# DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning

arXiv: https://arxiv.org/abs/2507.00008 · HTML: https://mlsanity.com/paper/2507.00008/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 28 of 28)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception](https://mlsanity.com/paper/2401.16158/) (2024)
- [GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents](https://mlsanity.com/paper/2504.10458/) (2025)
- [ShowUI: One Vision-Language-Action Model for GUI Visual Agent](https://mlsanity.com/paper/2411.17465/) (2024)
- [V-STaR: Training Verifiers for Self-Taught Reasoners](https://mlsanity.com/paper/2402.06457/) (2024)
- [OmniParser for Pure Vision Based GUI Agent](https://mlsanity.com/paper/2408.00203/) (2024)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [UFO: A UI-Focused Agent for Windows OS Interaction](https://mlsanity.com/paper/2402.07939/) (2024)
- [Aria-UI: Visual Grounding for GUI Instructions](https://mlsanity.com/paper/2412.16256/) (2024)
- [AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents](https://mlsanity.com/paper/2410.13825/) (2024)
- [VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents](https://mlsanity.com/paper/2408.06327/) (2024)
- [Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2505.12370/) (2025)
- [GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents](https://mlsanity.com/paper/2505.15810/) (2025)
- [The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use](https://mlsanity.com/paper/2411.10323/) (2024)
- [ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification](https://mlsanity.com/paper/2502.14565/) (2025)
- [Visual Test-time Scaling for GUI Agent Grounding](https://mlsanity.com/paper/2505.00684/) (2025)
- [Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems](https://mlsanity.com/paper/2503.06470/) (2025)
- [Improved GUI Grounding via Iterative Narrowing](https://mlsanity.com/paper/2411.13591/) (2024)
- [ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search](https://mlsanity.com/paper/2505.15259/) (2025)

## Built on by (most cited first, 30 of 31)

- [InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization](https://mlsanity.com/paper/2508.05731/) (2025)
- [Test-Time Reinforcement Learning for GUI Grounding via Region Consistency](https://mlsanity.com/paper/2508.05615/) (2025)
- [VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents](https://mlsanity.com/paper/2509.07553/) (2025)
- [RefineShot: Rethinking Cinematography Understanding with Foundational Skill Evaluation](https://mlsanity.com/paper/2510.02423/) (2025)
- [GUI-Eyes: Tool-Augmented Perception for Visual Grounding in GUI Agents](https://mlsanity.com/paper/2601.09770/) (2026)
- [Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding](https://mlsanity.com/paper/2512.05941/) (2025)
- [UI-Zoomer: Uncertainty-Driven Adaptive Zoom-In for GUI Grounding](https://mlsanity.com/paper/2604.14113/) (2026)
- [Gated Differentiable Working Memory for Long-Context Language Modeling](https://mlsanity.com/paper/2601.12906/) (2026)
- [VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG](https://mlsanity.com/paper/2604.05418/) (2026)
- [Zoom to Essence: Trainless GUI Grounding by Inferring upon Interface Elements](https://mlsanity.com/paper/2603.14448/) (2026)
- [Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding](https://mlsanity.com/paper/2604.21268/) (2026)
- [DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding](https://mlsanity.com/paper/2605.15542/) (2026)
- [See, Think, Act: Teaching Multimodal Agents to Effectively Interact with GUI by Identifying Toggles](https://mlsanity.com/paper/2509.13615/) (2025)
- [Generalist Scanner Meets Specialist Locator: A Synergistic Coarse-to-Fine Framework for Robust GUI Grounding](https://mlsanity.com/paper/2509.24133/) (2025)
- [Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts](https://mlsanity.com/paper/2602.02468/) (2026)
- [What Happens Before Decoding? Prefill Determines GUI Grounding in VLMs](https://mlsanity.com/paper/2605.12549/) (2026)
- [MEGA-GUI: Multi-stage Enhanced Grounding Agents for GUI Elements](https://mlsanity.com/paper/2511.13087/) (2025)
- [Beyond Clicking:A Step Towards Generalist GUI Grounding via Text Dragging](https://mlsanity.com/paper/2601.06031/) (2025)
- [AutoFocus: Uncertainty-Aware Active Visual Search for GUI Grounding](https://mlsanity.com/paper/2605.02630/) (2026)
- [BAMI: Training-Free Bias Mitigation in GUI Grounding](https://mlsanity.com/paper/2605.06664/) (2026)
- [Mitigating Coordinate Prediction Bias from Positional Encoding Failures](https://mlsanity.com/paper/2510.22102/) (2025)
- [GUI-C2: Coarse-to-Fine GUI Grounding via Difficulty-Aware Reinforcement Learning](https://mlsanity.com/paper/2605.30884/) (2026)
- [One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding](https://mlsanity.com/paper/2606.30084/) (2026)
- [Trifuse: Enhancing Attention-Based GUI Grounding via Multimodal Fusion](https://mlsanity.com/paper/2602.06351/) (2026)
- [Thinking with Patterns: Breaking the Perceptual Bottleneck in Visual Planning via Pattern Induction](https://mlsanity.com/paper/2605.16848/) (2026)
- [Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning](https://mlsanity.com/paper/2606.08231/) (2026)
- [V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure](https://mlsanity.com/paper/2607.21151/) (2026)
- [LookAgain: Closed-Loop GUI Grounding with Visually Grounded Reflection](https://mlsanity.com/paper/2608.09723/) (2026)
- [Efficient GUI Agents: A Systems Survey of Observation, Memory, Action, and Runtime Optimization](https://mlsanity.com/paper/2609.02309/) (2026)
- [Recurrent Latent Visual Search for GUI Grounding](https://mlsanity.com/paper/2610.05185/) (2026)

## Abstract (arXiv)

Grounding natural language queries in graphical user interfaces (GUIs) poses unique challenges due to the diversity of visual elements, spatial clutter, and the ambiguity of language. In this paper, we introduce DiMo-GUI, a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization. Instead of treating the GUI as a monolithic image, our method splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. When predictions are ambiguous or incorrect, DiMo-GUI dynamically focuses attention by generating candidate focal regions centered on the model's initial predictions and incrementally zooms into subregions to refine the grounding result. This hierarchical refinement process helps disambiguate visually crowded layouts without the need for additional training or annotations. We evaluate our approach on standard GUI grounding benchmarks and demonstrate consistent improvements over baseline inference pipelines, highlighting the effectiveness of combining modality separation with region-focused reasoning.