What MLLMs Learn about When they Learn about Multimodal Reasoning

Jiwan Chung; Neel Joshi; Pratyusha Sharma; Vibhav Vineet; Youngjae Yu

arxiv: 2510.01719 · v4 · submitted 2025-10-02 · 💻 cs.CL

What MLLMs Learn about When they Learn about Multimodal Reasoning

Jiwan Chung , Neel Joshi , Pratyusha Sharma , Youngjae Yu , Vibhav Vineet This is my paper

Pith reviewed 2026-05-18 10:47 UTC · model grok-4.3

classification 💻 cs.CL

keywords multimodal reasoningMLLMsgeometry benchmarkperception decompositiontraining strategieserror categorization

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The pith

Different training strategies for multimodal models create distinct profiles of perception, reasoning, and interaction skills that a single accuracy number hides.

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

The paper introduces MathLens, a benchmark of textbook geometry problems that breaks performance down into separate measures for how well a model sees the diagram, how well it reasons step by step, and how those two abilities interact. It finds that reinforcement learning mainly strengthens diagram reading and tolerance for visual changes, while text-only supervised fine-tuning improves the quality of reflective reasoning chains. As both perception and reasoning get better, the share of errors that cannot be traced to either component grows and gets labeled multimodal-specific. The central point is that what looks like steady progress on multimodal reasoning is actually a changing mix of these subskills rather than uniform gains across the board.

Core claim

By deriving each problem from a symbolic specification and supplying visual diagrams, text-only versions, multimodal questions, and targeted perceptual probes, MathLens decomposes model performance into perception, reasoning, and multimodal-specific components; reinforcement learning improves perceptual grounding and robustness to diagram variation while textual SFT improves reflective reasoning, and the fraction of multimodal-specific errors rises as the other components strengthen.

What carries the argument

MathLens benchmark that decomposes performance on geometry problems into perception, reasoning, and multimodal-specific components using symbolic specifications plus controlled visual, textual, and probe variants.

If this is right

Reinforcement learning primarily improves perceptual grounding and robustness to diagram variation.
Textual supervised fine-tuning yields gains through reflective reasoning.
As perception and reasoning improve, a growing fraction of remaining errors fall outside these components and are categorized as multimodal-specific.
Apparent progress in multimodal reasoning reflects shifting balances among subskills rather than uniform advancement.

Where Pith is reading between the lines

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

Evaluation practices for multimodal models would need to track these separate components routinely instead of reporting only aggregate accuracy.
The same decomposition approach could be applied to other multimodal tasks such as visual question answering to test whether training effects follow similar patterns.
Once perception and reasoning reach high levels, new training objectives may be required that directly target the remaining multimodal interaction errors.

Load-bearing premise

The perceptual probes, text-only variants, and error categorization rules cleanly separate perception, reasoning, and multimodal-specific errors without adding new confounds or missing interactions between them.

What would settle it

A training run that raises overall accuracy on MathLens but shows no shift in the relative sizes of the three error categories or no rise in multimodal-specific errors would undermine the claim that training strategies produce systematically different capability profiles.

read the original abstract

Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. We introduce MathLens, a benchmark of textbook-style geometry problems that exposes this assumption by operationally decomposing performance into perception, reasoning, and multimodal-specific components. Each problem is derived from a symbolic specification and accompanied by visual diagrams, text-only variants, multimodal questions, and targeted perceptual probes, enabling controlled measurement of each component. Using this decomposition, we show that common training strategies induce systematically different capability profiles that are invisible under aggregate accuracy. Reinforcement learning primarily improves perceptual grounding and robustness to diagram variation, while textual SFT yields gains through reflective reasoning. In contrast, as perception and reasoning improve, a growing fraction of remaining errors fall outside these components and are categorized as multimodal-specific. These results suggest that apparent progress in multimodal reasoning reflects shifting balances among subskills rather than uniform advancement, motivating evaluation beyond scalar accuracy.

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.

Desk Editor's Note private letter to a colleague

MathLens shows training strategies create different sub-skill profiles in multimodal reasoning, but needs more proof on clean separation of components.

read the letter

Hi colleague, The main thing to know about this paper is that it introduces MathLens, a benchmark for geometry problems that splits performance into perception, reasoning, and multimodal-specific parts, and finds that RL and textual SFT affect those parts differently in ways aggregate accuracy misses. They generate problems symbolically, include diagrams and text-only variants, plus perceptual probes. The results indicate RL strengthens perceptual grounding and robustness to diagram changes, while SFT boosts reflective reasoning. As those get better, more errors fall into the multimodal-specific category. This approach is a solid step because it moves beyond single-score evaluations and gives a way to see shifting balances in sub-skills. The controlled setup with variants is a practical contribution to how we test these models. On the downside, the decomposition's reliability depends on the probes and error rules not creating new confounds. If text variants still involve implicit visual simulation or if categorization lumps interactions into the multimodal bucket, the distinct profiles could be overstated. The abstract lacks specifics on categorization rules or probe validation, so those details will determine how much weight to give the findings. This is relevant for researchers focused on multimodal model evaluation and training dynamics. Readers looking for better metrics than overall accuracy would find value here. I would recommend sending it for peer review. The core idea is worth referee attention to refine the methods and confirm the results.

