H-Mem dual-structure memory tops benchmarks

H-Mem solves the "forgetfulness" problem in long-context agents by treating memory as an evolving structure rather than a static vector store.

• –Temporal-semantic trees handle the "when" and "what" by grouping facts chronologically and topically across different granularities.
• –Integrated knowledge graphs enable multi-hop reasoning across disjoint memory windows, outperforming traditional RAG.
• –Progressive consolidation reduces noise and prevents context bloat by distilling raw interactions into high-level summaries.
• –SOTA performance on LoCoMo, LongMemEval-S, and REALTALK confirms its efficacy for complex, multi-week agent simulations.
• –Provides a scalable blueprint for developers building autonomous agents that require persistent personality and world-state consistency.