WebWorld is presented, the interface that lets a VLM prior interact with this browser-as-world-model autonomously and decides which interactions become supervision and reaches the level of strong frontier systems such as Kimi-K2.6 and GPT-5.4 on interactive HTML generation.
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.
Evaluating WebUI code generation at scale is difficult: outputs may compile and look plausible yet fail under user interaction, and prior benchmarks largely rely on free-form prompts with static checks (build success, screenshots) that miss functional correctness. We introduce WebUIProof, an execution-oriented benchmar...
Yun-Yun Tsai, Yu-Ning Mao, Shi-Qi Wang et al.· 0 citations
We present WebFovea, a vision-based web agent that placed 2nd in the WebRetriever Challenge 2026 with a final score of 57.0 out of 100. The challenge evaluates agents end to end on Protocol III of the WebRetriever benchmark (arXiv:2607.06118): starting from an entry URL on a live website, the agent must operate the sit...
CAT is introduced, a paradigm in which the agent writes Playwright code to drive the browser, gathers feedback, and autonomously explores web applications to uncover bugs, revealing a clear gap between current VLM capabilities and the demands of real-world testing in AI web development.
Bin Hong, Zhen-Chao Zhang, Ji-Yuan He et al.· 0 citations
WebPageBench is an open framework for evaluating web agents in which every task is verified from the interface's own event log, and sensitivity to interface form can be measured under a fixed task specification.
Anton A. Emelyanov, M. Tikhonova, Zaven Martirosian et al.· 0 citations
With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propose modifications to their own components. This autonomy raises a fundamental question for safe self-improvement: can an automated gate like a reward model or an automate...
Brian B. Moser, Stanislav Frolov, T. Nauen et al.· 0 citations
Purpose: This paper examines WebMCP, a proposed browser-native web platform standard that lets websites expose explicit, schema-described tools to large language model agents, as an alternative to actuation, in which an agent simulates the clicks, keystrokes, and navigation a human would perform.
Methodology: The pape...
Harsha Nooka, Suhas Madupuri· Journal of Technology and Sy...· 0 citations
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