PhysMRV: Physical Memory Retrieval and Verification for Physical Plausibility Reasoning

Video-language models (VLMs) have achieved remarkable performance on traditional visual question answering tasks, yet they remain unreliable in reasoning about physical plausibility, where understanding object interactions, causal dynamics, and fundamental physical principles is essential. This limitation is particularly evident on challenging physical reasoning benchmarks, revealing a persistent gap in physical commonsense reasoning. To address this challenge, we propose PhysMRV, a training-free physical memory and verification framework for physical plausibility reasoning. PhysMRV constructs a Hierarchical Memory Bank of structured physical knowledge comprising three complementary levels: scene descriptions capturing general visual information, event graphs modeling object interactions and state transitions, and physics-rule summaries distilling reusable physical principles. During inference, PhysMRV retrieves physically relevant memories and leverages their structured physical evidence to guide the VLM to verify physical plausibility without requiring parameter updates. We evaluate PhysMRV on three challenging physical plausibility reasoning benchmarks: ImplausiBench, IntPhys2, and GRASP Level 2, across multiple state-of-the-art VLMs. Experimental results demonstrate consistent improvements across diverse VLM backbones and strong baselines, validating the effectiveness of structured physical memory and evidence-guided verification for physical plausibility reasoning.