The Missing Piece in LLM Memory: Why Long Context and Retrieval Aren't Enough for Belief Revision

Long context windows, retrieval systems, and KV caches can preserve or surface past information, but they do not inherently determine how an LLM should respond when newer evidence contradicts older records, a problem described as belief revision rather than simple forgetting. The article introduces “update semantics” as a framework for comparing mutable memory systems according to how they write new evidence, represent stored state, and read it back, distinguishing revision performed during storage from revision performed during retrieval. Using linear attention and fast-weight associative memory as an analytic example, it shows that additive outer-product updates efficiently compress history and can support exact retrieval under orthogonal keys or useful averaging of repeated noisy observations, but they do not naturally overwrite obsolete facts. When keys overlap, stored associations can also interfere with one another, making revision less local and retrieval less reliable. The article contrasts approaches that retain explicit histories, compress information into recurrent states, use gated or prediction-error corrections, update parameters during inference, or distribute memory across plastic components, arguing that each has different trade-offs among stability, adaptability, interference, noise sensitivity, and forgetting. It concludes that replacing an outdated association requires a state-dependent correction, such as a delta-rule update based on the difference between the current prediction and new target, which will be examined in the series’ second part.

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