Learning Agent-Compatible Context Management for Long-Horizon Tasks
Lu Yi,
Runlin Lei,
Liuyi Yao,
Yuexiang Xie,
Yuyang Li,
Wenhao Zhang,
Zhewei Wei,
Yaliang Li,
Jian-Yun Nie.
Work in progress.
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Abstract:
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 control or fixed strategies such as summarization, which require training the agent itself for adaptation and are impractical for closed-source agents. We introduce Adaptive Context Management (AdaCoM), which trains an external LLM to manage the context of a frozen agent through flexible modification actions and end-to-end reinforcement learning. Across diverse agents on web search and deep research benchmarks, AdaCoM improves performance by preserving task constraints and progress while pruning stale content. The learned strategies reveal a Fidelity-Reliability Trade-off, and transfer experiments suggest a practical path toward reusable context managers for agent systems.