# 重新思考智能体轨迹压缩：面向工作流的视角

> 全文为英语

**Published:** 2026年11月9日
**Author:** Qijia Zhuang, Rui Liu, Yuxiang Ren
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/rethinking-agent-trajectory-compression-a-workflow-aware-perspective

arXiv:2611.08865v1 公告类型：新 摘要：随着长时程智能体越来越多地依赖多步骤工具交互来完成复杂任务，其执行轨迹不断积累结构化输出和中间产物，导致上下文不断增长…

arXiv:2611.08865v1 Announce Type: new Abstract: As long-horizon agents increasingly rely on multi-step tool interactions to accomplish complex tasks, their execution trajectories continuously accumulate structured outputs and intermediate artifacts, leading to growing context overhead. Existing compression methods primarily rely on text importance or fixed truncation rules, often overlooking cross-step dependencies within workflows and potentially discarding critical information required for subsequent task execution. To address this challenge, we propose WAAC, a workflow-aware framework for agent trajectory compression. WAAC structurally models successful historical trajectories to extract reusable workflow compression experience and incorporates online backward dependency analysis to dynamically identify information retention requirements. By combining current dependency constraints with historical experience, WAAC selects appropriate compression strategies for different types of historical information. Experiments on PinchBench and Odysseys demonstrate that WAAC achieves an overall score of 87.6 and a perfect completion rate of 19.0%, respectively, while reducing execution costs by 19.7% and 29.1% compared with the strongest baseline methods. These results demonstrate the effectiveness of workflow-aware trajectory compression in improving task performance and cost efficiency for long-horizon agents. Code and resources are available at https://anonymous.4open.science/r/waac-6527/.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08865). 查看原文.*

**标签：** #arXiv

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