# LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

arXiv: https://arxiv.org/abs/2605.27365 · HTML: https://mlsanity.com/paper/2605.27365/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 185 of its 239 references that are in the map.
- First posted on arXiv 26 May 2026.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 185)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (2023)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [LISA: Reasoning Segmentation via Large Language Model](https://mlsanity.com/paper/2308.00692/) (2023)
- [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://mlsanity.com/paper/2309.12284/) (2023)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://mlsanity.com/paper/2412.10302/) (2024)

## Built on by (most cited first, 28 of 28)

- [Cosmos 3: Omnimodal World Models for Physical AI](https://mlsanity.com/paper/2606.02800/) (2026)
- [Vision as Unified Multimodal Generation](https://mlsanity.com/paper/2607.06560/) (2026)
- [SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them](https://mlsanity.com/paper/2607.27703/) (2026)
- [Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding](https://mlsanity.com/paper/2607.24407/) (2026)
- [HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis](https://mlsanity.com/paper/2609.26793/) (2026)
- [UniRef-UAV: A Multimodal Benchmark for Universal Referring in UAV Imagery](https://mlsanity.com/paper/2607.08267/) (2026)
- [OVEarth-Bench: Evaluating Category Breadth and Query Diversity for Open-Vocabulary Earth Observation](https://mlsanity.com/paper/2607.27278/) (2026)
- [LocAnyMed: Vision-Language Grounding for Multimodal Medical Images](https://mlsanity.com/paper/2608.03322/) (2026)
- [Thinking with Anchors: Grounded and Efficient Document Reasoning](https://mlsanity.com/paper/2608.04424/) (2026)
- [Falcon Perception-HD: High Density Perception via Reinforcement Learning](https://mlsanity.com/paper/2608.18881/) (2026)
- [PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence](https://mlsanity.com/paper/2608.25299/) (2026)
- [Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2608.28192/) (2026)
- [TempoGround: State-Aware Streaming Visual Grounding with Vision-Language Models](https://mlsanity.com/paper/2609.02359/) (2026)
- [What Looks Like a Capability Limit in Vision-Language Models Is a Readout Limit](https://mlsanity.com/paper/2609.27408/) (2026)
- [A Unified Framework and Dataset for Oriented Object Visual Grounding in Remote Sensing](https://mlsanity.com/paper/2609.28230/) (2026)
- [HEIR: Learning Human-Entity Interactions with Functional Roles](https://mlsanity.com/paper/2609.35955/) (2026)
- [S4VY: Segment Anything in Feed-Forward 4D Visual Geometry](https://mlsanity.com/paper/2609.36875/) (2026)
- [PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligence](https://mlsanity.com/paper/2609.37712/) (2026)
- [EviRover: Reinforcing Agentic Perception Beyond a Glance](https://mlsanity.com/paper/2609.40230/) (2026)
- [AutoGUIWorld: Image Generators as Visual World Models for GUI Agent](https://mlsanity.com/paper/2610.01215/) (2026)
- [IRSTD-Agent: Agentic Infrared Small Target Detection via Zoom-Guided Interaction Learning](https://mlsanity.com/paper/2610.05342/) (2026)
- [NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime](https://mlsanity.com/paper/2610.10787/) (2026)
- [Jag: Direct Box Prediction for Efficient Visual Grounding](https://mlsanity.com/paper/2610.13239/) (2026)
- [Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation](https://mlsanity.com/paper/2610.13502/) (2026)
- [Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection](https://mlsanity.com/paper/2610.14647/) (2026)
- [OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding](https://mlsanity.com/paper/2610.18829/) (2026)
- [WinARDet: Compressed Window Auto-Regression for Long-Horizon Vision-Language Grounding](https://mlsanity.com/paper/2610.19419/) (2026)
- [ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions](https://mlsanity.com/paper/2610.20352/) (2026)

## Abstract (arXiv)

Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely independently. This token-by-token decoding mismatches the coupled structure of box geometry and creates a practical inference bottleneck due to strictly sequential generation. We introduce LocateAnything, a unified generative grounding and detection framework based on Parallel Box Decoding (PBD). By decoding geometric elements such as bounding boxes and points as atomic units in a single step, LocateAnything preserves intra-box geometric coherence and unlocks substantial parallelism. We show that PBD improves both decoding throughput and localization accuracy. We further develop a scalable data engine and curate LocateAnything-Data, a large-scale dataset with more than 138 million training samples, substantially increasing data diversity for high-precision localization. Extensive evaluations show that LocateAnything advances the speed-accuracy frontier, achieving significantly higher decoding throughput while improving high-IoU localization quality across diverse benchmarks. The results highlight the complementary benefits of Parallel Box Decoding and large-scale training data in enabling efficient and precise unified visual grounding and detection.