MOSAIC: Accurate and Efficient Long-Term Memory for LLM Agents
A new framework called MOSAIC introduces structured, conflict-aware long-term memory for LLM agents, using entity-typed graph storage, hash-accelerated retrieval, and active conflict detection. MOSAIC achieves 89.35% accuracy on the LoCoMo benchmark, outperforming baselines by 27.21 percentage points, and detects 66% of factual conflicts—4.7 times higher than the best baseline—while maintaining low search latency (0.58 seconds per question). The system also demonstrates state-of-the-art results on HaluMem benchmarks for extraction F1 and QA correctness.
Why it matters: MOSAIC addresses major limitations in LLM agent memory by enabling more accurate, efficient, and contradiction-aware long-term recall, representing a significant advance over existing methods.
Full story at: arXiv AI/ML ↗