title: Is Your LLM Secretly a World Model of the Internet? Model-Based Planning
    for Web Agents
summary: Language agents have demonstrated promising capabilities in automating
  web-based tasks, though their current reactive approaches still underperform
  largely compared to humans. While incorporating advanced planning algorithms,
  particularly tree search methods, could enhance these agents' performance,
  implementing tree search directly on live websites poses significant safety
  risks and practical constraints due to irreversible actions such as confirming
  a purchase. In this paper, we introduce a novel paradigm that augments language
  agents with model-based planning, pioneering the innovative use of large
  language models (LLMs) as world models in complex web environments. Our method,
  WebDreamer, builds on the key insight that LLMs inherently encode comprehensive
  knowledge about website structures and functionalities. Specifically,
  WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g.,
  "what would happen if I click this button?") using natural language
  descriptions, and then evaluates these imagined outcomes to determine the
  optimal action at each step. Empirical results on two representative web agent
  benchmarks with online interaction -- VisualWebArena and Mind2Web-live --
  demonstrate that WebDreamer achieves substantial improvements over reactive
  baselines. By establishing the viability of LLMs as world models in web
  environments, this work lays the groundwork for a paradigm shift in automated
  web interaction. More broadly, our findings open exciting new avenues for
  future research into 1) optimizing LLMs specifically for world modeling in
  complex, dynamic environments, and 2) model-based speculative planning for
  language agents.
paper_authors._id: 673e9edfaac00eafcaf4a83c
paper_authors.name: Yu Gu
paper_authors._id: 673e9edfaac00eafcaf4a83d
paper_authors.name: Boyuan Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-06T16:08:43.883Z
paper_authors._id: 631a95cfa66151e36e54a905
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/631a95cfa66151e36e54a905/jM_4vy9_Kd8zomFVYtsTo.png
paper_authors.fullname: Boyuan Zheng
paper_authors.type: user
paper_authors.user: boyuanzheng010
paper_authors._id: 673e9edfaac00eafcaf4a83e
paper_authors.name: Boyu Gou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-05T23:03:15.619Z
paper_authors._id: 6500870f1e14749e84f8f887
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6500870f1e14749e84f8f887/wfvx4BZvh2OyW-vpq5jEy.jpeg
paper_authors.fullname: Boyu Gou
paper_authors.type: user
paper_authors.user: BoyuNLP
paper_authors._id: 673e9edfaac00eafcaf4a83f
paper_authors.name: Kai Zhang
paper_authors._id: 673e9edfaac00eafcaf4a840
paper_authors.name: Cheng Chang
paper_authors._id: 673e9edfaac00eafcaf4a841
paper_authors.name: Sanjari Srivastava
paper_authors._id: 673e9edfaac00eafcaf4a842
paper_authors.name: Yanan Xie
paper_authors._id: 673e9edfaac00eafcaf4a843
paper_authors.name: Peng Qi
paper_authors._id: 673e9edfaac00eafcaf4a844
paper_authors.name: Huan Sun
paper_authors._id: 673e9edfaac00eafcaf4a845
paper_authors.name: Yu Su
paper_title: Is Your LLM Secretly a World Model of the Internet? Model-Based Planning
    for Web Agents
paper_summary: Language agents have demonstrated promising capabilities in automating
  web-based tasks, though their current reactive approaches still underperform
  largely compared to humans. While incorporating advanced planning algorithms,
  particularly tree search methods, could enhance these agents' performance,
  implementing tree search directly on live websites poses significant safety
  risks and practical constraints due to irreversible actions such as confirming
  a purchase. In this paper, we introduce a novel paradigm that augments language
  agents with model-based planning, pioneering the innovative use of large
  language models (LLMs) as world models in complex web environments. Our method,
  WebDreamer, builds on the key insight that LLMs inherently encode comprehensive
  knowledge about website structures and functionalities. Specifically,
  WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g.,
  "what would happen if I click this button?") using natural language
  descriptions, and then evaluates these imagined outcomes to determine the
  optimal action at each step. Empirical results on two representative web agent
  benchmarks with online interaction -- VisualWebArena and Mind2Web-live --
  demonstrate that WebDreamer achieves substantial improvements over reactive
  baselines. By establishing the viability of LLMs as world models in web
  environments, this work lays the groundwork for a paradigm shift in automated
  web interaction. More broadly, our findings open exciting new avenues for
  future research into 1) optimizing LLMs specifically for world modeling in
  complex, dynamic environments, and 2) model-based speculative planning for
  language agents.
paper_ai_summary: WebDreamer enhances web agents' performance by using LLMs as world models to simulate action outcomes, leading to significant improvements over reactive baselines.
paper_ai_keywords: tree search methods model-based planning large language models world models web environments natural language descriptions VisualWebArena Mind2Web-live speculative planning language agents