rereading them?