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Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes

Conference: ICLR2026 arXiv: 2510.19400
Code: Project Page (open-sourced)
Area: Multimodal VLM Keywords: multi-view spatial reasoning, benchmark, embodied AI, VLM evaluation, robotic manipulation

TL;DR

This paper proposes MV-RoboBench, the first benchmark integrating multi-view spatial reasoning with robotic manipulation tasks, systematically evaluating 40+ VLMs (open-source, closed-source, and reasoning-enhanced). The best-performing model, GPT-5, achieves only 56.4% accuracy, far below the human baseline of 91.0%. The study further reveals a positive correlation between spatial and robotic reasoning, and that performance on single-view benchmarks does not reliably transfer to multi-view settings.

Background & Motivation

  • VLMs serve as core components of Embodied AI, providing perceptual and reasoning capabilities for Vision-Language-Action (VLA) models.
  • Most VLM evaluations focus on single-view settings, leaving multi-view information integration severely underexplored.
  • Multi-camera configurations have become standard in robotic platforms, offering complementary viewpoints to mitigate occlusion and depth ambiguity.
  • Existing spatial reasoning benchmarks (EmbSpatial-Bench, RoboSpatial, etc.) primarily target single-view reasoning; ERQA and MMSI-Bench include only limited multi-view data.
  • All-Angles Bench and Ego3D-Bench employ multi-view inputs but are restricted to photo alignment or navigation perception, lacking manipulation-oriented embodied reasoning.

Method

Overall Architecture: MV-RoboBench Benchmark Design

Built upon the AgiWorld and BridgeV2 datasets, MV-RoboBench contains 1,708 manually annotated multiple-choice questions spanning Spatial Understanding and Robotic Execution, organized into 8 sub-tasks:

  • Spatial Understanding (4 sub-tasks):

    • Cross-View Matching: identifying the same object across viewpoints
    • Distance Judgement: estimating relative distances between objects
    • Viewpoint Identification: reasoning about viewpoint transformations
    • 3D Spatial Consistency: maintaining consistent 3D spatial relationships

  • Robotic Execution (4 sub-tasks):

    • Action Planning: planning multi-step action sequences
    • Step Execution: verifying correctness of a single next step
    • Trajectory Selection: assessing feasibility of candidate motion paths
    • Affordance Recognition: evaluating object interaction feasibility

Key Design 1: Multi-Stage Human Quality Control Pipeline

  • Data Collection: Rule-based filtering + GPT-4.1-assisted screening (for triage only, not QA generation) + human verification.
  • QA Generation: Task-specific templates combined with trained annotators to construct five-choice QA pairs with plausible yet distinguishable distractors.
  • Iterative Review: Multiple rounds of annotation, revision, and answer distribution balancing to eliminate bias.

Key Design 2: CoT-Inspired Augmentation Exploration

Three CoT-style augmentation strategies are systematically investigated: 1. Text CoT (w text): GPT-4.1-generated scene descriptions as supplementary textual context. 2. Visual CoT (w vggt): Novel view synthesis via VGGT to provide additional visual evidence. 3. Structural CoT (w depth): Depth estimation via MoGe-2 to introduce geometric constraints.

Key Design 3: Dual-Axis Correlation Analysis

  • Internal correlation axis: Relationship between spatial reasoning and robotic execution performance within multi-view scenes.
  • External transfer axis: Whether performance on a single-view spatial benchmark (OmniSpatial) reliably predicts multi-view embodied reasoning capability.

Experiments

Main Results

Model Type Representative Model Average Accuracy
Random Baseline — 19.7%
Closed-Source VLM GPT-4.1 30.9%
Open-Source VLM Qwen2.5-vl-72B 24.3%
Open-Source MoE Llama-4-Maverick 26.1%
Reasoning Model GPT-5 56.4%
Reasoning Model Gemini-2.5-pro 49.5%
Human — 91.0%

Ablation Study: CoT Augmentation

Model Base w cot w text w vggt w depth
Qwen2.5-vl-7B 20.84 20.49 20.90 20.02 21.14
Gemma-3-12B 20.49 24.19 18.43 18.31 20.41
GPT-4.1 29.87 29.84 31.66 28.02 33.12

Key Findings

  1. Reasoning capability is the primary differentiator: Reasoning-enhanced models (GPT-5, o4-mini) substantially outperform perception-focused models, yet remain far below human performance.
  2. 3D Spatial Consistency is the most challenging sub-task: Most non-reasoning models perform at or below random chance (~19.07%) on this task.
  3. CoT augmentation effects are model-dependent: Novel view synthesis generally degrades performance; depth priors are effective only for high-capacity models; CoT prompting is most beneficial for mid-scale open-source models.
  4. Spatial and robotic reasoning are positively correlated: This holds only for models with sufficient cross-view fusion capability.
  5. Single-view to multi-view transfer fails: Strong performance on OmniSpatial does not reliably predict multi-view embodied reasoning ability.

Highlights & Insights

  • First systematic benchmark for multi-view robotic manipulation spatial reasoning, filling a critical gap in the field.
  • Evaluation covers 40+ models across five categories, providing comprehensive experimental coverage.
  • Dual-axis analysis reveals an important negative result: single-view spatial capability does not transfer reliably.
  • All 1.7K QA items are fully human-curated, with data covering both single-arm and dual-arm manipulation scenarios.

Limitations & Future Work

  • Only 2D images are used as input; the impact of explicit 3D representations (point clouds, meshes) remains unexplored.
  • Camera configurations are fixed by the source datasets; the effect of varying camera layouts is not investigated.
  • The multiple-choice format precludes evaluation of open-ended spatial reasoning.
  • CoT augmentation strategies are relatively basic; more advanced approaches such as active view selection are not explored.
  • Spatial reasoning benchmarks: EmbSpatial-Bench, Visual Spatial, RoboSpatial, Spatial-MM, SpatialVLM, and VSI-Bench are all limited to single-view settings.
  • Multi-view benchmarks: All-Angles Bench (photo alignment) and Ego3D-Bench (navigation perception) do not address robotic manipulation.
  • Robotic scene evaluation: ShareRobot (single-view), ERQA (partial multi-view but small scale).
  • Geometry-augmented VLMs: SpatialRGPT, SpatialLLM, and 3D-LLM explore the injection of geometric priors into language models.

Rating

⭐⭐⭐⭐ (4/5)

A solid benchmark contribution with large-scale and systematic evaluation. The dual-axis correlation analysis provides valuable insights. As a benchmark paper, however, the methodological contribution is limited, and the CoT augmentation exploration remains relatively shallow.

title: >- [Paper Review] Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes description: >- [ICLR2026][Multimodal][Spatial Reasoning] Proposes MV-RoboBench, the first VLM evaluation benchmark targeting multi-view spatial reasoning in robotic scenes, comprising 1.7K manually annotated QA pairs across eight sub-tasks in spatial understanding and robotic execution. Experiments show that current state-of-the-art VLMs fall far short of human performance, and that single-view spatial benchmark performance does not reliably transfer to multi-view robotic scenarios. tags: - ICLR2026 - multimodal - spatial reasoning - benchmark - multi-view - robotic manipulation - VLM evaluation - embodied AI