Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with
  the MME-CoF Benchmark
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
690417142c556835fa67f021
Ziyu Guo
690417142c556835fa67f022
Xinyan Chen
claimed_verified
2025-11-03T20:55:22.903Z
647c7a4ed412b3b376572a00
/avatars/9cc310fd3f9e3f211475816ed9b0cdaa.svg
Xinyan Chen
xy06
user
xy06
690417142c556835fa67f023
Renrui Zhang
690417142c556835fa67f024
Ruichuan An
690417142c556835fa67f025
Yu Qi
claimed_verified
2025-10-31T14:27:15.920Z
661de604f8dcbd5a207c9012
https://cdn-avatars.huggingface.co/v1/production/uploads/661de604f8dcbd5a207c9012/8255_NnR0nhnWdT8-Af7F.jpeg
yu
yqi19
user
yqi19
690417142c556835fa67f026
Dongzhi Jiang
claimed_verified
2025-11-03T20:56:16.141Z
6349214f8146350b3a4c5cdf
/avatars/cfd24caac9a87efb528d0f4c375932bc.svg
Dongzhi Jiang
CaraJ
user
CaraJ
690417142c556835fa67f027
Xiangtai Li
claimed_verified
2025-12-15T08:14:43.715Z
63958b4414513eaf9029ebf1
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
Xiangtai Li
LXT
user
LXT
690417142c556835fa67f028
Manyuan Zhang
690417142c556835fa67f029
Hongsheng Li
690417142c556835fa67f02a
Pheng-Ann Heng
Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with
  the MME-CoF Benchmark
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
Video models like Veo-3 show promise in short-term visual reasoning but struggle with long-term causal reasoning and abstract logic, indicating they are not yet reliable standalone zero-shot reasoners.
video models Veo-3 spatial coherence fine-grained grounding locally consistent dynamics long-horizon causal reasoning geometric constraints abstract logic zero-shot reasoners Chain-of-Frame (CoF) reasoning MME-CoF benchmark