WebWorld: The Browser as a World Model for Self-Improving Web Code

Jiajun Wu, Jian Yang, Yaxin Du, Wei Zhang, Haowen Wang, Junhang Cheng, Yuxuan Zhang, Tuney Zheng, Xianglong Liu, Ming Zhou


Abstract
VLM-driven self-improvement of web code has a structural flaw: the model that proposes the repair is the model that judges it, and visual plausibility under that judge is a poor proxy for whether the page actually works. What the loop is missing is a counterparty the VLM cannot fool, and the browser already is that counterparty: a deterministic, executable simulator of how an HTML artifact behaves under user actions, and in everything but name a world model for web code. We present WEBWORLD, the interface that lets a VLM prior interact with this browser-as-world-model autonomously and decides which interactions become supervision. Each round, the VLM emits a critique that the planner compiles into a typed interaction contract; the browser re-executes the candidate and issues an acceptance certificate only when both target progress and preservation of every previously verified capability hold; certified transitions accumulate as a quality ratchet that is the only thing the SFT export ever sees. Under matched training, WEBWORLD-27B improves Raw-27B by 5.3 points on HTMLBench-400 and 14.9 points on MiniAppBench-Val, and reaches the level of strong frontier systems such as Kimi-K2.6 and GPT-5.4 on interactive HTML generation. Equal-size ablations show that browser-backed admission carries the gain: without the certificate, the matched 9B lift nearly disappears.
Anthology ID:
2026.emnlp-main.612
Volume:
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
14080–14102
Language:
URL:
https://aclanthology.org/2026.emnlp-main.612/
DOI:
Bibkey:
Cite (ACL):
Jiajun Wu, Jian Yang, Yaxin Du, Wei Zhang, Haowen Wang, Junhang Cheng, Yuxuan Zhang, Tuney Zheng, Xianglong Liu, and Ming Zhou. 2026. WebWorld: The Browser as a World Model for Self-Improving Web Code. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14080–14102, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
WebWorld: The Browser as a World Model for Self-Improving Web Code (Wu et al., EMNLP 2026)
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PDF:
https://aclanthology.org/2026.emnlp-main.612.pdf