The authors build a biologically-inspired memory architecture for LLM agents with six mechanisms: sleep-phase consolidation, interference-based forgetting, engram maturation, reconsolidation on retrieval, entity knowledge graphs, and hybrid retrieval. They introduce a synthetic calibration method to set pipeline thresholds without exposing evaluation data. On a VSCode issue-tracking dataset, deduplication-based consolidation achieves 97% retention precision with 58% storage reduction; on LongMemEval personal chat (streaming evaluation with 475 sessions, ~540K turns), the pipeline matches raw retrieval accuracy at 200K-token budget while enabling an accuracy/storage trade-off curve.
Main takeaways:
- Six cognitive mechanisms address failure modes of naive memory accumulation: consolidation, forgetting, maturation, reconsolidation, knowledge graphs, hybrid retrieval
- Synthetic calibration derives all thresholds without benchmark exposure, avoiding evaluation leakage
- On VSCode issues: 97.2% retention precision, 58% store reduction, +21.8 pp over baseline
- On LongMemEval (streaming, 540K turns): matches raw retrieval accuracy (70.1% vs. 71.2%) at 200K budget with tunable accuracy/size trade-off
- At smaller scale (50 sessions), dedup consolidation yields +13.3 pp improvement in preference recall