mlsanityData to 14 Oct 2026

Enhancing Spatial Reasoning through Visual and Textual Thinking

arXiv 2507.20529 ↗First posted 28 Jul 2025cs.CV cs.AIcited by 4

On the map: LLM reasoning → Spatial and embodied reasoning

Abstract (arXiv)

The spatial reasoning task aims to reason about the spatial relationships in 2D and 3D space, which is a fundamental capability for Visual Question Answering (VQA) and robotics. Although vision language models (VLMs) have developed rapidly in recent years, they are still struggling with the spatial reasoning task. In this paper, we introduce a method that can enhance Spatial reasoning through Visual and Textual thinking Simultaneously (SpatialVTS). In the spatial visual thinking phase, our model is trained to generate location-related specific tokens of essential targets automatically. Not only are the objects mentioned in the problem addressed, but also the potential objects related to the reasoning are considered. During the spatial textual thinking phase, Our model conducts long-term thinking based on visual cues and dialogues, gradually inferring the answers to spatial reasoning problems. To effectively support the model's training, we perform manual corrections to the existing spatial reasoning dataset, eliminating numerous incorrect labels resulting from automatic annotation, restructuring the data input format to enhance generalization ability, and developing thinking processes with logical reasoning details. Without introducing additional information (such as masks or depth), our model's overall average level in several spatial understanding tasks has significantly improved compared with other models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 24 of its 43 references that are in the map. First posted on arXiv 28 Jul 2025.

Builds on

24 of 43 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models3 Jun

Other references · 23

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2025Qwen2.5-VL Technical Reportcited by 5,587
  6. 2024GPT-4o System Cardcited by 4,588
  7. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  8. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,719
  9. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
  10. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  11. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  12. 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,599
11 more
  1. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  2. 2024OpenAI o1 System Cardcited by 1,244
  3. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  4. 2024LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Modelscited by 584
  5. 2024BLINK: Multimodal Large Language Models Can See but Not Perceivecited by 550
  6. 2025Visual-RFT: Visual Reinforcement Fine-Tuningcited by 513
  7. 2024LLaVA-CoT: Let Vision Language Models Reason Step-by-Stepcited by 454
  8. 2025Imagine while Reasoning in Space: Multimodal Visualization-of-Thoughtcited by 206
  9. 2025LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMscited by 147
  10. 2020Scene Graph Reasoning for Visual Question Answeringcited by 50
  11. 2025Introducing Visual Perception Token into Multimodal Large Language Modelcited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Modelscited by 37
  2. 2025Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarkscited by 33
  3. 2026ESPIRE: A Diagnostic Benchmark for Embodied Spatial Reasoning of Vision-Language Modelscited by 3
  4. 2026Visual Credit Audit for Multimodal Spatial Reasoningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
  2. 2026SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language Models
  3. 2026SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?
  4. 2024Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
  5. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
  6. 2025SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data
  7. 2025SpatialReasoner: Towards Explicit and Generalizable 3D Spatial Reasoning
  8. 2024Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models