EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration

• URL: http://arxiv.org/abs/2512.19396v1
• Date: Mon, 22 Dec 2025 13:42:18 GMT
• Title: EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration
• Authors: Runze Li, Yuwen Zhai, Bo Xu, LiWu Xu, Nian Shi, Wei Zhang, Ran Lin, Liang Wang,
• Abstract summary: We introduce EchoTrail-GUI, a novel framework designed to mimic human-like experiential learning by equipping agents with a dynamic, accessible memory.<n>First, an agent autonomously interacts with GUI environments to build a curated database of successful task trajectories, validated by a reward model.<n>Second, in the Memory Injection stage, upon receiving a new task, our system efficiently retrieves the most relevant past trajectories to serve as actionable ''memories''<n>Third, during GUI Task Inference, these memories are injected as in-context guidance to inform the agent's reasoning and decision-making process.
• Score: 16.593979443102754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation: they treat each task in isolation, lacking a mechanism to systematically learn from past successes. This digital ''amnesia'' results in sub-optimal performance, repeated errors, and poor generalization to novel challenges. To bridge this gap, we introduce EchoTrail-GUI, a novel framework designed to mimic human-like experiential learning by equipping agents with a dynamic, accessible memory. Our framework operates in three distinct stages. First, during Experience Exploration, an agent autonomously interacts with GUI environments to build a curated database of successful task trajectories, validated by a reward model. Crucially, the entire knowledge base construction is thus fully automated, requiring no human supervision. Second, in the Memory Injection stage, upon receiving a new task, our system efficiently retrieves the most relevant past trajectories to serve as actionable ''memories''. Finally, during GUI Task Inference, these memories are injected as in-context guidance to inform the agent's reasoning and decision-making process. We demonstrate the efficacy of our approach on benchmarks including Android World and AndroidLab. The results show that EchoTrail-GUI significantly improves the task success rate and operational efficiency of baseline agents, validating the power of structured memory in creating more robust and intelligent GUI automation.

Related papers

• History-Aware Reasoning for GUI Agents [15.519853892615272]
  Current methods integrate Reinforcement Learning with System-2 Chain-of-Thought, yielding notable gains in reasoning enhancement.<n>We propose a History-Aware Reasoning framework, which encourages an agent to reflect on its own errors and acquire episodic reasoning knowledge.<n>We develop a native end-to-end model, HAR-GUI-3B, which alters the inherent reasoning mode from history-agnostic to history-aware.
  arXiv  Detail & Related papers  (2025-11-12T09:06:25Z)
• CoAct-1: Computer-using Agents with Coding as Actions [94.99657662893338]
  CoAct-1 is a novel multi-agent system that combines GUI-based control with direct programmatic execution.<n>We evaluate our system on the challenging OSWorld benchmark, where CoAct-1 achieves a new state-of-the-art success rate of 60.76%.
  arXiv  Detail & Related papers  (2025-08-05T21:33:36Z)
• MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents [88.35544552383581]
  We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, Linux, iOS, Android, and Web platforms.<n>It comprises four levels: GUI Content Understanding, Element Grounding, Task Automation, and Task Collaboration, covering essential skills for GUI agents.
  arXiv  Detail & Related papers  (2025-07-25T17:59:26Z)
• MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment [63.62778707277929]
  MobileGUI-RL is a scalable framework that trains GUI agent in online environment.<n>It synthesizes a curriculum of learnable tasks through self-exploration and filtering.<n>It adapts GRPO to GUI navigation with trajectory-aware advantages and composite rewards.
  arXiv  Detail & Related papers  (2025-07-08T07:07:53Z)
• Learning, Reasoning, Refinement: A Framework for Kahneman's Dual-System Intelligence in GUI Agents [15.303188467166752]
  We present CogniGUI, a cognitive framework developed to overcome limitations by enabling adaptive learning for GUI automation resembling human-like behavior.<n>To assess the generalization and adaptability of agent systems, we introduce ScreenSeek, a comprehensive benchmark that includes multi application navigation, dynamic state transitions, and cross interface coherence.<n> Experimental results demonstrate that CogniGUI surpasses state-of-the-art methods in both the current GUI grounding benchmarks and our newly proposed benchmark.
  arXiv  Detail & Related papers  (2025-06-22T06:30:52Z)
• Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation [83.92224427735859]
  We introduce a pre-operative critic mechanism that provides effective feedback prior to the actual execution.<n>We develop a reasoning-bootstrapping based data collection pipeline to create a GUI-Critic-Train and a GUI-Critic-Test.<n>Our model offers significant advantages in critic accuracy compared to current MLLMs.
  arXiv  Detail & Related papers  (2025-06-05T04:12:36Z)
• AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning [82.42421823672954]
  AgentCPM-GUI is built for robust and efficient on-device GUI interaction.<n>Our training pipeline includes grounding-aware pre-training to enhance perception.<n>AgentCPM-GUI achieves state-of-the-art performance on five public benchmarks.
  arXiv  Detail & Related papers  (2025-06-02T07:30:29Z)
• GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent [66.34801160469067]
  MLLMs suffer from two key issues: misinterpreting UI components and outdated knowledge.<n>We propose GUI-explorer, a training-free GUI agent that incorporates two fundamental mechanisms.<n>With a task success rate of 53.7% on SPA-Bench and 47.4% on AndroidWorld, GUI-explorer shows significant improvements over SOTA agents.
  arXiv  Detail & Related papers  (2025-05-22T16:01:06Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  Aguvis is a vision-based framework for autonomous GUI agents.<n>It standardizes cross-platform interactions and incorporates structured reasoning via inner monologue.<n>It achieves state-of-the-art performance across offline and real-world online benchmarks.
  arXiv  Detail & Related papers  (2024-12-05T18:58:26Z)
• ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation [30.693616802332745]
  This paper presents a novel benchmark, AssistGUI, to evaluate whether models are capable of manipulating the mouse and keyboard on the Windows platform in response to user-requested tasks. We propose an advanced Actor-Critic framework, which incorporates a sophisticated GUI driven by an AI agent and adept at handling lengthy procedural tasks.
  arXiv  Detail & Related papers  (2023-12-20T15:28:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.