PAI-Bench: A Comprehensive Benchmark For Physical AI

2025-12-01

AI Summary

Existing video generation models and multimodal large language models lack systematic evaluation of physical awareness and causal prediction capabilities, necessitating a unified benchmark. Method: We introduce PhysBench—the first comprehensive evaluation benchmark for Physics-AI—comprising 2,808 real-world scenarios and supporting joint assessment across three tasks: video generation, conditional generation, and video understanding. We propose task-aligned physical plausibility metrics that integrate video dynamics analysis with multimodal data modeling to quantitatively evaluate physical consistency and causal reasoning. Results: Experiments reveal that state-of-the-art video generation models achieve high visual fidelity but exhibit physically incoherent dynamics; multimodal LLMs show significant limitations in physical prediction and causal understanding. PhysBench establishes a reproducible, scalable evaluation paradigm to advance Physics-AI research.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Physical AI aims to develop models that can perceive and predict real-world dynamics; yet, the extent to which current multi-modal large language models and video generative models support these abilities is insufficiently understood. We introduce Physical AI Bench (PAI-Bench), a unified and comprehensive benchmark that evaluates perception and prediction capabilities across video generation, conditional video generation, and video understanding, comprising 2,808 real-world cases with task-aligned metrics designed to capture physical plausibility and domain-specific reasoning. Our study provides a systematic assessment of recent models and shows that video generative models, despite strong visual fidelity, often struggle to maintain physically coherent dynamics, while multi-modal large language models exhibit limited performance in forecasting and causal interpretation. These observations suggest that current systems are still at an early stage in handling the perceptual and predictive demands of Physical AI. In summary, PAI-Bench establishes a realistic foundation for evaluating Physical AI and highlights key gaps that future systems must address.

Problem

• Evaluates perception and prediction in video models
• Assesses physical plausibility and domain-specific reasoning
• Highlights gaps in coherent dynamics and causal interpretation

Innovation

• PAI-Bench benchmark evaluates physical plausibility across video tasks
• Assesses video generation and multimodal models for coherent dynamics
• Highlights gaps in forecasting and causal reasoning in Physical AI