AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process

Jingrong Wu; Kunpeng Gao; Qing Li; Shilin Yan; Xiaowen Zhang; Xintong Zhang; Xuanyan Chen; Yunde Jia; Yuwei Wu; Zhenxin Diao

arxiv: 2602.02676 · v3 · submitted 2026-02-02 · 💻 cs.CV

AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process

Xintong Zhang , Xiaowen Zhang , Jingrong Wu , Zhi Gao , Shilin Yan , Zhenxin Diao , Kunpeng Gao , Xuanyan Chen

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Yuwei Wu Yunde Jia Qing Li

This is my paper

Pith reviewed 2026-05-16 07:47 UTC · model grok-4.3

classification 💻 cs.CV

keywords adaptive multimodal reasoningvision-language modelsmode selectionreasoning process evaluationbenchmarkMatthews Correlation Coefficienttool-augmented reasoningdynamic difficulty

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The pith

AdaptMMBench shows adaptive mode selection in vision-language models scales with capacity but decouples from final accuracy, while key step coverage tracks performance.

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

The paper introduces AdaptMMBench to test how vision-language models decide between tool-augmented visual reasoning and plain text reasoning on the fly. Prior benchmarks used fixed difficulty labels that blend selection skill with raw model power, making it hard to measure the decision process itself. AdaptMMBench instead sets difficulty thresholds separately for each model and applies the Matthews Correlation Coefficient to score how rationally the model switches modes. Across real-world, OCR, GUI, knowledge, and math tasks, the evaluations find that larger models choose modes more sensibly, yet this choice often fails to raise accuracy. Coverage of essential reasoning steps, however, stays tightly linked to correct answers, while the payoff from tools varies sharply by architecture.

Core claim

AdaptMMBench dynamically identifies task difficulties based on each model's own capability boundaries and employs the Matthews Correlation Coefficient to quantify the rationality of reasoning mode selection, isolating this meta-cognitive skill from overall performance. The benchmark spans five domains and measures three additional process dimensions: key step coverage, tool effectiveness, and computational efficiency. Results indicate that adaptive mode selection improves with model capacity yet shows little correlation with final accuracy, whereas key step coverage aligns closely with success and tool effectiveness remains highly inconsistent across architectures.

What carries the argument

Dynamic per-model difficulty identification paired with Matthews Correlation Coefficient scoring of mode selection rationality.

If this is right

Higher-capacity models exhibit more rational adaptive mode selection across domains.
Key step coverage in the reasoning trace reliably predicts final accuracy.
Tool effectiveness varies widely and does not scale consistently with model size.
The benchmark distinguishes direct perception tasks from complex reasoning tasks in five distinct domains.
Computational efficiency can be tracked alongside selection rationality and step coverage.

Where Pith is reading between the lines

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

Future training may need to target explicit step identification rather than mode switching alone.
The observed decoupling suggests that mode selection is only useful once underlying reasoning capacity is already strong.
Applying the same dynamic labeling to newly released models could expose architecture-specific tool-use patterns.
Extending the process metrics to measure how often models skip critical steps on difficult tasks would test the coverage finding further.

Load-bearing premise

Dynamically identifying task difficulties based on each model's capability boundaries accurately isolates adaptive mode selection ability from general performance without introducing selection bias.

What would settle it

If a model achieves high MCC scores for mode selection yet shows no accuracy gain on tasks it correctly flags as tool-appropriate, the claimed isolation of selection ability would be contradicted.

read the original abstract

Adaptive multimodal reasoning has emerged as a promising frontier in Vision-Language Models (VLMs), aiming to dynamically modulate between tool-augmented visual reasoning and text reasoning to enhance both effectiveness and efficiency. However, existing evaluations rely on static difficulty labels and simplistic metrics, which fail to capture the dynamic nature of difficulty relative to varying model capacities. Consequently, they obscure the distinction between adaptive mode selection and general performance while neglecting fine-grained process analyses. In this paper, we propose AdaptMMBench, a comprehensive benchmark for adaptive multimodal reasoning across five domains: real-world, OCR, GUI, knowledge, and math, encompassing both direct perception and complex reasoning tasks. AdaptMMBench utilizes a Matthews Correlation Coefficient (MCC) metric to evaluate the selection rationality of different reasoning modes, isolating this meta-cognition ability by dynamically identifying task difficulties based on models' capability boundaries. Moreover, AdaptMMBench facilitates multi-dimensional process evaluation across key step coverage, tool effectiveness, and computational efficiency. Our evaluation reveals that while adaptive mode selection scales with model capacity, it notably decouples from final accuracy. Conversely, key step coverage aligns with performance, though tool effectiveness remains highly inconsistent across model architectures.

