PAFFA: Premeditated Actions For Fast Agents

• URL: http://arxiv.org/abs/2412.07958v2
• Date: Fri, 04 Apr 2025 17:33:53 GMT
• Title: PAFFA: Premeditated Actions For Fast Agents
• Authors: Shambhavi Krishna, Zheng Chen, Yuan Ling, Xiaojiang Huang, Yingjie Li, Fan Yang, Xiang Li,
• Abstract summary: We introduce PAFFA, a method that makes LLMs faster and more accurate in completing tasks on the internet using a novel inference-time technique.<n>PAFFA drastically reduces inference time tokens by 87% while maintaining robust performance.<n>Unravel's ability to update its action library based on explorations allows generalization and adaptation to unseen websites.
• Score: 19.576180667174366
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Modern AI assistants have made significant progress in natural language understanding and tool-use, with emerging efforts to interact with Web interfaces. However, current approaches that heavily rely on repeated LLM-driven HTML parsing are computationally expensive and error-prone, particularly when handling dynamic web interfaces and multi-step tasks. We introduce PAFFA (Premeditated Actions For Fast Agents), a method that makes LLMs faster and more accurate in completing tasks on the internet using a novel inference-time technique that requires no task-specific training. PAFFA constructs an 'Action Library', leveraging the parametric knowledge of the base LLM to pre-compute browser interaction patterns that generalize across tasks. By strategically re-using LLM inference across tasks - either via 'Dist-Map' for task-agnostic identification of key interactive web elements, or 'Unravel' for first-encounter, stateful exploration of novel tasks/sites) - PAFFA drastically reduces inference time tokens by 87% while maintaining robust performance (achieving 0.57 vs. 0.50 step accuracy compared to baseline). Further, Unravel's ability to update its action library based on explorations allows generalization and adaptation to unseen websites. In sum, this work exhibits that LLM reasoning sequences can generalize across prompts, offering a way to scale inference-time techniques for internet-scale data with sublinear token count.

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