arXiv AI
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory
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Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
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LazyMem: Retrieve Broadly, Construct Selectively for Efficient Long-Term Agent Memory
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Hugging Face Trending Papers
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Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
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Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents
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LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation
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Understanding and Mitigating Inference-Time Overreliance Using Agentic Memory
The paper investigates how large language model agents can over-rely on agentic memory, a phenomenon where retrieved memories distort inference even when they are correctly stored and retrieved. It shows that memory is helpful when past experience fully transfers to the current task but becomes misleading under partial query-memory overlap, a pattern confirmed by controlled experiments. To address this, the authors propose MEMTRIM, a plug‑and‑play framework that indexes memory evidence at write time and limits its reuse at read time, thereby reducing over-reliance without retraining and preserving useful memory benefits across models and memory architectures.