GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection

• URL: http://arxiv.org/abs/2605.28534v1
• Date: Wed, 27 May 2026 14:29:41 GMT
• Title: GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection
• Abstract summary: Existing solutions rely on expensive multi-agent scaffolding or conventional post-training paradigms.<n>We propose GUI-CIDER, a mid-training method that explicitly internalizes GUI world knowledge through Causal Internalization and Density-aware Exemplar Reselection.<n>Experiments on two GUI knowledge benchmarks and three task completion benchmarks demonstrate that GUI-CIDER consistently improves both the agent's understanding of GUI operations and its task success rates.
• Score: 26.58169185548789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fundamentally bottlenecked by a lack of world knowledge about GUI operations. Existing solutions typically rely on expensive multi-agent scaffolding or conventional post-training paradigms, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). However, post-training only allows agents to implicitly absorb world knowledge through action annotations or reward signals, leading to inefficient trajectory memorization rather than genuine comprehension. Therefore, an approach that enables explicit learning of this knowledge is imperative. To this end, we propose GUI-CIDER, a mid-training method that explicitly internalizes GUI world knowledge through Causal Internalization and Density-aware Exemplar Reselection. GUI-CIDER operates in three stages: (1) data synthesis, which distills static planning and dynamic causal knowledge from GUI trajectories into text; (2) exemplar reselection, which filters the corpus by rewarding causal structures and penalizing semantic redundancy; and (3) mid-training, where the refined data is used to embed the acquired knowledge. Extensive experiments on two GUI knowledge benchmarks and three task completion benchmarks demonstrate that GUI-CIDER consistently improves both the agent's understanding of GUI operations and its task success rates.The codes are available at https://github.com/Wuzheng02/GUI-CIDER.

Related papers

• GUI-Eyes: Tool-Augmented Perception for Visual Grounding in GUI Agents [39.807839972627015]
  We present GUI-Eyes, a reinforcement learning framework for active visual perception in GUI tasks.<n>We introduce a progressive perception strategy that decomposes decision-making into coarse exploration and fine-grained grounding.<n>On the ScreenSpot-Pro benchmark, GUI-Eyes-3B achieves 44.8% grounding accuracy using only 3k labeled samples.
  arXiv  Detail & Related papers  (2026-01-14T14:27:28Z)
• EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration [16.593979443102754]
  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.
  arXiv  Detail & Related papers  (2025-12-22T13:42:18Z)
• 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)
• MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning [83.81404871748438]
  MagicGUI is a foundational mobile GUI agent designed to address critical challenges in perception, grounding, and reasoning within real-world mobile GUI environments.<n>The framework is underpinned by six key components, including a comprehensive and accurate dataset, enhanced perception and grounding capabilities, a comprehensive and unified action space, and planning-oriented reasoning mechanisms.
  arXiv  Detail & Related papers  (2025-07-19T12:33:43Z)
• 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)
• 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)
• UI-TARS: Pioneering Automated GUI Interaction with Native Agents [58.18100825673032]
  This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions.<n>In the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively)
  arXiv  Detail & Related papers  (2025-01-21T17:48:10Z)
• 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)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• GUICourse: From General Vision Language Models to Versatile GUI Agents [75.5150601913659]
  We contribute GUICourse, a suite of datasets to train visual-based GUI agents.<n>First, we introduce the GUIEnv dataset to strengthen the OCR and grounding capabilities of VLMs.<n>Then, we introduce the GUIAct and GUIChat datasets to enrich their knowledge of GUI components and interactions.
  arXiv  Detail & Related papers  (2024-06-17T08:30:55Z)

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.