ExpSeek：網路代理程式的自觸發經驗探索機制

ExpSeek: Self-Triggered Experience Seeking for Web Agents

January 13, 2026

作者: Wenyuan Zhang, Xinghua Zhang, Haiyang Yu, Shuaiyi Nie, Bingli Wu, Juwei Yue, Tingwen Liu, Yongbin Li

cs.AI

摘要

經驗干預在網路代理中已成為一種前景廣闊的技術範式，通過從累積經驗中提取有價值的洞察來增強代理的互動能力。然而現有方法主要在被動地將經驗作為全域上下文注入任務執行前，難以適應代理與環境互動過程中動態變化的情境觀測。我們提出ExpSeek方法，將經驗運用轉向步驟級別的主動搜尋：（1）利用模型內在信號估算步驟級熵值閾值以確定干預時機；（2）設計步驟級定制化經驗內容。在Qwen3-8B和32B模型上進行的四項高難度網路代理基準測試表明，ExpSeek分別實現了9.3%和7.5%的絕對效能提升。實驗驗證了熵作為自我觸發信號的可行性與優勢，並揭示即使僅使用40億參數的小型經驗模型也能顯著提升更大規模代理模型的效能。

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.