Entropic Memory: A Thermodynamics-Inspired Consolidation Mechanism for Lifelong Agent Learning

Large language model (LLM) agents often degrade over long interaction streams because memory accumulates noisy observations that reduce retrieval quality. We propose Entropic Memory, a two-tier memory consolidation mechanism that periodically transfers information from a hot working buffer into a cold long-term store. The method uses a free-energy objective to balance utility against embedding entropy, together with a temperature-controlled stochastic replacement rule. In the controlled Infinite Room environment under a fixed memory budget, Entropic Memory matches greedy importance sampling at 30\% noise ($SR \approx 0.29$) and improves survival rate from $0.24$ to $0.28$ at 50\% noise (+15\% relative). Overall, these results indicate that entropy-aware consolidation improves robustness to distractors in this controlled continual-memory setting.