CoT doesn't help improve in Sora Generation

Prompting
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For the video generation model Sora, it’s good. But I’ve found that if I try to use CoT to guide it through the generation, the results tend to be even worse than with a normal Prompt, and even more obviously wrong. I would like to know the reason for this.

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That’s an interesting observation! The reason why Chain of Thought (CoT) prompting might actually worsen the outputs of Sora (or any diffusion/generative model) likely comes down to a few key factors:

1. Sora is Not a Purely Text-Based Model

  • Unlike language models like GPT-4, Sora is a video generation model trained to map text prompts directly to video sequences. This means it doesn’t necessarily benefit from explicit reasoning steps in the same way a text-based model would.
  • CoT works well in LLMs because they use intermediate reasoning steps to break down a complex problem. But in Sora, there is no explicit “reasoning” step in how it processes prompts—it’s a direct text-to-video mapping.

2. CoT Adds Ambiguity Rather Than Clarity

  • For models like GPT-4, CoT works because it structures the reasoning, making logical connections clearer.
  • However, Sora isn’t interpreting the logical flow of text in the same way; instead, it’s learning from massive datasets of video-text pairs. A CoT prompt might introduce unnecessary complexity or conflicts in interpretation, leading to worse results than a direct, concise description.

3. Sora Likely Uses a Latent Space Instead of Logical Deduction

  • Most diffusion-based models (or transformer-based generative models) work in a latent space representation where concepts are encoded based on learned distributions.
  • When using CoT, you might be forcing the model to process information in an unnatural way, making it struggle to interpret what the “correct” visual output should be.

4. Visual Models Prefer Concise and Direct Descriptions

  • Video generation models work best with precise, vivid descriptions that directly relate to elements in their training data.
  • Adding reasoning steps (like CoT) might confuse the model by introducing abstract, non-visual information that doesn’t correlate well with the video dataset it was trained on.

What Should You Do Instead?

  • Use clear, direct, and highly descriptive prompts with sensory details (e.g., lighting, camera angles, object placement).
  • Avoid abstract reasoning or logic-based explanations.
  • Test iterative refinement instead of CoT—small prompt tweaks tend to work better in guiding generative models than long-winded explanations.
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Thank you for your reply! I think maybe I just use CoT in the wrong way. If I want to generate from the overall to local, and avoid mistakes due to hallucinations, CoT is a good tool.

For example, I want to generate a video from the driver’s view, I always find the it is difficult to keep the display in the vehicle’s front windscreen and vehicle screen consistent. Currently,my solution is like multi-agents，use one agent to generate and other agent to find mistake than base on the generation result and the mistakes they find, the agent can generate more accurate results.

If I have a tech like CoT can modify the results base on its reasoning step, than I don’t need multi-agents anymore.

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