paper-with-me

홈 › Papers

BEAP-Agent: Backtrackable Execution and Adaptive Planning for GUI Agents

2026-01-29 · Ziyu Lu, Tengjin Weng, Yiying Yang, Yuhang Zhao, Xinxin Huang, Wenhao Jiang arxiv

GUI agents are designed to automate repetitive tasks and enhance productivity. However, existing GUI agents struggle to recover once they follow an incorrect exploration path, often leading to task failure. In this work, we model GUI task execution as a DFS process and propose BEAP-Agent, a DFS-based framework that supports long-range, multi-level state backtracking with dynamic task tracking and updating. The framework consists of three collaborative components: Planner, Executor, and Tracker. Together, they enable effective task exploration and execution. BEAP-Agent fills the gap in systematic backtracking mechanisms for GUI agents, offering a systematic solution for long-horizon task exploration. We conducted a systematic evaluation on the OSWorld benchmark, where BEAP-Agent achieved an accuracy of 28.2%, validating the effectiveness of the proposed method.

📄 PDF Abstract BibTeX arXiv:2601.21352

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents

2026-02-23 · Jongwon Jeong, Jungtaek Kim, Kangwook Lee arxiv

Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment. However, they remain vulnerable in environments where a single error often le…

Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective

2026-03-15 · Mohamed Aghzal, Gregory J. Stein, Ziyu Yao arxiv

Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering …

Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol

2025-12-03 · Niklas Jobs, Luis Miguel Vieira da Silva, Jayanth Somashekaraiah, Maximilian Weigand 외 arxiv

Industrial automation increasingly requires flexible control strategies that can adapt to changing tasks and environments. Agents based on Large Language Models (LLMs) offer potential for such adaptive planning and execu…

State and Memory is All You Need for Robust and Reliable AI Agents

2025-06-30 · Matthew Muhoberac, Atharva Parikh, Nirvi Vakharia, Saniya Virani 외

Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, plan…

AllBenchmarkingDecision MakingNatural Language Understanding+1

R-HTN: Rebellious Online HTN Planning for Safety and Game AI

2026-02-01 · Hector Munoz-Avila, David W. Aha, Paola Rizzo arxiv

We introduce online Hierarchical Task Network (HTN) agents whose behaviors are governed by a set of built-in directives \D. Like other agents that are capable of rebellion (i.e., {\it intelligent disobedience}), our agen…