PAFFA: Premeditated Actions For Fast Agents

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Pubblicato in:	arXiv.org (Dec 10, 2024), p. n/a
Autore principale:	Shambhavi Krishna
Altri autori:	Chen, Zheng, Kumar, Vaibhav, Huang, Xiaojiang, Li, Yingjie, Yang, Fan, Li, Xiang
Pubblicazione:	Cornell University Library, arXiv.org
Soggetti:	Application programming interface
Accesso online:	Citation/Abstract Full text outside of ProQuest
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Descrizione
Abstract:	Modern AI assistants have made significant progress in natural language understanding and API/tool integration, with emerging efforts to incorporate diverse interfaces (such as Web interfaces) for enhanced scalability and functionality. 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. To overcome these challenges, we introduce PAFFA (Premeditated Actions For Fast Agents), a framework designed to enhance web interaction capabilities through an Action API Library of reusable, verified browser interaction functions. By pre-computing interaction patterns and employing two core methodologies - "Dist-Map" for task-agnostic element distillation and "Unravel" for incremental page-wise exploration - PAFFA reduces inference calls by 87% while maintaining robust performance even as website structures evolve. This framework accelerates multi-page task execution and offers a scalable solution to advance autonomous web agent research.
ISSN:	2331-8422
Fonte:	Engineering Database