See2Think: Do Multimodal Models Really Use Intermediate Visual States?
AI Summary
See2Think introduces a unified evaluation framework for testing whether multimodal models genuinely use intermediate visual states. See2ThinkBench contains 1,200 open-ended, visually dependent problems across 12 categories, covering 2D structures, 3D scenes, and real-world reasoning. Its Visual Action-of-Thought protocol records textual thoughts, visual operations, rendered states, and later reasoning under four controlled inference settings. Evaluations of representative proprietary and open-source models find strong model- and environment-dependence, with no universally best setting. Faithful rendering is the clearest bottleneck, while corrupted task-relevant feedback causes accuracy drops of more than 10 percentage points.
Why it's worth reading
As sketches, annotations, and visual tools become common in multimodal reasoning, this paper offers controlled interventions that test whether models actually depend on the visual states they generate.
Deep Read
What Happened
Original fact: The paper introduces See2Think, a framework composed of See2ThinkBench and Visual Action-of-Thought (VAoT). It evaluates whether multimodal models generate and use intermediate visual states rather than merely producing correct final answers.
Core Technology
Original fact: See2ThinkBench contains 1,200 open-ended, visually dependent problems across 12 task categories, covering 2D structured reasoning, 3D scenes, and real-world settings. VAoT records textual thoughts, visual actions, rendered states, and subsequent reasoning across four controlled inference settings.
Key Evidence & Numbers
Original fact: Evaluations of representative proprietary and open-source multimodal models show that no single setting consistently dominates across tasks. Models generally select relevant visual operations, but faithful rendering is the clearest bottleneck. High feedback uptake does not necessarily improve accuracy. Under task-relevant corrupted feedback, accuracy falls by more than 10 percentage points.
Why It Matters
Analysis: The study separates using a visual tool from depending on the visual state produced by that tool. Controlled corruption of intermediate feedback is a more diagnostic test than judging only final answers or counting tool calls, because it probes whether the state changes subsequent behavior.
Practical Impact
Analysis: Multimodal-agent evaluations should log actions, rendered outputs, and later feedback use, then add targeted state-corruption tests. In engineering workflows, improving rendering fidelity for diagrams, annotations, and scenes may matter more than simply increasing the number of visual operations.
Limitations & Uncertainty
Original fact: The abstract does not provide the complete model list, per-category results, precise definitions of the four settings, or statistical significance details. Analysis: Behavioral dependence may still reflect prompt structure, tool interfaces, or model priors. The scope of the reported accuracy drop requires verification in the full paper. Unverified inference: Dependence on benchmark visual states does not by itself establish a stable or interpretable internal visual workspace.
Original Sources
- arXiv:2607.26769
- Source label: hf-papers
- Published: 2026-07-31