arXiv Computer Vision
WebUniverse: Scaling Verified Training Data for Agentic Web Development
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arXiv AI
Oct 19
WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning
WebCoder is a general frontend development agent that uses artifact‑grounded agentic reinforcement learning to learn how to compose, generate, modify, and repair code over an evolving application state. It evaluates development actions by their impact on a shared executable artifact and a verifier that provides terminal rewards, while intermediate progress redistributes rollout advantage across development segments. The system includes Webdev‑World, an executable environment that integrates code editing, execution, browser inspection, and on‑demand skills, and Webdev‑Bench, a benchmark for composed development tasks, where WebCoder‑27B outperforms several large language models on requirement satisfaction and visual/functional scores.
arXiv AI
6d ago
WebAnything: From the Wild Web to Scalable Agent Environments
WebAnything is a fully automated framework that reconstructs source‑grounded, reusable web environments from real URLs without per‑site manual engineering. It uses an Explorer‑Builder to collect browser‑observable structures and an independent verifier to ensure fidelity, producing 244 website artifacts across 14 domains and 53 categories with 12,984 executed tasks. Compared to human‑assisted WebHarbor, WebAnything scores higher on three fidelity metrics and, when fine‑tuned with Qwen3.5‑9B, improves success rates across multiple benchmarks.
arXiv Computer Vision
Oct 27
Back to Basics: A Skill-Centric Approach to Generalist Web Agents
arXiv:2610.35823v1 Announce Type: new Abstract: Browser agents do not just need more practice. They need to practice the right things. We introduce MiniWeb, a realistic miniature of the web comprisin...
Hugging Face Trending Papers
Aug 25
BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes
Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high-quality interaction...
arXiv AI
Oct 15
HyWe: Unifying Search and Operation for Real-World Hybrid Web Tasks
arXiv:2610.16319v1 Announce Type: new Abstract: Real-world hybrid web tasks need both information gathering and page interaction, yet search-based and operation-based agents are developed and evaluat...
arXiv AI
Aug 19
Wuying-Browser-Agent: Real-World Centric Fundamental Long-Horizon Browser Agents
Wuying-Browser-Agent is a unified framework designed to improve long-horizon browser agents by aligning execution, supervision, optimization, and evaluation. It introduces a structured browser harness, reflection and UI-specialized Curriculum SFT (RUIC‑SFT) for recovery and complex UI interactions, and Divergence‑Aware Online GRPO (DAO‑GRPO) for better credit assignment. The framework is evaluated on BrowserBench—a bilingual real‑web benchmark of 350 tasks—and achieves state‑of‑the‑art results on multiple browser‑use benchmarks, while also transferring well to other agentic tasks.