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The paper introduces Oblivion, a memory control framework for LLM agents that mimics human memory by implementing decay-driven reductions in memory accessibility rather than explicit deletion. Oblivion decouples memory control into read and write paths, using agent uncertainty and memory buffer sufficiency to gate memory access and reinforcing memories that contribute to response generation. Experiments on long-horizon interaction benchmarks demonstrate that Oblivion dynamically adapts memory access and reinforcement, improving performance by balancing learning and forgetting.
LLM agents can learn more effectively by forgetting irrelevant information, mimicking human memory's decay-driven accessibility rather than relying on "always-on" retrieval.
Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues. In contrast, memory-augmented LLM agents rely on"always-on"retrieval and"flat"memory storage, causing high interference and latency as histories grow. We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility, not explicit deletion. Oblivion decouples memory control into read and write paths. The read path decides when to consult memory, based on agent uncertainty and memory buffer sufficiency, avoiding redundant always-on access. The write path decides what to strengthen, by reinforcing memories contributing to forming the response. Together, this enables hierarchical memory organization that maintains persistent high-level strategies while dynamically loading details as needed. We evaluate on both static and dynamic long-horizon interaction benchmarks. Results show that Oblivion dynamically adapts memory access and reinforcement, balancing learning and forgetting under shifting contexts, highlighting that memory control is essential for effective LLM-agentic reasoning. The source code is available at https://github.com/nec-research/oblivion.
