Context Language Models: native editable context beats SOTA strategies, cuts compute 35% vs SGLang
RulinShao · x · 2026-09-30
A paper by Rulin Shao, Luke Zettlemoyer, Pang Wei Koh and colleagues proposes Context Language Models, which treat context as an editable file managed natively by the model, replacing brittle external truncation/summarization harnesses.
- Outperforms state-of-the-art context strategies in accuracy with lower FLOPs across single- and multi-agent tasks
- Includes serving optimizations: 35% compute reduction over SGLang
- Directly relevant to long-running agents, swarm architectures, and context-heavy pipelines
Selected by arXivBangers with a 92/100 editorial score.
Related event: Meta Proposes Context Language Models: Models Manage Their Own Context(5 posts)→
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