Referee Report

2 major / 2 minor

Summary. The paper introduces MathLens, a benchmark consisting of textbook-style geometry problems derived from symbolic specifications, each accompanied by visual diagrams, text-only variants, multimodal questions, and targeted perceptual probes. This setup operationally decomposes model performance into perception, reasoning, and multimodal-specific components. The central empirical result is that common training strategies produce systematically different capability profiles—RL primarily improves perceptual grounding and diagram robustness while textual SFT improves reflective reasoning—that remain invisible when only aggregate accuracy is measured; as perception and reasoning improve, a larger share of errors is attributed to the multimodal-specific category.

Significance. If the decomposition is shown to be valid, the work is significant for shifting evaluation practices in multimodal reasoning away from scalar accuracy toward component-wise analysis. It supplies concrete evidence that apparent progress under different training regimes reflects rebalancing among subskills rather than uniform advancement, which has direct implications for diagnosing model limitations and designing future training objectives.

major comments (2)

[Error Categorization (Section 4)] The validity of the error categorization rules is load-bearing for the claim that multimodal-specific errors increase as perception and reasoning improve. The manuscript provides no explicit decision criteria, examples of borderline cases, or inter-annotator agreement statistics for assigning errors to the three buckets, leaving open the possibility that perception-reasoning interactions are systematically routed into the multimodal category.
[Probe Design and Text-Only Variants (Section 3.2)] The claim that text-only variants and perceptual probes cleanly isolate reasoning and perception rests on an untested assumption that MLLMs do not engage in implicit visual simulation when given text-only inputs. No ablation or control experiment is reported that would rule out this confound, which directly affects the attribution of RL gains to “perceptual grounding.”

minor comments (2)

[Abstract] The abstract states that problems are “derived from a symbolic specification” but does not indicate whether the symbolic source is released or how diagram variations are generated; adding a brief clause would improve reproducibility.
[Results] Tables or figures comparing component-wise scores across training regimes would benefit from explicit annotation of the aggregate-accuracy baseline so readers can immediately see the “invisible” differences highlighted in the text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help improve the clarity and rigor of our work. We address each major comment below and outline the revisions we will make to the manuscript.

read point-by-point responses

Referee: [Error Categorization (Section 4)] The validity of the error categorization rules is load-bearing for the claim that multimodal-specific errors increase as perception and reasoning improve. The manuscript provides no explicit decision criteria, examples of borderline cases, or inter-annotator agreement statistics for assigning errors to the three buckets, leaving open the possibility that perception-reasoning interactions are systematically routed into the multimodal category.

Authors: We agree that providing explicit decision criteria and examples is important for the validity of our error analysis. In the revised manuscript, we will expand Section 4 to include detailed decision criteria for categorizing errors into perception, reasoning, and multimodal-specific buckets. We will also include several examples of borderline cases and how they were classified. Additionally, we will conduct and report inter-annotator agreement statistics on a sample of 100 errors annotated by two independent annotators. These additions will strengthen the transparency of our methodology and address concerns about potential misclassification of perception-reasoning interactions. revision: yes
Referee: [Probe Design and Text-Only Variants (Section 3.2)] The claim that text-only variants and perceptual probes cleanly isolate reasoning and perception rests on an untested assumption that MLLMs do not engage in implicit visual simulation when given text-only inputs. No ablation or control experiment is reported that would rule out this confound, which directly affects the attribution of RL gains to “perceptual grounding.”

Authors: This is a valid concern regarding the interpretation of our results. While we did not include an explicit ablation for implicit visual simulation in text-only inputs, our design uses perceptual probes that directly assess visual understanding in the presence of diagrams, and the differential performance patterns between RL and textual SFT suggest distinct mechanisms. In the revision, we will add a dedicated limitations subsection discussing this potential confound and its implications for attributing gains to perceptual grounding. We will also propose future experiments to control for visual simulation, such as using models without visual encoders on text-only variants. We maintain that the current evidence supports our conclusions but acknowledge the need for this clarification. revision: partial

Circularity Check

0 steps flagged

Empirical benchmark study with self-contained measurements

full rationale

The paper introduces a new benchmark (MathLens) derived from symbolic specifications, with accompanying diagrams, text-only variants, multimodal questions, and perceptual probes to operationally decompose performance into perception, reasoning, and multimodal-specific components. It then reports empirical observations on how RL and SFT training strategies produce different profiles across these components, invisible in aggregate accuracy. No equations, fitted parameters, or predictions are involved; the central claims rest on direct data collection and error categorization rather than reducing to prior inputs by construction. This is a standard empirical evaluation paper with no load-bearing self-citations or self-definitional steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that the benchmark variants and probes successfully isolate the intended sub-skills.

axioms (1)

domain assumption The decomposition into perception, reasoning, and multimodal-specific components is valid and measurable via the probes and variants.
This assumption underpins the entire analysis and the claim that training strategies produce distinct profiles.

pith-pipeline@v0.9.0 · 5697 in / 1161 out tokens · 29973 ms · 2026-05-18T10:47:04.162493+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce MATHLENS, a benchmark ... decomposes performance into perception, reasoning, and multimodal-specific components ... derived from a symbolic specification
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Reinforcement learning primarily improves perceptual grounding ... textual SFT yields gains through reflective reasoning

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