Paper Detail
Lingfeng Zhang, yongan sun, Jinpeng Hu, Hui Ma, yang ying, Kuien Liu, Zenglin Shi, Meng Wang, Yongan Sun, Yang Ying
Recent advancements in large language models (LLMs) have empowered autonomous web agents to execute natural language instructions directly on real-world webpages. However, existing agents often struggle with complex tasks involving dynamic interactions and long-horizon execution due to rigid planning strategies and hallucination-prone reasoning. To address these limitations, we propose WebUncertainty, a novel autonomous agent framework designed to tackle dual-level uncertainty in planning and reasoning. Specifically, we design a Task Uncertainty-Driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments. Furthermore, we introduce an Action Uncertainty-Driven Monte Carlo tree search (MCTS) Reasoning Mechanism. This mechanism incorporates the Confidence-induced Action Uncertainty (ConActU) strategy to quantify both aleatoric uncertainty (AU) and epistemic uncertainty (EU), thereby optimizing the search process and guiding robust decision-making. Experimental results on the WebArena and WebVoyager benchmarks demonstrate that WebUncertainty achieves superior performance compared to state-of-the-art baselines.
No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.
No ranking explanation is available yet.
No tags.
@article{zhang2026webuncertainty,
title = {WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent},
author = {Lingfeng Zhang and yongan sun and Jinpeng Hu and Hui Ma and yang ying and Kuien Liu and Zenglin Shi and Meng Wang and Yongan Sun and Yang Ying},
year = {2026},
abstract = {Recent advancements in large language models (LLMs) have empowered autonomous web agents to execute natural language instructions directly on real-world webpages. However, existing agents often struggle with complex tasks involving dynamic interactions and long-horizon execution due to rigid planning strategies and hallucination-prone reasoning. To address these limitations, we propose WebUncertainty, a novel autonomous agent framework designed to tackle dual-level uncertainty in planning and re},
url = {https://arxiv.org/abs/2604.17821},
keywords = {cs.AI},
eprint = {2604.17821},
archiveprefix = {arXiv},
}
{}