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Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation
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Large language models (LLMs) have recently gained much attention in building autonomous agents. However, the performance of current LLM-based web agents in long-horizon tasks is far from optimal, often yielding errors such as repeatedly buying a non-refundable flight ticket. By contrast, humans can avoid such an irreversible mistake, as we have an awareness of the potential outcomes (e.g., losing money) of our actions, also known as the "world model". Motivated by this, our study first starts with preliminary analyses, confirming the absence of world models in current LLMs (e.g., GPT-4o, Claude-3.5-Sonnet, etc.). Then, we present a World-model-augmented (WMA) web agent, which simulates the outcomes of its actions for better decision-making. To overcome the challenges in training LLMs as world models predicting next observations, such as repeated elements across observations and long HTML inputs, we propose a transition-focused observation abstraction, where the prediction objectives are free-form natural language descriptions exclusively highlighting important state differences between time steps. Experiments on WebArena and Mind2Web show that our world models improve agents' policy selection without training and demonstrate our agents' cost- and time-efficiency compared to recent tree-search-based agents.
Forward citations
Cited by 6 Pith papers
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WebSynthesis: World-Model-Guided MCTS for Efficient WebUI-Trajectory Synthesis
A world-model-guided MCTS pipeline synthesizes 4k web navigation trajectories and yields a WebArena Pass@3 success rate of 20.15%, above OS-Genesis (18.66%) and AgentTrek (11.94%).
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IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
IndoorWorld is a new multi-agent environment that combines physical task solving with social interaction, and its experiments show effects of collaboration, resource competition, and layout on agent behavior.
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Quo Vadis, World Modeling?
An agent-centric reframing of world modeling, replacing physical state prediction with 'information transitions' organized into six proxy functions and three empowerment levels.
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TAPO: Transition-Aware Policy Optimization for LLM Agents
Interleaving action-conditioned next-observation supervision with group RL on a shared LLM backbone consistently lifts long-horizon agent success over pure policy optimization.
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Self-Improvements in Modern Agentic Systems: A Survey
Self-improving agents are classified by what they update — foundation-model weights or the surrounding scaffold — and by the signal that drives the update, under a single formal operator.
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Designing Memory-Augmented AR Agents for Spatiotemporal Reasoning in Personalized Task Assistance
A position paper proposing a four-module memory-augmented AR agent framework that uses stored scene graphs of past user experiences to personalize task guidance.
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