How Far Are Video Models from True Multimodal Reasoning?

2026-04-21

AI Summary

Current video models exhibit limited capabilities in complex multimodal reasoning tasks, such as logical inference and interactive generation, yet existing evaluation benchmarks fail to adequately assess these abilities due to their simplistic task designs and fragmented metrics. To address this gap, this work proposes CLVG-Bench, an evaluation framework that probes zero-shot reasoning through in-context learning–driven video generation tasks. It further introduces an Adaptive Video Evaluator (AVE), which leverages over 1,000 human-annotated video metadata samples and a perception-aligned mechanism to enable interpretable and scalable automatic assessment. Experimental results reveal that state-of-the-art models, including Seedance 2.0, achieve less than 25% success on logical reasoning tasks and nearly fail on interactive generation, highlighting critical bottlenecks in physical modeling and multimodal reasoning.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Despite remarkable progress toward general-purpose video models, a critical question remains unanswered: how far are these models from achieving true multimodal reasoning? Existing benchmarks fail to address this question rigorously, as they remain constrained by straightforward task designs and fragmented evaluation metrics that neglect complex multimodal reasoning. To bridge this gap, we introduce CLVG-Bench, an evaluation framework designed to probe video models' zero-shot reasoning capabilities via Context Learning in Video Generation. CLVG-Bench comprises more than 1,000 high-quality, manually annotated metadata across 6 categories and 47 subcategories, covering complex scenarios including physical simulation, logical reasoning, and interactive contexts. To enable rigorous and scalable assessment, we further propose an Adaptive Video Evaluator (AVE) that aligns with human expert perception using minimal annotations, delivering interpretable textual feedback across diverse video context tasks. Extensive experiments reveal a striking answer to our central question: while state-of-the-art (SOTA) video models, such as Seedance 2.0, demonstrate competence on certain understanding and reasoning subtasks, they fall substantially short with logically grounded and interactive generation tasks (achieving success rates <25% and ~0%, respectively), exposing multimodal reasoning and physical grounding as critical bottlenecks. By systematically quantifying these limitations, the proposed method provides actionable feedbacks and a clear roadmap toward truly robust, general-purpose video models. CLVG-Bench and code are released here.

Problem

• multimodal reasoning
• video models
• evaluation benchmark
• zero-shot reasoning
• physical grounding

Innovation

• multimodal reasoning
• video generation
• zero-shot evaluation
• CLVG-Bench
• Adaptive Video Evaluator