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arxiv: 2605.09266 · v2 · submitted 2026-05-10 · 💻 cs.AI

Recognition: no theorem link

SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning

Bokai Zhou, Boqiang Guo, Changzheng Zhang, Dandan Tu, Heng Li, Hui-Ling Zhen, Jiacong Lu, Junjie Yu, Kunkun Liu, Kun Xiang, Likui Zhang, Shangrui Huang, Terry Jingchen Zhang, Xiaodan Liang, Yangle Fang, Yinya Huang, Yueling Tang, Zirong Liu

Pith reviewed 2026-05-13 07:40 UTC · model grok-4.3

classification 💻 cs.AI
keywords modality transfermultimodal reasoningphysics reasoningblind trainingvisual groundingRLVRbenchmarkrepresentation invariance
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0 comments X

The pith

Frontier models lose physics reasoning ability as problems shift from text to diagrams, mainly from failures to ground visual variables.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

SeePhys Pro creates four versions of each physics problem that move the same information gradually from language into images. Frontier models show consistent accuracy drops as more content becomes visual, with the sharpest losses when diagrams must be linked to specific variables. The authors build large training sets for multimodal reinforcement learning with vision and reward, then test a blind-training control where every training image is masked. Even with masks in place, performance rises on normal unmasked tests, but targeted controls using text deletion, varying mask rates, and format changes show the gains trace to leftover language patterns and data distributions instead of actual visual reading. The work therefore argues that accuracy numbers alone are not enough; multimodal systems need separate checks for whether they stay robust when information crosses modalities and whether their improvements actually use the visual evidence provided.

Core claim

The paper establishes that current frontier multimodal models are not representation-invariant reasoners: their performance on physics reasoning degrades on average as critical information is transferred from language to diagrams, with visual variable grounding as the dominant bottleneck. It further shows that large-scale multimodal RLVR training can produce gains on unmasked validation sets even when all training images are masked, and that these gains survive only because of residual textual and distributional cues rather than reliance on task-critical visual content.

What carries the argument

SeePhys Pro's set of four semantically aligned problem variants per question, constructed to increase visual content in controlled steps while holding semantics fixed.

If this is right

  • Multimodal evaluation must measure robustness under controlled modality transfer rather than accuracy alone.
  • RLVR gains observed on standard tests can occur without the model using the visual portions of the input.
  • Text-deletion, image-mask-rate, and format-saturation controls are necessary to confirm that training improvements depend on task-critical visual evidence.
  • Representation invariance should become a standard requirement when claiming progress in multimodal physics reasoning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training pipelines may need explicit penalties for shortcuts that ignore visual content to force genuine cross-modal use.
  • The same progressive-modality design could be extended to geometry or chemistry problems to test whether the grounding bottleneck is domain-specific.
  • Architectures that add explicit alignment losses between text descriptions and diagram elements could be tested as a direct response to the observed fragility.

Load-bearing premise

The four variants of each problem are equivalent in difficulty and free of unintended cues that models might exploit differently depending on the version.

What would settle it

A result in which masked-image blind training produces no accuracy lift on unmasked validation once text-deletion controls have removed all residual language signals would falsify the claim that gains come from non-visual cues.

Figures

Figures reproduced from arXiv: 2605.09266 by Bokai Zhou, Boqiang Guo, Changzheng Zhang, Dandan Tu, Heng Li, Hui-Ling Zhen, Jiacong Lu, Junjie Yu, Kunkun Liu, Kun Xiang, Likui Zhang, Shangrui Huang, Terry Jingchen Zhang, Xiaodan Liang, Yangle Fang, Yinya Huang, Yueling Tang, Zirong Liu.

