Video Question Answering: The Perception Problem in AI

In the field of AI, there's always a challenge that gets the industry buzzing. The VRR Challenge at CVPR 2026 has thrown down the gauntlet for AI systems, asking them to tackle multiple-choice video question answering. But the real story here isn't about reasoning. It's about perception and how AI still struggles to “see” what humans take for granted.

The Perception-Reasoning Divide

The challenge is built on the ImplicitQA and VRR-QA benchmarks, focusing on questions that require understanding video content in ways that aren't immediately obvious. Answers can't be found in a single frame. Instead, they need to be inferred from a mix of spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of video.

Our study went all in on evaluating open-source Video-LMMs like Qwen2.5-VL, Qwen3-VL, InternVL3, Gemma-3, and RL-tuned video reasoners Video-R1 and VideoChat-R1.5. We tested a battery of inference-time strategies, from chain-of-thought and question decomposition to multi-model ensembling and category routing. But here's the kicker: the challenge isn't reasoning-bound, it's perception-bound. Slapping a model on a GPU rental isn't a convergence thesis. Reasoning-side augmentations proved neutral or even harmful, while perceptual capability and lightweight test-time denoising turned out to be the only reliable levers.

The Real Challenge: See Like a Human

An error analysis classified the toughest nuts to crack. Low-level perception, relative depth, viewpoint, and counting, posed the greatest challenges. Nearly solved, though, are causal and social reasoning tasks. In fact, when we prompted the model with monocular depth cues to specifically target these challenging categories, we saw a $5.8$ point drop in test accuracy. This tells us the model doesn’t need a better procedure. It needs a better percept. If the AI can hold a wallet, who writes the risk model?

Why This Matters

Why should anyone outside the labs care? Because these benchmarks illustrate a fundamental gap in what AI currently delivers versus human-like understanding. Decentralized compute sounds great until you benchmark the latency, and the same goes for AI perception. As AI continues to weave into our everyday lives, these challenges become more than academic, they're practical hurdles that systems must overcome to be truly beneficial. The intersection is real. Ninety percent of the projects aren't.

So the question remains: how do we teach AI to see the world as we do, not just reason about it? Show me the inference costs. Then we'll talk.