You don't need long context

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You don't need long context

AI research arXiv

New research suggests that frontier models can maintain task accuracy while using significantly shorter contexts, challenging the industry trend toward massive context windows.

What: Researchers from various institutions including Nvidia and various academic labs found that frequent context compaction allows models to perform long-horizon software engineering tasks without needing long-context windows, which are computationally expensive.

Why it matters: This finding threatens the competitive advantage of providers focused purely on scaling context window size, suggesting that intelligent memory management is more efficient than raw context expansion.

Deep dive

Shows that later tokens generated by LLMs are up to 7x costlier than earlier ones.
Introduces the concept of 'semantic state space models' (SSSMs) for managing state without infinite context.
Demonstrates that performance remains stable even with limited action-per-context frequency.
Argues that the industry focus on ever-larger context windows may be suboptimal for long-horizon task execution.

Decoder

Context compaction: A process of summarizing or distilling past interactions into a smaller representation to fit within a model's active memory.
Long-horizon task: A complex objective that requires many sequential steps to complete, typically involving multiple software engineering actions.

Original article

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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