Title: Causal Scaffolding for Physical Reasoning: A Benchmark for Causally-Informed Physical World Understanding in VLMs
Primary area: foundation or frontier models, including LLMs
Keywords: physical reasoning, causality, VLM

Abstract: Understanding and reasoning about the physical world is the foundation of intelligent behavior, yet state-of-the-art vision-language models (VLMs) still fail at causal physical reasoning, often producing plausible but incorrect answers. To systematically address this gap, we introduce CausalPhys, a benchmark of over 3,000 carefully curated video- and image-based questions spanning four domains: Perception, Anticipation, Intervention, and Goal Orientation. Each question is paired with a causal graph that captures underlying interactions and dependencies, enabling fine-grained and interpretable evaluation. We further propose a causal-graph-grounded metric that verifies whether a model’s chain-of-thought reasoning follows correct causal relations, moving beyond answer-only accuracy. Systematic evaluations of leading VLMs on CausalPhys expose consistent failures to capture causal dependencies, underscoring fundamental weaknesses in their physical reasoning. To overcome these shortcomings, we introduce a Causal Rationale-informed Fine-Tuning strategy (CRFT) that scaffolds VLM reasoning with causal graphs. Extensive experiments show that CRFT significantly improves both reasoning accuracy and interpretability across multiple backbones. By combining diagnostic evaluation with causality-informed fine-tuning, this work establishes a foundation for advancing VLMs toward causally grounded physical reasoning.