Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts

• URL: http://arxiv.org/abs/2602.02468v1
• Date: Mon, 02 Feb 2026 18:50:07 GMT
• Title: Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts
• Authors: Aiden Yiliu Li, Xinyue Hao, Shilong Liu, Mengdi Wang,
• Abstract summary: Avenir-Web is a web agent that achieves a new open-source state of the art on the Online-Mind2Web benchmark in real-world deployment.<n>We evaluate Avenir-Web on Online-Mind2Web, a rigorous benchmark of live and user-centered web tasks.
• Score: 59.68272935616536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advances in multimodal large language models, autonomous web agents still struggle to reliably execute long-horizon tasks on complex and dynamic web interfaces. Existing agents often suffer from inaccurate element grounding, the absence of site-specific procedural knowledge, and unstable long-term task tracking and memory, particularly when operating over complex Document Object Model structures. To address these limitations, we introduce Avenir-Web, a web agent that achieves a new open-source state of the art on the Online-Mind2Web benchmark in real-world deployment. Avenir-Web leverages a Mixture of Grounding Experts, Experience-Imitation Planning for incorporating procedural priors, and a task-tracking checklist combined with adaptive memory to enable robust and seamless interaction across diverse user interface paradigms. We evaluate Avenir-Web on Online-Mind2Web, a rigorous benchmark of live and user-centered web tasks. Our results demonstrate that Avenir-Web significantly surpasses prior open-source agents and attains performance parity with top-tier proprietary models, thereby establishing a new open-source state of the art for reliable web agents on live websites.

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