TLDR: A new empirical study investigates whether advanced video models like Veo-3 can perform zero-shot reasoning across 12 dimensions using the MME-COF benchmark. While these models excel at short-horizon spatial coherence and fine-grained grounding, they struggle with complex causal reasoning, strict geometric constraints, and long-horizon planning. The findings suggest that current video models are not yet reliable standalone reasoners, but show promise as complementary visual components for future reasoning systems.
Recent advancements in artificial intelligence have brought forth video generation models capable of producing incredibly realistic and temporally coherent videos. These models, such as Veo-3, Sora-2, Kling, and Seedance, have shown an impressive ability to synthesize visual content, leading researchers to ponder a deeper question: Can these models also serve as zero-shot reasoners, understanding and solving complex visual problems without explicit task-specific training?
A new empirical study delves into this very question, investigating the reasoning capabilities of leading video models, with a particular focus on Veo-3. The study introduces a novel benchmark called MME-COF (Multi-Modal Evaluation for Chain-of-Frame), designed to thoroughly assess how these models perform across 12 diverse reasoning dimensions. These dimensions span a wide range of cognitive tasks, including spatial awareness, geometric understanding, physical plausibility, temporal logic, and even embodied interactions.
The Chain-of-Frame Concept
At the heart of this investigation is the concept of “Chain-of-Frame” (CoF) reasoning. Analogous to the “Chain-of-Thought” (CoT) process in large language models, CoF suggests that as a video model generates a sequence of frames, it can iteratively refine and update the scene, effectively working through a problem step-by-step in time and space. This sequential generation process hints at an emergent capability for general-purpose visual reasoning.
MME-COF Benchmark: A Comprehensive Assessment
To standardize the evaluation, the researchers curated the MME-COF benchmark, comprising 59 carefully selected entries across the 12 reasoning categories. Each task is designed to probe specific aspects of reasoning, with prompts meticulously crafted to transform textual problem-solving into a clear, video-presentation format. The evaluation protocol uses Gemini-2.5-Pro as an automatic verifier, scoring generated videos on criteria like instruction alignment, temporal consistency, visual stability, content fidelity, and focus relevance.
Key Findings: Strengths and Limitations
The study reveals a mixed bag of results. On the one hand, current video models demonstrate promising reasoning patterns in certain areas. They show a good grasp of short-horizon spatial coherence, meaning they can maintain consistent object relations over brief periods. Fine-grained grounding, such as identifying specific attributes like color or texture, and locally consistent dynamics also appear to be strengths, especially when targets are visually distinct.
However, the models encounter significant limitations when faced with more complex reasoning scenarios. They struggle with long-horizon causal reasoning, failing to maintain logical consistency over extended sequences. Strict geometric constraints, abstract logic, and multi-step transformations in 2D and 3D geometry often lead to errors, with models prioritizing visual plausibility over precise correctness. Tasks requiring specialized knowledge, such as medical reasoning or understanding graphical user interfaces (GUI), also proved challenging, often resulting in distortions or a superficial imitation of interaction without true functional understanding.
Not Yet Standalone Reasoners
The overarching conclusion is that while current video models can generate high-fidelity videos, their strong generative performance does not automatically translate into robust reasoning capabilities. They are not yet reliable as standalone zero-shot reasoners. Their behavior appears to be more pattern-driven, learning surface-level correlations from training data, rather than principle-driven, internalizing general rules and causal relationships. This often leads to plausible but instructionally flawed outputs.
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A Path Forward: Complementary Visual Engines
Despite these limitations, the emergent behaviors observed in video models signal strong potential. The CoF concept offers a novel way to approach visual problems step-by-step. The study suggests that while these models may not be robust standalone reasoners today, their foundational capabilities indicate they could serve as encouraging complementary visual engines alongside dedicated reasoning models. This collaborative approach could pave the way for next-generation visual reasoning systems.
For more details, you can read the full research paper here.
