Are Video Models Ready as Zero-Shot Reasoners? Findings from an Empirical Study with the MME-CoF Benchmark

This empirical study analyzed 18,384 generated videos across 62 qualitative and 7 quantitative tasks to assess zero-shot reasoning in video models. The findings indicate that while models like Veo 3 demonstrate nascent zero-shot capabilities across tasks they were neither trained nor adapted for, practical application requires careful consideration of task dependency, prompt design, and evaluation rigor. The insights are grounded in the MME-CoF benchmark, offering task-structured evaluation rather than broad, policy-style claims.

Empirical Evidence and Methodology

Study Design and Data

How far can a video model reason when asked to generalize without task-specific fine-tuning? This study tackles that question by analyzing a large library of generated videos under strict zero-shot conditions. The methodology is summarized as follows:

• Data scale: 18,384 generated videos were collected and analyzed.
• Task breadth: The analysis spanned 62 qualitative tasks and 7 quantitative tasks, covering perceptual, reasoning, and cross-modal challenges.
• Evaluation setting: Videos were evaluated under zero-shot conditions without model fine-tuning to simulate practical deployment.
• Benchmark framework: The MME-CoF benchmark organizes tasks by modality and reasoning complexity to enable structured comparisons.

Through this approach, researchers use the MME-CoF benchmark to group tasks by modality and reasoning complexity, enabling fair, structured comparisons across models and deployment contexts.

What Veo 3 Demonstrates

Veo 3 can tackle tasks it was never explicitly trained for, showing strong zero-shot capabilities that let it adapt to new challenges without extra tuning. However, its performance varies significantly by task type.

At a glance, the contrast is clear: strong capabilities where perception and scene context matter, with room to grow in more abstract reasoning.

Practical Workflows for Reproducibility

Reproducible research is a repeatable workflow. This section outlines a lean end-to-end process for evaluating-the-segment-anything-model-across-video-shots-methods-and-a-comprehensive-benchmark/”>evaluating Veo 3 on the MME-CoF benchmark with zero-shot prompts, emphasizing clear data, traceable results, and shared work patterns. The key requirements and outputs for each stage are as follows:

Detailed Workflow Steps

• Environment setup: Use Python 3.x and a recent PyTorch build. Isolate the setup with a reproducible environment (conda or virtualenv) and pin dependency versions.
• Data preparation: Obtain the MME-CoF benchmark task set and associated zero-shot prompts. Version data assets as part of the experiment.
• Inference: For each video sample, run zero-shot prompts across all tasks using Veo 3. Log raw model outputs with corresponding prompt metadata for auditability.
• Evaluation: Compute task-level success metrics and assess cross-task consistency against human baselines. Store results in structured JSON and CSV formats.
• Reproducibility: Maintain a shared experiment notebook, version prompts carefully, and publish a public repository with clear instructions, licenses, and contribution guidelines.

Zero-Shot Reasoning Coverage Across Tasks

The study provides direct, task-specific evidence for zero-shot video reasoning, analyzing 18,384 generated videos across 62 qualitative and 7 quantitative tasks using the MME-CoF benchmark. This contrasts with many sources that focus on infrastructure rather than empirical data.

Practical Implementation: A Ready-to-Run Workflow for Practitioners

The study offers a practical workflow for practitioners:

• Pros: Reduces reliance on task-specific fine-tuning; demonstrates strong zero-shot reasoning with Veo 3.
• Cons: Performance is task-dependent; requires careful evaluation and reliable prompts; results may vary with video quality and domain.

Runnable Steps:

1. Setup environment with Python 3.x and a recent PyTorch; install necessary SDKs for Veo 3.
2. Acquire the MME-CoF benchmark task set and associated evaluation prompts for zero-shot tasks.
3. For each video, run zero-shot inferences across 62 qualitative tasks and 7 quantitative tasks using standard prompts.
4. Collect metrics such as task-level accuracy, cross-task consistency, and human-aligned scoring; store in JSON/CSV.
5. Analyze failure modes by task category (perception gaps, reasoning gaps, temporal dependencies).
6. Iterate prompts and evaluation setup; publish code, prompts, and evaluation scripts to enable reproducibility.

Future Work, Limitations, and Research Gaps

Limitations of the Current Study

This study presents a focused look at Veo 3, but its findings are limited:

• Model coverage: Evaluation includes only Veo 3. Generalizability to other video models remains to be shown.
• Benchmark scope: All experiments are conducted on the MME-CoF benchmark. Results may differ on other datasets or in-the-wild videos.
• Task scope and protocols: For long-form or highly temporally dependent tasks, different evaluation protocols may be required beyond the 62 qualitative and 7 quantitative tasks used here.

Recommendations for Researchers and Practitioners

To promote trustworthy and reproducible AI research, the following practical steps are recommended:

• Promote reproducibility: Share code, prompts, and evaluation scripts publicly. Publish well-documented code repositories with clear instructions. Share prompts and prompt-generation pipelines. Provide evaluation scripts and data splits with versioning.
• Develop richer evaluation metrics: Capture temporal reasoning, multi-step inference, and cross-modal alignment. Incorporate time-aware assessments and step-level scoring. Assess cross-modal alignment with tasks that fuse various modalities.
• Report uncertainty and variability: Run multiple seeds, provide confidence intervals, and include error analyses. Offer a concrete metric suite or dashboard.
• Explore domain-specific prompting strategies: Tailor prompts to domain language and norms. Use few-shot demonstrations that reflect realistic distributions. Experiment with meta-prompts to guide reasoning style.
• Assess zero-shot baselines: Report when few-shot prompts provide meaningful gains. Be mindful of data leakage and clearly separate training vs. evaluation prompts.

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