HMARS introduces a hierarchical multi-agent memory system that outperforms standard retrieval and other baselines on long-document and multi-turn reasoning tasks through improved evidence coverage.
HiMem: Hierarchical long-term memory for LLM long-horizon agents.CoRR, abs/2601.06377
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
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2026 6representative citing papers
RoleMemo dataset and DualMem dual-memory framework let role-playing agents interpret facts through personas, with a 4B model beating larger zero-shot systems on fidelity.
DimMem introduces typed dimensional memory units that improve accuracy to 81.43% and 78.20% on two long-term agent benchmarks while cutting token cost by 24% and enabling small models to match larger extractors.
MemForest reformulates agent memory as a temporal data management problem using a hierarchical index (MemTree) for parallel construction and localized updates, reporting 79.8% accuracy and 6x throughput on LongMemEval-S and LoCoMo benchmarks.
ContextForge recycles context in long-horizon LLM tasks via query generation, memory retrieval, and synthesis, yielding reduced token use and improved consistency on a 15-turn healthcare benchmark while preserving accuracy.
citing papers explorer
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HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
HMARS introduces a hierarchical multi-agent memory system that outperforms standard retrieval and other baselines on long-document and multi-turn reasoning tasks through improved evidence coverage.
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From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents
RoleMemo dataset and DualMem dual-memory framework let role-playing agents interpret facts through personas, with a 4B model beating larger zero-shot systems on fidelity.
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DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory
DimMem introduces typed dimensional memory units that improve accuracy to 81.43% and 78.20% on two long-term agent benchmarks while cutting token cost by 24% and enabling small models to match larger extractors.
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MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing
MemForest reformulates agent memory as a temporal data management problem using a hierarchical index (MemTree) for parallel construction and localized updates, reporting 79.8% accuracy and 6x throughput on LongMemEval-S and LoCoMo benchmarks.
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Context Recycling for Long-Horizon LLM Inference
ContextForge recycles context in long-horizon LLM tasks via query generation, memory retrieval, and synthesis, yielding reduced token use and improved consistency on a 15-turn healthcare benchmark while preserving accuracy.
- Retention Consequence in Lifecycle Memory Control