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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...
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.
The study turns the filesystem default from an assumption into a design space for agent memory, and turns the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model.
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