SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks

• URL: http://arxiv.org/abs/2506.14512v1
• Date: Tue, 17 Jun 2025 13:40:00 GMT
• Title: SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks
• Authors: Zijian Song, Xiaoxin Lin, Qiuming Huang, Guangrun Wang, Liang Lin,
• Abstract summary: We introduce SIRI-Bench, a benchmark designed to evaluate Vision-Language Models' spatial intelligence through video-based reasoning tasks.<n> SIRI-Bench comprises nearly 1K video-question-answer triplets, where each problem is embedded in a realistic 3D scene and captured by video.<n>To facilitate large-scale data synthesis, we develop an Automatic Scene Creation Engine.
• Score: 53.611256895338585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are experiencing rapid advancements in complex reasoning, exhibiting remarkable generalization in mathematics and programming. In contrast, while spatial intelligence is fundamental for Vision-Language Models (VLMs) in real-world interaction, the systematic evaluation of their complex reasoning ability within spatial contexts remains underexplored. To bridge this gap, we introduce SIRI-Bench, a benchmark designed to evaluate VLMs' spatial intelligence through video-based reasoning tasks. SIRI-Bench comprises nearly 1K video-question-answer triplets, where each problem is embedded in a realistic 3D scene and captured by video. By carefully designing questions and corresponding 3D scenes, our benchmark ensures that solving the questions requires both spatial comprehension for extracting information and high-level reasoning for deriving solutions, making it a challenging benchmark for evaluating VLMs. To facilitate large-scale data synthesis, we develop an Automatic Scene Creation Engine. This engine, leveraging multiple specialized LLM agents, can generate realistic 3D scenes from abstract math problems, ensuring faithfulness to the original descriptions. Experimental results reveal that state-of-the-art VLMs struggle significantly on SIRI-Bench, underscoring the challenge of spatial reasoning. We hope that our study will bring researchers' attention to spatially grounded reasoning and advance VLMs in visual problem-solving.

Related papers

• Spatial Understanding from Videos: Structured Prompts Meet Simulation Data [79.52833996220059]
  We present a unified framework for enhancing 3D spatial reasoning in pre-trained vision-language models without modifying their architecture.<n>This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes.
  arXiv  Detail & Related papers  (2025-06-04T07:36:33Z)
• ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models [47.237216851265316]
  Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and reasoning about visual content.<n>Current VLMs excel primarily at egocentric spatial reasoning (from the camera's perspective) but fail to generalize to allocentric viewpoints.<n>We introduce ViewSpatial-Bench, the first comprehensive benchmark designed specifically for multi-viewpoint spatial localization recognition evaluation.
  arXiv  Detail & Related papers  (2025-05-27T17:59:26Z)
• VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction [86.82819259860186]
  We introduce VLM-3R, a unified framework for Vision-Language Models (VLMs) that incorporates 3D Reconstructive instruction tuning.<n>VLM-3R processes monocular video frames by employing a geometry encoder to derive implicit 3D tokens that represent spatial understanding.
  arXiv  Detail & Related papers  (2025-05-26T17:56:30Z)
• SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding [64.15606979785355]
  Multimodal large language models (MLLMs) have achieved impressive success in question-answering tasks, yet their capabilities for spatial understanding are less explored.<n>This work investigates a critical question: do existing MLLMs possess 3D spatial perception and understanding abilities?
  arXiv  Detail & Related papers  (2025-05-22T17:59:03Z)
• iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs [4.381263829108405]
  Vision-Language Models (VLMs) are known to struggle with spatial reasoning and visual alignment.<n>We introduce iVISPAR, an interactive multi-modal benchmark designed to evaluate the spatial reasoning capabilities of VLMs acting as agents.
  arXiv  Detail & Related papers  (2025-02-05T14:29:01Z)
• When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models [113.18524940863841]
  This survey provides a comprehensive overview of the methodologies enabling large language models to process, understand, and generate 3D data. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs) It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue.
  arXiv  Detail & Related papers  (2024-05-16T16:59:58Z)
• OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning [68.45848423501927]
  We propose a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning.<n>Our approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions.
  arXiv  Detail & Related papers  (2024-05-02T17:59:24Z)
• SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors [42.85605789984155]
  Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA) We present SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner. Our spatial reasoning-imbued VLM performs well on various forms of spatial VQA and can extend to help in various downstream robotics tasks such as pick and stack and trajectory planning.
  arXiv  Detail & Related papers  (2024-03-18T17:38:29Z)
• SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities [59.39858959066982]
  understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. We develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
  arXiv  Detail & Related papers  (2024-01-22T18:01:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.