See2ThinkBench: rendering, not reasoning, breaks visual chains

A new benchmark called See2Think, posted to arXiv by Siyu Yan and twelve co-authors, keeps landing on the same finding no matter how the authors slice their results: when a multimodal model gets a visual reasoning task wrong, the failure is usually not the reasoning itself. It is the rendering.

The setup is worth understanding because it is what makes the claim testable. See2ThinkBench is 1,200 open-ended, visually dependent problems across 12 task categories, spanning 2D structured, 3D scene, and real-world reasoning. Instead of only grading final answers, the paper looks at the intermediate visual states models produce and consume, and then intervenes on them directly. When those intermediate renders are corrupted, accuracy drops by over 10 percentage points. That is the authors' evidence that the models are behaviorally dependent on the visual state, not just gesturing at it.

The other half of the result is the negative finding. The models usually select the relevant visual operations, so the tool-selection layer is not the villain either. Faithful rendering, the authors write, remains the clearest bottleneck, and high feedback uptake does not necessarily translate into accuracy gains. Giving a model more visual feedback does not automatically help if what it is looking at is a bad drawing.

Why this matters for anyone shipping a multimodal agent: if you are diffing model versions on end-to-end accuracy alone, you have no way to see whether progress is coming from better reasoning or better rendering, and no way to catch a rendering regression silently capping the ceiling. The paper suggests the eval discipline that will actually move the number is one that grades intermediate visual states, and that infra investment in the rendering pipeline may pay back faster than another round of reasoning fine-tunes.

The honest caveat is that this is one benchmark, and the authors themselves note that visual reasoning is strongly model- and environment-dependent, with no single setting consistently dominating across tasks. What the abstract does not give you is which rendering approaches held up best, whether the >10 point drop is a threshold or a gradient, or how the picture changes across proprietary and open models. Those are the numbers to watch as follow-ups land. But the direction of travel, and the specific place to point the investment, are the parts worth taking away.