Learning Agent-Compatible Context Management for Long-Horizon Tasks
External Reinforcement Learning Improves Context Efficiency for Frozen LLM Agents
LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause long-context degradation and reasoning failures. Prior work mitigates this through context management with agent-side context contr...
ArXiv CS.AI
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AdaCoM decouples context management from agent training, allowing external systems to optimize long-horizon task performance without modifying closed-source models. This approach demonstrates that tailored compression strategies based on an agent's reasoning capability are essential for scaling LLMs in complex, multi-step environments.
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