Rohan Paul@rohanpaul_ai · X论文提出 AdaCoM，一个独立的 LLM，在智能体每步操作前编辑其工作上下文。它可重写、合并、剪枝或保留任务历史，使主智能体保持冻结，无需重新训练或暴露权重。与简单摘要不同，AdaCoM 学习不同智能体需要不同类型上下文——强智能体保留更多原始历史，弱智能体需更短更清晰的笔记。在 web search 和 deep research 任务上测试，平均提升 39%。
AI agent can get better at long tasks without retraining the agent itself， by using a separate small model to clean and organize its context.
Moves context management outside the agent， so a separate helper can clean up the task history while the main agent stays unchanged.
The paper proposes AdaCoM， which is a separate LLM that edits the agent's working context before the agent takes its next step.
AdaCoM places a separate， trained manager between the task history and the frozen agent， so the agent does not need to learn a new memory habit or expose its weights.
Before each step， this manager can rewrite， merge， prune， or preserve parts of the running context， then the original agent acts on the cleaned version.
That sounds like summarization， but the distinction matters.
A summary assumes the right answer is compression， while AdaCoM learns that different agents need different kinds of context to stay competent， because stronger agents can use more raw history while weaker agents need shorter and cleaner notes.
They tested AdaCoM on web search and deep research tasks across several agents， and it improved average web search performance by 39%.
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Link - arxiv. org/abs/2605.30785
Title： "Learning Agent-Compatible Context Management for Long-Horizon Tasks"