Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts explores Avenir-Web: An open-source state-of-the-art agent for executing tasks on dynamic web interfaces using multimodal grounding and adaptive memory.. Commercial viability score: 8/10 in Agents.
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Aiden Yiliu Li
University College London
Xinyue Hao
The University of Edinburgh
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Avenir-Web introduces significant improvements in web automation agents by addressing core issues like element grounding and task tracking, making it a formidable player in the open-source domain, capable of competing with proprietary models.
Package Avenir-Web as a SaaS tool for businesses looking to automate repetitive web-based tasks without needing in-house technical expertise, offering integration APIs for seamless adoption.
Avenir-Web can disrupt existing solutions by providing an open-source, cost-effective alternative to proprietary web automation platforms, fostering greater accessibility and innovation in the industry.
The demand for web automation in industries like e-commerce, B2B services, and data extraction (around $5 billion market) presents an opportunity. Companies constrained by slow, manual processes could greatly benefit, particularly if they cannot afford specialized proprietary solutions.
Deploy Avenir-Web in e-commerce to automate product listing processes, managing inventory updates and price adjustments across multiple online platforms.
The paper presents Avenir-Web, an AI agent designed to operate on complex, dynamic web interfaces. It uses a 'Mixture of Grounding Experts' to improve element detection on web pages, incorporates 'Experience-Imitation Planning' to use procedural knowledge from human interactions, and employs 'Adaptive Memory' for long-term task tracking. These innovations allow the agent to efficiently handle tasks on live websites with improved accuracy and stability, particularly in long-horizon tasks.
The effectiveness of Avenir-Web was demonstrated using the ONLINE-MIND2WEB benchmark, where it achieved a 23.7% improvement in task success rate over prior open-source systems and matched the performance of leading proprietary agents.
While promising, Avenir-Web's reliance on external procedural knowledge makes it potentially error-prone if the underlying web content changes significantly. It also may require robust support and updates to maintain high accuracy over time.