An agentic harness letting the LLM self-manage flat text-file storage via tool calls outperforms eight prior memory systems on cross-scenario generality across QA, chat, trajectory, stress-test, and long-horizon tasks.
InThe Four- teenth International Conference on Learning Repre- sentations
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MemIR is a typed memory representation for LLM agents that structures memory into atoms separating evidence, cues, and claims, leading to better performance on source tracking tasks in experiments on LoCoMo and BEAM-100K.
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Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
An agentic harness letting the LLM self-manage flat text-file storage via tool calls outperforms eight prior memory systems on cross-scenario generality across QA, chat, trajectory, stress-test, and long-horizon tasks.
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Mitigating Provenance-Role Collapse in Long-Term Agents via Typed Memory Representation
MemIR is a typed memory representation for LLM agents that structures memory into atoms separating evidence, cues, and claims, leading to better performance on source tracking tasks in experiments on LoCoMo and BEAM-100K.