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A technical analysis comparing memory designs in RNNs, Transformers, and SSMs, arguing that the key question is where to store sequence state rather than which architecture is better. Discusses trade-offs between compressed hidden states, growing KV caches, and synaptic-like memory in model connectivity.
This paper introduces EnterpriseMem-Bench, a multi-turn Text-to-SQL benchmark, and evaluates five frontier models across memory architectures, finding that stateless models collapse by the third turn and that working memory yields the largest gains.