You don't need long context

Peter Belcak, Saurav Muralidharan, Jindong Jiang, Yonggan Fu, Wonmin Byeon, Hanrong Ye, Ryan Angilly, Ali Taghibakhshi, Shih-Yang Liu, Siru Ouyang, Greg Heinrich, Pavlo Molchanov

You don't need long context: 5 upvotes on Hugging Face Daily Papers, #29 of 46 papers on 2026-11-04. Day-by-day upvote history.

Upvotes over time

It had 5 upvotes by its first day, 5 upvotes by the next day, 5 upvotes by day 3, 5 upvotes by day 7, and has 5 now. It ranked #88 of 130 papers in its week and #88 of 130 in its month.

Later tokens are costlier to generate than earlier tokens. On frontier open weight models, the disparity in cost may rise to as much as 7x. Through frequent compaction, we show across a broad range of long-horizon and long-input tasks that frontier language models sacrifice little to no task accuracy when maintaining short contexts. We further demonstrate that LLM performance does not collapse on long-horizon software engineering tasks even when taking at most one action per context, and based on that we outline the concept of semantic state space models (SSSMs). Our findings question the need for long-context language models.

Paper page on Hugging Face · arXiv

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