# Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation

Authors: Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong, Minju Gwak, Gwanwoo Song, Jihoon Kim, Sunghwan Kim, Dongha Lee, Jinyoung Yeo.

Predicts the effects of candidate web actions as natural-language descriptions of important changes, then uses those predictions for action selection.

First public: 2024-10-17.
ArXiv initial submission: 2024-10-17.

[Original paper](https://arxiv.org/abs/2410.13232) · [Reading-list entry](https://neural-os.com/reading-list/papers/2410.13232/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/2410.13232/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/2410.13232/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/2410.13232/record.json)

**Contribution:** Introduces a transition-focused abstraction that reduces the burden of predicting long, repetitive web observations and supports world-model-augmented planning.

**Model output:** Natural-language descriptions of changes in the next web observation.

**Action interface:** Candidate web-navigation actions.

**State and memory:** The planner uses web context and predicted descriptions of state changes.

**Relationship to NeuralOS:** Connects environment prediction to agent decisions in web navigation.

**Scope and limitations:** The paper evaluates web navigation using textual descriptions of state changes.

[Relationship source](https://arxiv.org/abs/2410.13232)

Related terms: web world model, counterfactual planning, transition prediction, WMA.

## Approaches

- [GUI prediction and planning](https://neural-os.com/reading-list/guides/games-to-computers/#gui-prediction-and-planning): Predict the consequences of interface actions so an agent can compare alternatives before execution. Prediction targets include textual changes, screen images, and renderable interface code.

Reading-list version 1.0.1; literature checked through 2026-10-19.