PhysMRV：用于物理合理性推理的物理内存检索与验证
PhysMRV: Physical Memory Retrieval and Verification for Physics Plausibility Reasoning
AI总结：
针对视频语言模型物理合理性推理不可靠的问题，提出免训练的PhysMRV框架，通过将训练视频转化为分层内存库，利用结构化物理证据指导VLM验证物理合理性，在多基准测试中取得一致改进，有效增强该推理能力。
AI中文摘要：
视频语言模型（VLM）在视频理解和视觉问答方面表现出色，但在物理合理性推理上不可靠，在具有挑战性的物理推理基准测试中存在明显差距。为此提出PhysMRV，一个用于物理合理性推理的免训练物理内存和验证框架。它将训练视频转换为结构化物理知识的分层内存库，包括场景描述、物理事件图和物理规则摘要三个互补级别。推理时，检索相关内存并利用其结构化物理证据指导冻结的VLM验证物理合理性。在三个具有挑战性的物理推理基准上评估，结果表明结构化物理内存能有效增强物理合理性推理且无需额外训练。
英文摘要：
Video-language models (VLMs) have achieved remarkable performance on video understanding and visual question answering, 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. Unlike retrieval-augmented VLMs that retrieve semantically similar videos as additional context, PhysMRV transforms training videos into a Hierarchical Memory Bank of structured physical knowledge comprising three complementary levels: scene descriptions capturing visual context, physical-event graphs modeling object interactions and causal structure, and physics-rule summaries distilling reusable physical principles and cues. During inference, PhysMRV retrieves physically relevant memories and leverages their structured physical evidence to guide a frozen VLM in verifying physical plausibility, requiring neither fine-tuning nor parameter updates. We evaluate PhysMRV on three challenging physical reasoning benchmarks, ImplausiBench, IntPhys2, and GRASP Level 2, across multiple state-of-the-art VLMs. Experimental results demonstrate consistent improvements over direct prompting across diverse VLMs and evaluation benchmarks, showing that structured physical memories provide an effective and scalable means of enhancing physical plausibility reasoning without additional training.
