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"