Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
arXiv:2606.27330v1 Announce Type: cross Abstract: Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy preserving compared with commercial large models, they suffer from weak planning and limited cross website generalization. To address these limitations, we introduce the planning experience exploration and utilization (PEEU) method, which autonomously explores environments to discover experiences and utilizes hinds
Ongoing advancements in multimodal large language models and the increasing demand for autonomous agentic systems are driving current research into more efficient task planning for GUI agents.
Improved task planning and generalization for GUI agents can significantly enhance productivity by automating complex, repetitive digital tasks across various platforms.
The ability of smaller, open-source MLLMs to perform sophisticated task planning, previously limited to larger commercial models, is improving.
- · AI software developers
- · Businesses with repetitive digital workflows
- · Open-source AI communities
- · Tasks requiring manual GUI interaction
- · Commercial large model providers (for certain use cases)
More efficient automation of web-based and GUI-driven tasks through improved AI agents.
Reduced operational costs and increased productivity for businesses adopting these advanced GUI agents.
Potential for new business models and services built around highly autonomous and adaptable AI assistants for digital tasks.
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Read at arXiv cs.LG