PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models

arXiv:2608.14741 · cs.CV, cs.AI · Submitted 2026-08-13 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-13

Updated: 2026-08-27

Code: https://github.com/sidpatelgit/polycomp

Terminology

• Benchmark — 11× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Compositional Spatial Reasoning — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes

Sources

• MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
• MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
• MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
• Are We on the Right Way for Evaluating Large Vision-Language Models?
• MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
• Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
• InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Models
• SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition

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