PAFFA: Premeditated Actions For Fast Agents

Сохранить в:

Библиографические подробности
Опубликовано в::	arXiv.org (Dec 10, 2024), p. n/a
Главный автор:	Shambhavi Krishna
Другие авторы:	Chen, Zheng, Kumar, Vaibhav, Huang, Xiaojiang, Li, Yingjie, Yang, Fan, Li, Xiang
Опубликовано:	Cornell University Library, arXiv.org
Предметы:	Application programming interface
Online-ссылка:	Citation/Abstract Full text outside of ProQuest
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

Описание
Краткий обзор:	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
Источник:	Engineering Database