WebHarbor is a collection of 153 dockerized website environments that faithfully replicate real-world web services, offering 3,518 tasks that include multi-step and post-authentication workflows. Built through task-driven human-agent collaboration, the suite ensures deterministic control, visual fidelity, and functional correctness. The platform enables reliable evaluation and cost-efficient reinforcement learning, with a Qwen 4B-level GUI agent trained in WebHarbor generalizing well to unseen tasks and matching leading open-source agents on four benchmarks.
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
MAI-Gym is an open, all-in-one platform that unifies GUI agent evaluation, data collection, and online reinforcement learning training across mobile, computer, and web environments. It supports ten established benchmarks, scales to 20,000 parallel environment instances, and enables automated synthesis of diverse GUI applications and tasks with execution-validated verifiers. Using this platform, the authors trained a 9B-parameter agent, MAI-Gym-9B, achieving state‑of‑the‑art performance on MobileWorld and OSWorld-Verified, and demonstrated that training on a mocked train‑ticketing app improves real‑world task success from 28.8% to 48.1% on a physical phone.
arXiv:2601. 04126v3 Announce Type: replace-cross Abstract: GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants.
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...
arXiv:2608.21898v1 Announce Type: new Abstract: Web agents promise to automate complex digital workflows, but their training remains limited by synthetic environments that look plausible while hiding...
arXiv:2606. 02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites.
Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large collections of curated web trajectories.
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.
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.
arXiv:2606. 29705v1 Announce Type: new Abstract: Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models.
arXiv:2609.22083v1 Announce Type: new Abstract: We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual too...
arXiv:2611.02102v3 Announce Type: replace Abstract: Scaling computer-use agents from individual actions to complex software work requires experience that connects decisions to complete outcomes. We p...