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arXiv Computation and Language By Guangyuan Wang, Yifan Zhang, Shilong Liu

When Should an Agent Refresh? Adaptive Index Reuse for Efficient Long-Horizon Reasoning

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The paper introduces Adaptive Index Reuse (AIR), a method that dynamically determines how many layers of a sparse attention indexer should refresh during a language agent’s reasoning process. AIR replaces the static layer‑refresh schedule of IndexCache with a small controller that selects from a nested library of patterns based on recent computation signals and tool events, optimizing for task reward under a compute budget. Experiments on Qwen3‑4B‑Instruct‑2507 with a 128K context show AIR improving success rates on SWE‑bench Verified, Terminal‑Bench 2.0, and three τ²‑bench domains while reducing time per successful task, and it matches or surpasses the static schedule across 50 varied settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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