Short Review
Evaluating Video Models as Zero-Shot Visual Reasoners
This empirical study meticulously investigates the capacity of recent video generation models, specifically focusing on the prominent Veo-3, to function as zero-shot reasoners in complex visual reasoning scenarios. The research addresses a critical question: do these models encode sufficient world knowledge to perform advanced reasoning beyond realistic synthesis? To comprehensively assess this, the authors developed MME-CoF, a novel and compact benchmark, enabling an in-depth evaluation across 12 distinct dimensions, including spatial, geometric, physical, temporal, and embodied logic. Findings reveal that while current video models exhibit promising patterns in short-horizon spatial coherence and local dynamics, they face significant limitations in long-horizon causal reasoning and abstract logic, suggesting they are not yet reliable as standalone reasoners.
Critical Evaluation of Veo-3's Reasoning Capabilities
Strengths
The study's primary strength lies in its rigorous methodology and comprehensive scope. The introduction of the MME-CoF benchmark, featuring expert-curated test cases and a 12-category task taxonomy, provides a robust framework for evaluating generative video models. This systematic approach allows for a detailed characterization of both strengths and failure modes across diverse reasoning dimensions, from visual detail and trace reasoning to 3D geometry and physics. The research effectively identifies areas where Veo-3 shows competence, such as handling salient targets, simple transformations, and generating visually plausible short-term physics dynamics, highlighting its potential as a complementary visual engine rather than a standalone reasoner.
Weaknesses
Despite its strengths, Veo-3 demonstrates notable limitations in several critical areas. The model consistently struggles with long-horizon causal reasoning, strict geometric constraints, and abstract logic, often producing misaligned or inconsistent structures in complex multi-step tasks. Its understanding of physics is often superficial, generating visually plausible but quantitatively inaccurate or causally unfaithful dynamics. Furthermore, the study reveals a lack of precision and consistency in tasks like chart/table reasoning, object counting, and GUI interaction, where it exhibits inconsistencies, workarounds, or even hallucinations. These findings underscore a fragile understanding of underlying logic and robust constraint awareness, limiting its capabilities to basic recognition rather than true reasoning.
Implications
This research offers significant future research directions for advancing visual reasoning in generative models. By clearly delineating current capabilities and limitations, it provides a roadmap for developing more robust geometric reasoning, causal understanding, and long-term planning mechanisms. The findings are crucial for application development, informing where current video models can be reliably deployed and where they require augmentation with dedicated reasoning modules. The MME-CoF benchmark itself is a valuable contribution, establishing a new standard for evaluating generative video models and fostering progress towards more intelligent hybrid AI systems that combine the strengths of visual synthesis with advanced logical reasoning.
Conclusion
This empirical study provides an invaluable, in-depth analysis of the reasoning capabilities of leading video generation models. It effectively establishes the current state-of-the-art, demonstrating promising patterns in localized visual tasks while critically exposing significant limitations in complex, abstract, and long-horizon reasoning. The work is essential for researchers and developers, offering a clear assessment of where these models stand in their journey towards becoming reliable visual reasoners and guiding future efforts to bridge the gap between impressive synthesis and true intelligence in generative models.
