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See2Think benchmark finds rendering is the bottleneck in multimodal visual reasoning

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See2Think benchmark finds rendering is the bottleneck in multimodal visual reasoning

Researchers have introduced See2Think, a unified evaluation framework built to answer a specific question: when multimodal large language models produce sketches, annotations, tool calls, or intermediate images while reasoning through a problem, do they actually rely on those visual states, or do they reach the same answer regardless? The framework has two parts. See2ThinkBench is a set of 1,200 open-ended, visually dependent problems spanning 12 task categories, covering 2D structured tasks, 3D scenes, and real-world reasoning. Visual Action-of-Thought (VAoT) is the evaluation method: it records a model's textual thoughts, the visual actions it takes, the resulting rendered states, and the reasoning that follows, under four controlled inference settings. The authors motivate the work by pointing out two gaps in existing benchmarks: task collections that are either narrow or partly solvable from text alone, and evaluations that score only the final answer without checking how the intermediate visual states were generated, rendered, or actually used. Running representative proprietary and open-source multimodal models through the framework, the researchers found that visual reasoning performance is strongly dependent on both the specific model and the inference environment, with no single setting winning consistently across tasks. A closer process analysis showed that models are generally capable of picking the relevant visual operation for a task, but that faithful rendering, producing an intermediate visual state that accurately reflects what it is supposed to show, is the clearest bottleneck. The analysis also found that models absorbing more of the visual feedback they generate does not reliably translate into higher accuracy. To test whether models genuinely depend on the visual states rather than ignoring them, the researchers corrupted task-relevant feedback under controlled interventions. Accuracy dropped by more than 10 percentage points, which the authors read as evidence that models do exhibit real behavioral dependence on the visual states they produce, rather than treating them as decorative byproducts of the reasoning process.

Key facts

  • See2ThinkBench contains 1,200 open-ended, visually dependent problems across 12 task categories covering 2D structured, 3D scene, and real-world reasoning.
  • VAoT records textual thoughts, visual actions, rendered states, and subsequent reasoning under four controlled inference settings.
  • Process analysis found models usually pick relevant visual operations, but faithful rendering is the clearest bottleneck.
  • High feedback uptake does not necessarily translate into accuracy gains.
  • Corrupting task-relevant feedback in controlled interventions dropped accuracy by over 10 percentage points, showing models are behaviorally dependent on visual states.

Why it matters

Multimodal models increasingly reason by generating and consulting intermediate visual material, sketches, annotations, tool outputs, rendered scenes, rather than working from text alone. Whether that visual scaffolding is actually load-bearing, or just a plausible-looking trace attached to an answer the model would have reached anyway, is a basic question about how these systems work. See2Think is built specifically to test that, rather than to score final-answer accuracy the way most benchmarks do.

Who it affects

The framework targets both proprietary and open-source multimodal models, and by extension the researchers and developers building or evaluating them. Anyone designing agentic or tool-using multimodal systems that lean on generated visual intermediates, diagrams, annotated images, rendered simulations, has a direct stake in whether that machinery is diagnosed as reliable or as a weak link.

How to use it

See2ThinkBench and VAoT are offered as an evaluation methodology: 1,200 problems across 12 task categories, run through four controlled inference settings, with the full chain of thoughts, actions, rendered states, and follow-on reasoning recorded for analysis. The source text does not state a release date, licence, or access details for the benchmark.

How solid is it

The findings come from evaluating representative proprietary and open-source multimodal models on the new benchmark, with process-level analysis of how models select and use visual operations rather than only scoring final answers. The controlled-intervention design, corrupting feedback to see if accuracy changes, gives the central claim (behavioral dependence on visual states) a causal test rather than resting on correlation alone. The source text does not name the specific models evaluated, the authors, their institutions, or a publication venue.

Risks and caveats

The headline result is double-edged: models do depend on the visual states they generate, but the process analysis shows faithful rendering is the clearest bottleneck, meaning the visual states models produce are often not accurate representations of what they are meant to depict. High feedback uptake also does not reliably improve accuracy, which suggests that even when a model uses its visual output, that use does not guarantee a better answer. The source text gives no detail on which models were tested or how they compared to one another.

“models usually select relevant visual operations, while faithful rendering remains the clearest bottleneck and high feedback uptake does not necessarily translate into accuracy gains”

— See2Think process analysis