Dynamic Compression in Recurrent Networks

Dynamic Compression in Recurrent Networks proposes a method for recurrent models to selectively revisit and update past tokens, rather than compressing all history in a single causal pass. By allowing the model to refine its fixed-size state only when needed, it can maintain lower-fidelity information in the raw sequence and revisit it later. Experiments show that this selective re-scanning reduces the recurrent state needed for accurate task reuse and scales better as the number of stored functions increases.

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