Elevating Multimodal Reasoning: The "Thinking with Video" Paradigm

Executive Summary

Current large language models (LLMs) and vision-language models (VLMs) face inherent difficulties when attempting complex reasoning tasks that require understanding dynamic processes or continuous changes. The "Thinking with Video" paradigm addresses this by repurposing advanced video generation models, specifically Sora-2, into unified multimodal reasoners. By integrating temporal and spatial information, this approach aims to solve problems that static images and text alone cannot capture effectively. The research introduces the Video Thinking Benchmark (VideoThinkBench) to validate this concept. The core takeaway is that video generation capabilities are intrinsically linked to powerful, unified multimodal understanding, positioning video models as strong contenders for next-generation reasoning engines in enterprise applications requiring real-time situational awareness.

The Motivation: What Problem Does This Solve?

Existing multimodal AI heavily relies on "Thinking with Text" (LLMs) and "Thinking with Images" (VLMs). However, these paradigms have critical limitations. First, an image captures only a single moment, rendering it insufficient for understanding actions, physics, cause-and-effect sequences, or continuous transformations vital for many real-world tasks, such as robotics or complex diagnostics. Second, the fundamental separation of text and vision into distinct processing modalities creates inefficiencies and knowledge silos, preventing truly unified comprehension. This research seeks a solution that integrates vision and language naturally within a shared temporal framework, improving robustness and depth of reasoning for complex, dynamic scenarios encountered in enterprise automation and decision support systems.

Key Contributions

How the Method Works

The "Thinking with Video" method fundamentally reframes complex reasoning as a temporal prediction or generation task. Instead of requiring the model to output only a textual answer, the intermediate step involves generating a video that visually simulates the problem and its solution path. For example, when solving a visual puzzle, the model generates the sequence of actions needed to complete the puzzle. The underlying architecture, leveraging a model like Sora-2, is inherently spatial and temporal. It integrates textual prompts and initial visual conditions (if applicable) to synthesize a coherent video sequence. This forces the model to internally represent and track dynamic object states, spatial relationships, and temporal physics. The final textual output is then extracted or inferred from this internally generated, dynamic visual simulation, bridging the gap between perception (vision), temporal processing (video), and cognition (language).

Results & Benchmarks

The research introduces VideoThinkBench to systematically evaluate Sora-2's reasoning capacity. The quantitative results demonstrate strong performance, often comparable to or exceeding established state-of-the-art VLMs.

On vision-centric tasks, Sora-2 achieved parity with SOTA VLMs, and notably surpassed them on specialized tasks like Eyeballing Games, which require precise visual estimation and temporal tracking.

More critically, Sora-2 showed surprising strength on complex, text-centric benchmarks, suggesting its video processing capability translates into robust cognitive skills:

These figures suggest that the internal temporal modeling used for video generation yields strong abstract reasoning abilities, even on tasks not explicitly visual. This performance validates the paradigm shift proposed by the authors.

Strengths: What This Research Achieves

One major strength is the model's unified approach. By treating dynamic visual changes and textual commands within the same temporal generation framework, the model avoids the architectural complexity and information bottlenecks associated with coupling separate vision encoders and language decoders. This inherent temporal understanding should lead to higher reliability when modeling dynamic physical systems or process flows. Additionally, the ability of Sora-2 to achieve 92% accuracy on the MATH subset suggests powerful, generalized reasoning that transcends mere visual description. The use of standard performance enhancers, like self-consistency, confirms that these video generation models are trainable using conventional LLM optimization techniques.

Limitations & Failure Cases

While promising, the research presents several inherent limitations. First, reliance on extremely large, resource-intensive foundation models like Sora-2 makes this paradigm prohibitively expensive for most immediate enterprise deployment scenarios. The computational cost of generating a video simulation for every reasoning step is enormous compared to purely textual or even static VLM inference. Second, the robustness relies heavily on the quality of the generated video. If the simulated physics or object interactions in the generated video are flawed (a common issue in current video synthesis), the resulting logical conclusion will also be incorrect. Furthermore, the paper needs more detailed analysis on the failure modes: where does the temporal simulation break down, and how does this affect complex, multi-step planning tasks versus simpler single-step reasoning puzzles. Scalability to long-duration, highly complex process flows remains a significant practical challenge.

Real-World Implications & Applications

If the computational burden can be managed, the "Thinking with Video" paradigm has transformative implications for Enterprise AI. It shifts AI capability from descriptive interpretation to predictive simulation. In manufacturing, for instance, a model could not only identify a fault but generate a video showing the precise sequence of events leading to the failure. This level of dynamic understanding is critical for next-generation automated diagnostics, synthetic data generation for testing complex control systems, and robotic task planning where understanding continuous interaction with the environment is paramount. It allows enterprise systems to move beyond static, rule-based decision-making toward high-fidelity, simulated decision pathways.

Relation to Prior Work

This research directly builds upon the successes of "Chain of Thought" (CoT) prompting in LLMs and intermediate visual representation generation in advanced VLMs like GPT-4V. Earlier work focused on using text or static visual scratchpads to aid complex reasoning. For example, some VLMs generate intermediate images before answering. However, these prior approaches still suffered from the static nature of image-based reasoning or the abstraction gap between text and real-world physics. "Thinking with Video" represents a logical extension, integrating the temporal dimension missing in predecessors. It effectively raises the bar for SOTA multimodal reasoning by demanding physical and temporal coherence from the reasoning process itself, unifying two formerly separate capabilities: generation and inference.

Conclusion: Why This Paper Matters

This paper is highly significant because it proposes a viable, unified architecture for multimodal reasoning, suggesting that the path to true intelligence may lie in the ability to simulate and predict dynamic reality. By demonstrating that advanced video generation models like Sora-2 possess deep reasoning capabilities, the authors make a compelling case for investing heavily in temporally aware foundation models. While the immediate deployment challenges around computational overhead are substantial, the research validates a powerful conceptual framework. We should view the video generation model not just as a content creation tool, but as a potential blueprint for a truly unified multimodal AI agent capable of complex, dynamic reasoning across all enterprise verticals.

Appendix

The core method requires the model to simulate the problem's solution in the temporal domain. This contrasts with traditional VLMs that primarily rely on static spatial understanding. The success on tasks like Eyeballing Puzzles indicates a superior capacity for geometric and physical prediction across time, a critical feature for any system needing to interact with the physical world.