Figure 1
Figure 1. Figure 1: Overview of the four modality-transfer levels in SEEPHYS PRO. Each seed problem is transformed into four semantically aligned variants that progressively move problem-critical information from language to vision: Level 1 is text-only, Level 2 moves structure into the image, Level 3 further moves variables and labels into the image, and Level 4 renders the full problem into a single visual input. We therefo… view at source ↗
Figure 3
Figure 3. Figure 3: RL does not appear to close modality gaps. Qwen3-VL-4B is trained on source-matched but test-disjoint vision-necessary physics data and evaluated on unmasked SEEPHYS PRO Level 1–4. The top row tracks validation accuracy on each modality-transfer level after normal and blind RL updates; the bottom row tracks the total transfer gap ∆T = A1 − A4 and variable-grounding gap ∆V = A2 − A3. Both normal and blind R… view at source ↗
Figure 4
Figure 4. Figure 4: Blind gains are not unique to SEEPHYS PRO. Peak validation gains for normal and blind RL on external physics and math benchmarks. Blind RL often recovers a substantial fraction of normal RL gains, and on several math settings it matches or exceeds normal RL. The gap dynamics tell a different story. The total transfer gap ∆T widens from 3.5 percentage points before training to 7.5 points after normal RL and… view at source ↗
Figure 5
Figure 5. Figure 5: Mechanism controls for blind-training gains. Text deletion, targeted deletion, mask￾rate ablation, and post-format-saturation controls show that blind gains depend on residual tex￾tual/distributional cues rather than valid visual evidence. All gains are computed on unmasked validation sets. relies on residual language, problem style, answer priors, and generic reasoning practice rather than valid visual ev… view at source ↗
Figure 6
Figure 6. Figure 6: Data pipeline for constructing SEEPHYS PRO. We describe the construction process as four logical stages: source collection, curation, transformation, and controlled sketching into raw, structural, and variable-level variants [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: SEEPHYS PRO and MathVerse Data Embeddings Panels (a, b) are the embeddings of text and multimodal inputs at Level 2 and Level 3 of SEEPHYS PRO. Panels (c, d) are the embeddings of text and multimodal inputs for the Vision Intensive and Vision Dominant subsets of MathVerse [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: PhysRL-40K training-pool validation with Qwen3-VL-4B. We train with GSPO on PhysRL-40K using two sequence-length settings and evaluate on held-out, unmasked validation sets. Curves show that validation accuracy improves on SeePhys Pro Level 3/4 and PhysReason, while training reward accuracy also increases. Duplicate values from interrupted/resumed runs are averaged by step before smoothing for display. 15 … view at source ↗
Figure 9
Figure 9. Figure 9: Post-format-saturation gains. After the format reward crosses 90%, answer accuracy can still increase substantially, especially in the Qwen2.5-VL-7B math runs highlighted in the main paper. This helps rule out a purely format-compliance explanation for blind gains. 4% 6% 8% 10% 12% reward SeePhys Pro: accuracy 20% 40% 60% 80% 100% SeePhys Pro: format 0 50 100 150 200 250 300 step 32% 33% 34% 35% 36% 37% 38… view at source ↗
Figure 10
Figure 10. Figure 10: Full image-mask-rate ablation curves. We plot accuracy and format reward for Qwen3-VL-4B trained with different image mask rates and evaluated on unmasked SeePhys Pro and PhysReason. Main-paper [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Separate cross-benchmark peak-gain plots. These are expanded views of [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Error-type clustering across modality-transfer levels and models. Donut charts show the distribution of manually annotated error types for GPT-5.4, Gemini-3.1- Pro, and Claude-Opus-4.7 across Level 1–4. The center value n denotes the number of analyzed errors for each model-level cell. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Oversimplification of physical modelling. Models oversimplified or misread the motion structure. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Visual geometry grounding errors. Models misground geometric cues such as angles, radii, and arc marks. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Constraint and equilibrium failures. Models show constraint over￾simplification, false equilibrium assumptions, and incorrect motion assumptions. As shown in the figure, the correct statement is () (A). The maximum rate of change of the magnetic flux through the iron core is 0.2 Wb/s (B). The frequency of the alternating current is 50 Hz (C). The ammeter A reads 0.4 2 A (D). The input power of the transfo… view at source ↗
Figure 16
Figure 16. Figure 16: Transformer numerical misreading. Models misread numerical values in the visual input, which propagates into incorrect frequency and option judgments. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Reaction-force reasoning. The examples contrast correct force-balance reasoning with wrong reaction-direction and moment-equation assumptions. Result: 300 N Result: 600 N Question: As shown in the figure, the areas are all S=2.0×10⁻² m², the distance between plates A and B is d₁=4.0×10⁻³ m, the distance between plates A and C is d₂=2 mm, and both plates B and C are grounded. If plate A is positively charg… view at source ↗
Figure 18
Figure 18. Figure 18: Induced-charge calculation. The examples show how correct physical grounding can be disrupted by numerical misreading. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_18.png] view at source ↗
read the original abstract

