ExpSeek：网络智能体的自触发式经验探索

摘要

经验干预在网页智能体领域正成为一种前景广阔的技术范式，通过从积累的经验中提取有价值洞见来增强智能体的交互能力。然而现有方法主要在任务执行前将经验作为全局上下文被动注入，难以适应智能体与环境交互过程中动态变化的上下文观察。我们提出ExpSeek方法，将经验使用方式转向步骤级主动寻求：首先基于模型内在信号估计步骤级熵阈值以确定干预时机；其次设计步骤级定制化经验内容。在Qwen3-8B和32B模型上进行的四项高难度网页智能体基准测试表明，ExpSeek分别实现了9.3%和7.5%的绝对性能提升。实验验证了熵作为自我触发信号的可行性优势，并揭示即使仅使用4B小规模经验模型也能显著提升更大规模智能体模型的性能。

English

Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insights from accumulated experiences. However, existing methods predominantly inject experience passively as global context before task execution, struggling to adapt to dynamically changing contextual observations during agent-environment interaction. We propose ExpSeek, which shifts experience toward step-level proactive seeking: (1) estimating step-level entropy thresholds to determine intervention timing using the model's intrinsic signals; (2) designing step-level tailor-designed experience content. Experiments on Qwen3-8B and 32B models across four challenging web agent benchmarks demonstrate that ExpSeek achieves absolute improvements of 9.3% and 7.5%, respectively. Our experiments validate the feasibility and advantages of entropy as a self-triggering signal, reveal that even a 4B small-scale experience model can significantly boost the performance of larger agent models.

ExpSeek：网络智能体的自触发式经验探索

ExpSeek: Self-Triggered Experience Seeking for Web Agents

摘要

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