MemCon

MemCon: Learned adaptive memory management for LLM agents via MDP-based policy

Researchers introduce Memory as a Controlled Process (MemCon), a framework that models LLM agent memory operations as a Markov Decision Process and learns an online policy for adaptive retrieval, plan injection, and memory consolidation. The system is backend-agnostic, wraps existing memory implementations, and uses a lightweight tabular contextual bandit with UCB exploration requiring no pretraining and no additional LLM calls. Evaluated across 6 benchmarks, 3 agent frameworks, and 3 LLM backbones, MemCon outperforms static memory baselines by up to 15.2 points in task success while reducing token consumption by 5–20%.