【论文速递】You don't need long context

英文标题：You don't need long context

arXiv编号：2611.05306
作者：Peter Belcak, Saurav Muralidharan, Jindong Jiang
来源：arXiv
原文链接：https://arxiv.org/abs/2611.05306

中文摘要：
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.

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