ExpSeek: Self-Triggered Experience Seeking for Web Agents
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.
69670413c5e371f6b235d0ea
Wenyuan Zhang
69670413c5e371f6b235d0eb
Xinghua Zhang
69670413c5e371f6b235d0ec
Haiyang Yu
69670413c5e371f6b235d0ed
Shuaiyi Nie
69670413c5e371f6b235d0ee
Bingli Wu
69670413c5e371f6b235d0ef
Juwei Yue
69670413c5e371f6b235d0f0
Tingwen Liu
69670413c5e371f6b235d0f1
Yongbin Li
ExpSeek: Self-Triggered Experience Seeking for Web Agents
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 enables web agents to proactively seek experience during interaction by using entropy-based timing and tailored content, achieving significant performance improvements across multiple benchmarks.
experience intervention web agents step-level proactive seeking entropy thresholds intrinsic signals experience content design Qwen3-8B Qwen3-32B web agent benchmarks