HiconAgent: History Context-aware Policy Optimization for GUI Agents
Xurui Zhou, Gongwei Chen, Yuquan Xie, Zaijing Li, Kaiwen Zhou, Shuai Wang, Shuo Yang, Zhuotao Tian, Rui Shao

TL;DR
HiconAgent introduces a novel history-aware policy optimization method for GUI agents, improving efficiency and accuracy in sequential navigation tasks by adaptively utilizing relevant historical context.
Contribution
The paper proposes HCPO with DCS and AHC components, enabling adaptive history sampling and compression, leading to better performance and efficiency in GUI navigation.
Findings
HiconAgent-3B outperforms larger models on GUI-Odyssey by +8.46% accuracy.
Achieves up to 2.47x speedup and 60% FLOPs reduction.
Demonstrates strong performance on multiple GUI benchmarks.
Abstract
Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can improve decision making, naive use of full history leads to excessive computational overhead and distraction from irrelevant information. To address this, we introduce HiconAgent, a GUI agent trained with History Context-aware Policy Optimization (HCPO) for efficient and effective utilization of historical information. HCPO optimizes history usage in both sampling and policy updates through two complementary components: (1) Dynamic Context Sampling (DCS) presents the agent with variable length histories during sampling, enabling adaptive use of the most relevant context; (2) Anchor-guided History Compression (AHC) refines the policy update phase with a dual branch strategy where the compressed branch removes…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Artificial Intelligence in Games · Personal Information Management and User Behavior