We introduce SeePhys Pro, a fine-grained modality transfer benchmark that studies whether models preserve the same reasoning capability when critical information is progressively transferred from text to image. Unlike standard vision-essential benchmarks that evaluate a single input form, SeePhys Pro features four semantically aligned variants for each problem with progressively increasing visual elements. Our evaluation shows that current frontier models are far from representation-invariant reasoners: performance degrades on average as information moves from language to diagrams, with visual variable grounding as the most critical bottleneck. Motivated by this inference-time fragility, we further develop large training corpora for multimodal RLVR and use blind training as a diagnostic control, finding that RL with all training images masked can still improve performance on unmasked validation sets. To analyze this effect, text-deletion, image-mask-rate, and format-saturation controls suggest that such gains can arise from residual textual and distributional cues rather than valid visual evidence. Our results highlight the need to evaluate multimodal reasoning not only by final-answer accuracy, but also by robustness under modality transfer and by diagnostics that test whether improvements rely on task-critical visual evidence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces SeePhys Pro, a fine-grained benchmark for physics reasoning consisting of problems with four semantically aligned variants that progressively transfer critical information from text to diagrams. Frontier models exhibit average performance degradation under this modality shift, with visual variable grounding identified as the primary bottleneck. The authors further construct multimodal RLVR training corpora and apply blind-training (image-masked) controls, showing that validation gains can arise from residual textual and distributional cues rather than task-critical visual evidence, as diagnosed via text-deletion, image-mask-rate, and format-saturation experiments.

Significance. If the results hold after validation of variant equivalence, the work would be significant for multimodal reasoning research. It supplies a diagnostic benchmark that directly tests representation invariance and exposes limitations in current RLVR pipelines that may exploit spurious cues. The combination of modality-transfer evaluation with blind-training controls offers a concrete methodology for distinguishing genuine visual reasoning from artifact-driven gains, which could influence evaluation standards in vision-language models for scientific domains.

major comments (2)
  1. [§3] §3 (Benchmark Construction): The central degradation claim requires that the four variants per problem are equivalent in intrinsic difficulty and free of differential cues. The manuscript describes them as 'semantically aligned' but reports no quantitative equivalence checks (human difficulty ratings, expert review, or cross-variant correlation), which is load-bearing for attributing drops to modality transfer rather than unintended hardness differences.
  2. [§4.2] §4.2 (Blind-Training Diagnostics): The conclusion that RLVR gains under masked images arise from residual cues rather than valid visual evidence depends on the text-deletion, image-mask-rate, and format-saturation controls. Without reported dataset sizes, statistical tests, or exclusion criteria for these controls, it is unclear whether the isolation of non-visual factors is robust enough to support the claim.
minor comments (2)
  1. [Abstract] Abstract: Specific numerical effect sizes, model identifiers, and total problem counts are omitted, which would allow immediate assessment of practical significance.
  2. [Figure 1] Figure 1 or equivalent: The progressive visual-element transfer across variants would benefit from an explicit side-by-side example with annotations for each modality level to improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of rigor in benchmark validation and experimental controls, which we address point by point below. We will incorporate the suggested additions in the revised version.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark Construction): The central degradation claim requires that the four variants per problem are equivalent in intrinsic difficulty and free of differential cues. The manuscript describes them as 'semantically aligned' but reports no quantitative equivalence checks (human difficulty ratings, expert review, or cross-variant correlation), which is load-bearing for attributing drops to modality transfer rather than unintended hardness differences.

    Authors: We agree that quantitative equivalence validation is essential to confidently attribute performance degradation to modality transfer. While the variants were designed by physics experts to maintain semantic alignment and equivalent reasoning demands, the original manuscript did not report formal checks. In the revision, we will add a human study with expert difficulty ratings (on a 5-point scale) for a random subset of 150 problems, report inter-rater reliability (Cohen's kappa), and include cross-variant Pearson correlations of model accuracies to demonstrate equivalence. These additions will directly support the central claim. revision: yes

  2. Referee: [§4.2] §4.2 (Blind-Training Diagnostics): The conclusion that RLVR gains under masked images arise from residual cues rather than valid visual evidence depends on the text-deletion, image-mask-rate, and format-saturation controls. Without reported dataset sizes, statistical tests, or exclusion criteria for these controls, it is unclear whether the isolation of non-visual factors is robust enough to support the claim.

    Authors: We acknowledge that greater transparency on the control experiments is needed to substantiate the isolation of non-visual factors. The original submission summarized the controls without full numerical and statistical details. In the revised manuscript, we will explicitly report the dataset sizes for each control (text-deletion, image-mask-rate, format-saturation), include statistical significance tests (paired t-tests with p-values and effect sizes), and detail the exclusion criteria applied during corpus construction. These updates will strengthen the evidence that gains stem from residual cues. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark and controls are self-contained

full rationale

The paper's central claims rest on direct empirical comparisons across four semantically aligned problem variants and on blind-training diagnostic experiments with text-deletion and mask-rate controls. No equations, fitted parameters, or predictions are defined in terms of the target quantities; the modality-transfer degradation and residual-cue findings are measured outcomes rather than reductions to inputs by construction. Any self-citations are incidental and not load-bearing for the reported results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; benchmark alignment and control interpretations are not detailed enough to identify any.

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