MRAgent combines a Cue-Tag-Content associative graph with active reconstruction to enable dynamic memory access in LLM agents, reporting up to 23% gains on long-memory benchmarks with lower token costs.
Memotime: Memory-augmented temporal knowledge graph enhanced large language model reasoning.arXiv preprint arXiv:2510.13614, 2025a
3 Pith papers cite this work. Polarity classification is still indexing.
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RoMem uses a Semantic Speed Gate to assign volatility to relations and continuous phase rotation to shadow obsolete facts in complex space, delivering SOTA temporal KG completion and 2-3x gains on agentic memory benchmarks.
A minimalist retrieval-and-generation framework using turn isolation and query-driven pruning outperforms complex memory systems by directly addressing signal sparsity and dual-level redundancy in dialogues.
citing papers explorer
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Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents
MRAgent combines a Cue-Tag-Content associative graph with active reconstruction to enable dynamic memory access in LLM agents, reporting up to 23% gains on long-memory benchmarks with lower token costs.
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Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory
RoMem uses a Semantic Speed Gate to assign volatility to relations and continuous phase rotation to shadow obsolete facts in complex space, delivering SOTA temporal KG completion and 2-3x gains on agentic memory benchmarks.
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Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation
A minimalist retrieval-and-generation framework using turn isolation and query-driven pruning outperforms complex memory systems by directly addressing signal sparsity and dual-level redundancy in dialogues.