Accurate and Efficient Long-Term Memory for LLM Agents

• URL: http://arxiv.org/abs/2607.16211v1
• Date: Fri, 15 May 2026 02:21:45 GMT
• Title: Accurate and Efficient Long-Term Memory for LLM Agents
• Abstract summary: We present MOSAIC, a structured, conflict-aware long-term memory framework for LLM agents.<n> MOSAIC introduces three key capabilities: entity-typed graph storage with semantic classification; hash-accelerated dual-path retrieval; and active conflict detection.<n> evaluated on LoCoMo (long-conversation QA), HaluMem, and a novel clinical-guideline error compounding test.
• Score: 8.441856252027652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM agents augmented with persistent memory can recall past interactions, but existing systems suffer from two limitations: flat, unstructured storage loses relational context needed for multi-hop and temporal reasoning, and reliance on expensive LLM-based classification makes them impractical for latency-sensitive deployment. Without mechanisms to validate new information against stored knowledge, these systems silently accumulate contradictions. We present MOSAIC (Memory-Organized Structured Agent for Information Collection), a structured, conflict-aware long-term memory framework for LLM agents that is substantially more accurate and efficient. MOSAIC introduces three key capabilities: (1) entity-typed graph storage with semantic classification preserving relational structure across events, personas, and relationships, enabling multi-hop and temporal reasoning over conversation history; (2) hash-accelerated dual-path retrieval replacing LLM-based classification with locality-sensitive hashing, achieving near-instantaneous lookup with negligible accuracy loss; and (3) active conflict detection at save time that cross-references new information against existing graph neighbors, triggering updates or deletions for contradictory entries. Evaluated on LoCoMo (long-conversation QA), HaluMem, and a novel clinical-guideline error compounding test, MOSAIC achieves 89.35% accuracy on LoCoMo (+27.21 pp over the best baseline), best HaluMem-Medium extraction F1(86.77%) and HaluMem-Long extraction F1 (85.84%), best QA correctness on both Medium and Long (73.10%, 70.75%), and detects 66% of injected factual conflicts-4.7 times higher than the best baseline (14%)-while hash-accelerated retrieval keeps average search latency at 0.58 s per question.

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