RULER-Bench: Probing Rule-based Reasoning Abilities of Next-level Video Generation Models for Vision Foundation Intelligence

2025-12-02

AI Summary

Existing video generation models are primarily evaluated on perceptual quality and temporal coherence, overlooking their capacity for cognitive-level rule-based reasoning. To address this gap, we introduce RULER-Bench—the first systematic benchmark for evaluating rule reasoning in video generation, covering both text-to-video and image-to-video paradigms. It comprises 40 tasks across six categories and 622 annotated instances. We propose a fine-grained, four-dimensional evaluation framework—assessing logical, spatiotemporal, causal, and symbolic consistency—leveraging GPT-4o for automated scoring (85% human alignment) augmented by expert validation. Experimental results reveal that state-of-the-art models achieve only 48.87% rule consistency, exposing a critical reasoning bottleneck. RULER-Bench establishes a foundational, interpretable, and reasoning-aware evaluation standard for next-generation visual foundation models, accompanied by open-source data and evaluation tools.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recent advances in video generation have enabled the synthesis of videos with strong temporal consistency and impressive visual quality, marking a crucial step toward vision foundation models. To evaluate these video generation models, existing benchmarks primarily focus on factors related to visual perception and understanding, like visual aesthetics, instruction adherence, and temporal coherence. However, the rule-based reasoning capabilities of video generation models remain largely unexplored. Although recent studies have carried out preliminary explorations into whether video models can serve as zero-shot learners, they still lack a fine-grained decomposition of reasoning capabilities and a comprehensive evaluation protocol. To address this gap, we introduce RULER-Bench, a benchmark designed to evaluate the reasoning ability of video generation models from the perspective of cognitive rules. Built upon two fundamental paradigms: text-to-video and image-to-video, RULER-Bench covers 40 representative tasks spanning six rule categories with 622 high-quality annotated instances. For the evaluation of each generated video, we construct a checklist covering four metrics and leverage GPT-o3 to assign scores to each question, achieving 85% alignment with human judgements. Extensive experiments show that the state-of-the-art model achieves only 48.87% on the rule coherence metric, highlighting significant room for improvement in the reasoning capability of next-level video models. We expect that the insight obtained from RULER-Bench will facilitate further development of reasoning-aware video generation, advancing video generation models toward vision foundation intelligence.

Problem

• Evaluates rule-based reasoning in video generation models
• Introduces benchmark for cognitive rule assessment
• Highlights gaps in reasoning capabilities of current models

Innovation

• Introduces RULER-Bench benchmark for rule-based reasoning evaluation
• Uses GPT-4o for automated scoring aligned with human judgments
• Covers 40 tasks across six rule categories in video generation