Hacker News Front Page · [Submitted on 29 Sep 2026] ·
Context Language Models
Se introducen los Context Language Models (CLMs), que permiten a los modelos de lenguaje gestionar nativamente su propio contexto tratándolo como un archivo que pueden modificar sin restricciones. Esta arquitectura supera estrategias existentes de gestión de contexto en múltiples tareas, logrando 11.4% más precisión con 59% menos operaciones en pruebas de resistencia de larga duración, y permite que el modelo aprenda dinámicamente qué información es crítica mantener. Los CLMs se extienden naturalmente a sistemas multiagente donde contextos independientes coexisten como archivos separados, y pueden optimizarse mediante instrucciones en lenguaje natural o aprendizaje por refuerzo en línea, mejorando el rendimiento hasta 47.6% mientras reduce el cómputo necesario.
We introduce Context Language Models (CLMs), language models that natively manage their own context. We implement this by treating the context as a file and allowing the model to make unrestricted updates to this file. This allows the model to learn what is most important to maintain in context, and naturally extends to multi-agent systems where multiple agent contexts coexist as files. Building CLMs zero-shot with existing models outperforms SOTA context management strategies across a variety of tasks: 11.4% higher accuracy with 21.5% fewer FLOPs on BrowseComp-Plus, 5% higher scores with 59% fewer FLOPs on 12-hour EdgeBench, and 65% greater improvement with the same compute on a 24-hour multi-repository agent-swarm task. Moreover, by shifting context management from external harness control to intrinsic model behavior, CLMs naturally enable both in-context and parametric learning of context-management strategies. We show that CLMs can be steered with natural-language instructions evolved through a standard skill-optimization loop, improving held-out accuracy by up to 35.9 points on a context-management task while reducing compute. We also introduce an online reinforcement learning method for CLMs, improving Qwen3.5-9B performance on BrowseComp-Plus by 47.6% while using 12% fewer FLOPs. Finally, we co-design Suffix Cache Reuse for CLM serving, further reducing server-side compute by 35% relative to standard SGLang at matched performance.