# MVP: Multiple View Prediction Improves GUI Grounding

arXiv: https://arxiv.org/abs/2512.08529 · HTML: https://mlsanity.com/paper/2512.08529/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 47 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 30 of 31)

- [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)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://mlsanity.com/paper/2401.01614/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://mlsanity.com/paper/2410.23218/) (2024)
- [PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction](https://mlsanity.com/paper/2410.17247/) (2024)
- [Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction](https://mlsanity.com/paper/2412.04454/) (2024)
- [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://mlsanity.com/paper/2504.07981/) (2025)
- [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)
- [Aria-UI: Visual Grounding for GUI Instructions](https://mlsanity.com/paper/2412.16256/) (2024)
- [Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis](https://mlsanity.com/paper/2505.13227/) (2025)
- [GUI Agents with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2411.04890/) (2024)
- [GUI Agents: A Survey](https://mlsanity.com/paper/2412.13501/) (2024)
- [VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?](https://mlsanity.com/paper/2404.05955/) (2024)
- [DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models](https://mlsanity.com/paper/2411.15024/) (2024)
- [GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents](https://mlsanity.com/paper/2506.03143/) (2025)
- [Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2505.12370/) (2025)
- [UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction](https://mlsanity.com/paper/2503.15661/) (2025)
- [PruneVid: Visual Token Pruning for Efficient Video Large Language Models](https://mlsanity.com/paper/2412.16117/) (2024)
- [A Survey on (M)LLM-Based GUI Agents](https://mlsanity.com/paper/2504.13865/) (2025)
- [Visual Test-time Scaling for GUI Agent Grounding](https://mlsanity.com/paper/2505.00684/) (2025)
- [Phi-Ground Tech Report: Advancing Perception in GUI Grounding](https://mlsanity.com/paper/2507.23779/) (2025)
- [UI-Ins: Enhancing GUI Grounding with Multi-Perspective Instruction-as-Reasoning](https://mlsanity.com/paper/2510.20286/) (2025)
- [FlexSelect: Flexible Token Selection for Efficient Long Video Understanding](https://mlsanity.com/paper/2506.00993/) (2025)
- [Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning](https://mlsanity.com/paper/2412.10840/) (2024)
- [Learning GUI Grounding with Spatial Reasoning from Visual Feedback](https://mlsanity.com/paper/2509.21552/) (2025)
- [GUI-ARP: Enhancing Grounding with Adaptive Region Perception for GUI Agents](https://mlsanity.com/paper/2509.15532/) (2025)
- [Generalist Scanner Meets Specialist Locator: A Synergistic Coarse-to-Fine Framework for Robust GUI Grounding](https://mlsanity.com/paper/2509.24133/) (2025)

## Built on by (most cited first, 12 of 12)

- [UI-Venus-1.5 Technical Report](https://mlsanity.com/paper/2602.09082/) (2026)
- [What Happens Before Decoding? Prefill Determines GUI Grounding in VLMs](https://mlsanity.com/paper/2605.12549/) (2026)
- [VISTA: View-Consistent Self-Verified Training for GUI Grounding](https://mlsanity.com/paper/2606.14579/) (2026)
- [One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding](https://mlsanity.com/paper/2606.30084/) (2026)
- [UI-Venus-2 Technical Report](https://mlsanity.com/paper/2609.00028/) (2026)
- [From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents](https://mlsanity.com/paper/2609.04869/) (2026)
- [GUI-Lens: Coarse-to-Fine Cropping for GUI Grounding with General-Purpose VLMs](https://mlsanity.com/paper/2608.03270/) (2026)
- [LookAgain: Closed-Loop GUI Grounding with Visually Grounded Reflection](https://mlsanity.com/paper/2608.09723/) (2026)
- [RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection](https://mlsanity.com/paper/2609.18690/) (2026)
- [ScreenHaystack: Finding Blind Zones in GUI Grounding](https://mlsanity.com/paper/2609.32036/) (2026)
- [Recurrent Latent Visual Search for GUI Grounding](https://mlsanity.com/paper/2610.05185/) (2026)
- [ConClick: Element-Wise Contrastive Learning Improves GUI Grounding](https://mlsanity.com/paper/2610.13268/) (2026)

## Abstract (arXiv)

GUI grounding, which translates natural language instructions into precise pixel coordinates, is essential for developing practical GUI agents. However, we observe that existing grounding models exhibit significant coordinate prediction instability, minor visual perturbations (e.g. cropping a few pixels) can drastically alter predictions, flipping results between correct and incorrect. This instability severely undermines model performance, especially for samples with high-resolution and small UI elements. To address this issue, we propose Multi-View Prediction (MVP), a training-free framework that enhances grounding performance through multi-view inference. Our key insight is that while single-view predictions may be unstable, aggregating predictions from multiple carefully cropped views can effectively distinguish correct coordinates from outliers. MVP comprises two components: (1) Attention-Guided View Proposal, which derives diverse views guided by instruction-to-image attention scores, and (2) Multi-Coordinates Clustering, which ensembles predictions by selecting the centroid of the densest spatial cluster. Extensive experiments demonstrate MVP's effectiveness across various models and benchmarks. Notably, on ScreenSpot-Pro, MVP boosts UI-TARS-1.5-7B to 56.1%, GTA1-7B to 61.7%, Qwen3VL-8B-Instruct to 65.3%, and Qwen3VL-32B-Instruct to 74.0%. The code is available at https://github.com/ZJUSCL/MVP.