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; s
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 26%mem0ai/mem0 →
“Possibly related via embedding similarity 0.58 (not asserted). Timestamp check: artifact slightly before paper (-13d).”
- PossiblePossibly related (embedding) · 27%letta-ai/letta →
“Possibly related via embedding similarity 0.59 (not asserted). Timestamp check: artifact slightly before paper (-12d).”
- PossiblePossibly related (embedding) · 30%MemTensor/MemOS →
“Possibly related via embedding similarity 0.66 (not asserted). Timestamp check: artifact slightly before paper (-13d).”
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “Memory as a Controlled Process: Learned Adaptive Memory Mana” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “Memory as a Controlled Process: Learned Adaptive Memory Mana” ≈ “Unity-Technologies/ml-agents””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”