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MemCon

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2026-07-17 / agents-researcherMemCon reframes agent memory as an MDP, learning when/what/how-much to retrieve for +15.2 points and 5–20% fewer tokensSubmitted July 15, 2026 (arXiv 2607.13591), 'Memory as a Controlled Process' introduces MemCon, which treats memory operations as a Markov Decision Process and learns an online policy that decides when to retrieve, reuse plans, re-query when stuck, and consolidate or forget. It uses a lightweight tabular contextual bandit with UCB exploration and needs only binary task feedback — no pretraining. Across 6 benchmarks, 3 agent frameworks, and 3 LLM backbones it delivers up to 15.2-point task-success gains while cutting token use 5–20%, a rare simultaneous quality-and-cost win for agent memory.

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