DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding
The paper presents DRS-GUI, a training-free framework for GUI grounding that enhances the performance of Multimodal Large Language Models. It introduces a lightweight UI Perceptor that mimics human-like perceptual actions to identify relevant regions in complex user interfaces. Experimental results indicate a significant improvement in grounding performance, achieving a 14% increase on benchmark tests.
- ▪DRS-GUI is designed to improve the grounding of instruction-relevant elements in high-resolution screenshots.
- ▪The framework employs a UI Perceptor that performs actions such as Focus, Shift, and Scatter to explore interfaces.
- ▪A Monte Carlo Tree Search-based Action Planner is used to dynamically schedule perceptual actions and evaluate region quality.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15542 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | nk36S_My8LAj |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.15542 (cs) [Submitted on 15 May 2026] Title:DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding Authors:Yichao Liu, Huawen Shen, Liu Yu, Shiyu Liu, Zeyu Chen, Yu Zhou View a PDF of the paper titled DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding, by Yichao Liu and 5 other authors View PDF HTML (experimental) Abstract:GUI agents powered by Multimodal Large Language Models (MLLMs) have demonstrated impressive capability in understanding and executing user instructions. However, accurately grounding instruction-relevant elements from high-resolution screenshots cluttered with irrelevant UI components remains challenging for existing approaches.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
