Registration Now Open for the Mininglamp Technology WebRetriever Global Challenge--Win Your Share of the $15,000 Prize Pool!
Registration Now Open for the Mininglamp Technology WebRetriever Global Challenge--Win Your Share of the $15,000 Prize Pool! |
| [28-July-2026] |
Registration for the WebRetriever Global Challenge is officially open! BEIJING, July 28, 2026 /PRNewswire/ -- This competition is hosted by Mininglamp Technology and co-organized by Peking University, the Institute of Automation of the Chinese Academy of Sciences (CASIA), Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, and Machine Heart. Featuring a total prize pool of $15,000, the competition is open to both individuals and teams, with no restrictions on nationality or institutional background. We warmly invite participants from academia, industry, and independent developer communities to check the registration details below and compete alongside top talent from around the world! Background When AI agents enter real browsers, can they truly complete tasks independently in the ever-changing environment of the open internet—just as humans do? This is the core bottleneck for deploying Web Agents from labs to real business scenarios. For too long, the field has lacked a realistic "yardstick" enough for the open web—most mainstream benchmarks rely on a limited number of simulated or self-hosted sites, falling significantly short of the complexity found on the real open web. In terms of evaluation dimensions, existing methods largely focus on the correctness of individual operations, while lacking systematic measurement of whether an Agent can truly deliver end-to-end task results. To address these gaps, Mininglamp Technology built WebRetriever, a large-scale comprehensive Web Agent evaluation benchmark. The corresponding paper has been officially accepted by ECCV 2026, a top-tier international academic conference:
Covers 800 live websites and 1,550 cross-industry tasks across eight domains including technology, finance, healthcare, education, and government services—all run on the real open internet.
The self-developed NavEval framework achieves 91.2% agreement with human expert judgments (vs. ~81% for the best existing method), enabling large-scale automated evaluation at trustworthy precision for the first time.
Evaluation data shows that even the top-performing single model achieves less than 50% on basic navigation success, while end-to-end task completion rate is only ~20%. "Reaching" a page is far from "completing" a task. The WebRetriever Challenge calls upon researchers, developers, and technical teams from academia and industry worldwide to validate the practical performance of Web Agents within an environment that mirrors real-world applications. Together, we drive the pivotal leap from "demo-ready" to "delivery-ready." Task The challenge centers on Protocol III (End-to-End Task Protocol) within WebRetriever, the protocol closest to real deployment scenarios—requiring agents not only to navigate to the correct page, but also to precisely extract target information from text, documents, charts, and other multimodal content. All tasks follow three construction principles: authority, mandatory interaction, and determinism.
Evaluation The competition adopts an automated evaluation framework. Teams initiate evaluation through conversation with the Competition Bot and receive scoring feedback upon completion. Multiple submissions are allowed within each evaluation window; the best score counts as the stage result. All participating agents must connect to the unified evaluation environment via OpenAI-compatible APIs. Teams are fully responsible for their own model inference resource management. Detailed evaluation specifications will be officially released during the preparation period. Prizes
Schedule Registration (3 weeks): 07/16 — 08/07 Preparation (4 weeks): Late July — Late August The preparation period partially overlaps with registration—registered teams may begin preparation early. Submission (1 week): Late August Review: Early September Results Announcement: September Detailed schedule updates will be posted in the dedicated WebRetriever Competition Octo Space. Register
Step 1 — Create an Octo account (skip if you already have one)
Step 2 — Join the Competition Space Log in and use the invite code above to join the WebRetriever Competition Space Step 3 — Complete Registration Once inside the Space, follow the instructions to submit your registration details (team name, members, etc.). Eligibility Individuals or teams may register. No restrictions on nationality or institutional affiliation. Researchers, industry practitioners, and independent developers are all welcome. Organizers & Support
Mininglamp Technology (2718.HK)
Zhen Lei — Ph.D., IEEE / IAPR / AAIA Fellow. Researcher at the Institute of Automation, CAS; Professor at the University of Chinese Academy of Sciences; Professor at the Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, CAS; Doctoral supervisor. Research: video analysis and understanding, multimodal large models, biometric recognition. 200+ papers, 41,000+ citations, H-index 92. Listed in the Global Top 2% Scientists Ranking. Ping Wang — Ph.D., Professor & doctoral supervisor at Peking University; State Council Special Allowance Expert. Director of the Intelligent Computing & Sensing Lab, National Engineering Research Center for Software Engineering. MOE Higher Education Scientific Research Output Award (Natural Science), 1st Prize, first contributor. Research: intelligent computing & sensing, smart healthcare, information security. 200+ papers; 40+ patents and software copyrights. Meng Ma — Ph.D., Associate Researcher at Peking University. Deputy Director of the National Engineering Research Center for Software Engineering; Research Lead of the Intelligent Computing and Sensing Lab. Member of multiple CCF technical committees; Member of CCF YOCSEF (Young Computer Scientists & Engineers Forum). Research: intelligent network O&M, fault diagnosis and prediction, situational awareness. 80+ papers, 2,700+ citations. Kun Yan — Ph.D., Distinguished Associate Researcher at PKU School of Computer Science. Selected for National Postdoctoral Researcher Funding Program; CCF Digital Medicine Division Executive Committee. Research: low-annotation learning, 3D visual segmentation, smart healthcare. 21 papers in T-PAMI, CVPR, AAAI, ACM MM, Nature Communications. ACM MM 2024 Best Paper Nomination. Contact Us: WebRetrieverChallenge@mininglamp.com
SOURCE Mininglamp Technology | ||
Company Codes: HongKong:2718 |
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