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arXiv · 2602.02468

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

Aiden Yiliu Li·Xinyue Hao·Shilong Liu·Mengdi Wang
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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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Aiden Yiliu Li, Xinyue Hao, Shilong Liu, Mengdi Wang. 2026-02-02. Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts. https://arxiv.org/abs/2602.02468

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