Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with
  the MME-CoF Benchmark
Authors: name: Ziyu Guo, name: Xinyan Chen, name: Renrui Zhang, name: Ruichuan An, name: Yu Qi, name: Dongzhi Jiang, name: Xiangtai Li, name: Manyuan Zhang, name: Hongsheng Li, name: Pheng-Ann Heng
Organization: The Chinese University of Hong Kong

Recent video generation models can produce high-fidelity, temporally coherent
videos, indicating that they may encode substantial world knowledge. Beyond
realistic synthesis, they also exhibit emerging behaviors indicative of visual
perception, modeling, and manipulation. Yet, an important question still
remains: Are video models ready to serve as zero-shot reasoners in challenging
visual reasoning scenarios? In this work, we conduct an empirical study to
comprehensively investigate this question, focusing on the leading and popular
Veo-3. We evaluate its reasoning behavior across 12 dimensions, including
spatial, geometric, physical, temporal, and embodied logic, systematically
characterizing both its strengths and failure modes. To standardize this study,
we curate the evaluation data into MME-CoF, a compact benchmark that enables
in-depth and thorough assessment of Chain-of-Frame (CoF) reasoning. Our
findings reveal that while current video models demonstrate promising reasoning
patterns on short-horizon spatial coherence, fine-grained grounding, and
locally consistent dynamics, they remain limited in long-horizon causal
reasoning, strict geometric constraints, and abstract logic. Overall, they are
not yet reliable as standalone zero-shot reasoners, but exhibit encouraging
signs as complementary visual engines alongside dedicated reasoning models.
Project page: https://video-cof.github.io