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Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone does not guarantee memory usefulness: downstream performance depends on how memories are selected and presented to the agent. We propose MemComposer, a memory composer t...
Results show that entity-structured indexing and episode-level provenance provide a reliable foundation for long-term memory in agents, and EnSIMem achieves high answer accuracy while maintaining compact, evidence-focused contexts.
StateMem is presented, a state-first memory method that explicitly tracks supersession and relational dependencies, and it is shown it improves current-state accuracy over the strongest same-backbone baseline and over the strongest memory system, while remaining competitive with the long-context baselines.
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