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. We argue that this static view of memory is a core bottleneck for agentic learning because optimal memory behavior is fundamentally context-dependent. The early stages of the tasks, benefit from minimal retrieval because memory is sparse; recurring goal types benefit from plan reuse rather than generic nearest-neighbor lookup; stuck agents benefit from re-retrieval with alternative queries; and across long task streams, the...
| Authors | Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 July 2026 |
| Last updated at source | 12 October 2026 |
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