🤖 AI Summary
This work addresses the lack of effective long-term memory management in large language model agents during extended interactions. Inspired by human cognition, the authors propose a novel memory architecture that systematically integrates six key mechanisms: sleep-based consolidation, interference-driven forgetting, memory trace maturation, retrieval-induced reconsolidation, entity-centric knowledge graphs, and multi-cue hybrid retrieval. To prevent data leakage, they introduce an unsupervised synthetic calibration method for threshold setting. The framework further incorporates memory deduplication and compression, context budget control, and a streaming multi-level evaluation paradigm. Evaluated on the VSCode dataset, the approach achieves 97.2% memory retention accuracy while reducing storage overhead by 58%. On the LongMemEval benchmark, it matches baseline retrieval accuracy using only 200K tokens of context and improves S-tier preference recall by 13.3 percentage points.
📝 Abstract
Current LLM agents lack principled mechanisms for managing persistent memory across long interaction horizons. We present a biologically-grounded memory architecture comprising six cognitive mechanisms: (1) sleep-phase consolidation, (2) interference-based forgetting, (3) engram maturation, (4) reconsolidation upon retrieval, (5) entity knowledge graphs, and (6) hybrid multi-cue retrieval. Each mechanism addresses a specific failure mode of naive memory accumulation. We introduce a synthetic calibration methodology that derives all pipeline thresholds without benchmark data exposure, eliminating a common source of evaluation leakage. We evaluate on two benchmarks. First, a VSCode issue-tracking dataset (13K issues, 120K events) where deduplication-based consolidation achieves 97.2% retention precision with 58% store reduction (+21.8 pp over baseline). Second, the LongMemEval personal-chat benchmark where we conduct the first streaming M-tier evaluation (475 sessions, ~540K unique turns). At a 200K-token context budget, our pipeline matches raw retrieval accuracy (70.1% vs. 71.2%, overlapping 95% CI) while exposing a tunable accuracy/store-size operating curve. At S-tier scale (50 sessions), dedup-based consolidation yields a +13.3 pp improvement in preference recall.