STORMS: Rethinking Video Reasoning with Internalized Models

Video reasoning is a tricky business. It demands an intricate juggling act, involving tracking motion, temporal sequences, and shifting visual cues. Traditional methods lean heavily on large vision-language models (LVLMs), often outsourcing reasoning to external tools or complex textual sequences. But this externalized approach isn't without its drawbacks. It can lead to increased latency and engineering headaches. What if visual reasoning could happen internally, bypassing these constraints?

Introducing STORMS

Enter STORMS, short for Spatial-Temporal reasOning via inteRnalized Modeling. This framework is a breakthrough in the video reasoning landscape. It teaches LVLMs to process bounded continuous latent trajectories, offering an alternative to the conventional textual chain-of-thought (CoT) strategies. It hinges on the idea that visual reasoning can be implicitly managed, avoiding the need for externalization.

STORMS operates in two stages. During the first stage, it aligns latent tokens with thought-video representations from generated videos, grounding these latent states in dynamic visual evidence. This alignment is essential. It ensures that the model isn't just parroting data but genuinely understanding and processing visual information. The second stage eschews step-by-step annotations, using answer-only supervision to internalize reasoning.

Efficiency Without Compromise

The brilliance of STORMS lies in its ability to speed up inference. Unlike traditional methods that regenerate videos or reinstate frames at inference time, STORMS executes a bounded latent rollout. This means no repeated video generation or reliance on external visual tools, drastically reducing inference overhead.

Experiments on platforms like VideoMME, MVBench, TempCompass, and MMVU reveal that STORMS not only enhances video reasoning accuracy but also slashes latency. The AI-AI Venn diagram is getting thicker, as STORMS proves that internalized modeling isn't just efficient, it's superior.

Why It Matters

So, why should this matter to those in the field? For one, the implications extend beyond mere technical efficiency. By internalizing reasoning, STORMS paves the way for more autonomous AI systems that can operate with less human oversight and intervention, a essential step toward true agentic AI.

this isn't just a convergence of technology and technique. It's a rethinking of how machines process and reason about the world visually. If agents have wallets, who holds the keys? In the context of video reasoning, STORMS appears to be handing the keys firmly back to the machines themselves.

The future of video reasoning may very well lie in frameworks like STORMS. It's not just a question of efficiency. It's about autonomy and the evolution of AI's ability to interpret increasingly complex data without human crutches.