MIT proposes dynamic compression to fix info loss in long-context recurrent models
burkov · x · 2026-08-25
Researchers from MIT CSAIL propose "dynamic compression" to address the issue where long-context recurrent models discard details needed for future tasks due to premature compression. By keeping the original sequence available and selectively rescanning relevant tokens once the current task reveals what matters, the method effectively rewrites the model's compact working memory. Experiments show this approach significantly reduces the internal state needed for accurate reuse in tasks involving learned functions.
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