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

AdaptMMBench introduces a dynamic MCC-based benchmark for mode selection in VLMs but the decoupling claim looks vulnerable to circular labeling of difficulties.

read the letter

Colleague, the main point is that this paper offers AdaptMMBench, a benchmark spanning real-world, OCR, GUI, knowledge, and math tasks, to measure how VLMs choose between tool-augmented and direct reasoning. It uses MCC on dynamically set difficulties and adds process metrics for step coverage, tool use, and efficiency. The reported pattern is that selection ability grows with model size yet separates from final accuracy, while step coverage tracks performance and tool results vary wildly by architecture. That combination of dynamic labeling and multi-dimensional scoring is new relative to the static setups cited in the abstract. The motivation to separate meta-cognition from raw accuracy is clear and the domains chosen are practical. The paper does a straightforward job laying out why existing evaluations fall short and why process-level tracking matters. The soft spot sits in the dynamic difficulty step. The abstract ties labels to each model's capability boundaries, but does not show whether those boundaries are computed independently of the same accuracy data used for the final scores. If the boundaries come from running the models on the benchmark tasks, larger models will simply receive fewer hard labels, which can mechanically inflate their selection rationality numbers and manufacture the observed decoupling. Without the exact procedure, data tables, or model list, the central isolation claim cannot be checked. The abstract also states findings without numbers or error bars, so the strength of the results stays unclear. This work is for researchers who build or evaluate adaptive VLMs and want a framework that looks at selection and process rather than accuracy alone. A reader focused on efficiency or meta-reasoning would get usable ideas from the setup even if they treat the decoupling result as provisional. It deserves peer review so the labeling method and raw results can be examined directly.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes AdaptMMBench, a benchmark for adaptive multimodal reasoning in vision-language models that dynamically selects between tool-augmented visual reasoning and text reasoning across five domains (real-world, OCR, GUI, knowledge, math). It introduces an MCC-based metric for selection rationality that identifies task difficulties dynamically from each model's capability boundaries, aiming to isolate this meta-cognitive ability from general performance. The work also enables multi-dimensional process evaluation on key step coverage, tool effectiveness, and computational efficiency. Evaluations show adaptive mode selection scales with model capacity yet decouples from final accuracy, while key step coverage aligns with performance and tool effectiveness varies across architectures.

Significance. If the decoupling result and non-circularity of the dynamic labeling hold, the benchmark would offer a useful advance in evaluating meta-cognition in VLMs beyond static accuracy metrics, with the MCC approach and process-level dimensions providing concrete tools for future work on adaptive systems.

major comments (1)

[Abstract and §3] Abstract and §3 (Benchmark Construction): the claim that MCC on dynamically identified difficulties isolates selection rationality from accuracy is load-bearing for the decoupling result, yet the method for determining per-model capability boundaries is not shown to be independent of the same accuracy patterns used to compute final performance; if boundaries are fitted from model error rates on the benchmark tasks, the MCC scores become performance-dependent by construction and the observed decoupling may be artifactual rather than reflective of independent meta-cognition.

minor comments (2)

[§4] §4 (Experiments): the abstract states clear findings but supplies no data tables, error bars, model details, or exclusion criteria; these must be added with full per-domain breakdowns and statistical tests to allow verification of the scaling and decoupling claims.
[§3.2] §3.2 (Metric Definition): clarify the exact procedure for computing capability boundaries and MCC (including any thresholds or cross-validation steps) so readers can reproduce the dynamic labeling without ambiguity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback, particularly on the methodological independence of our dynamic difficulty labeling. We address the concern below and commit to revisions that strengthen the presentation of this aspect.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Benchmark Construction): the claim that MCC on dynamically identified difficulties isolates selection rationality from accuracy is load-bearing for the decoupling result, yet the method for determining per-model capability boundaries is not shown to be independent of the same accuracy patterns used to compute final performance; if boundaries are fitted from model error rates on the benchmark tasks, the MCC scores become performance-dependent by construction and the observed decoupling may be artifactual rather than reflective of independent meta-cognition.

Authors: We agree that explicit demonstration of independence is necessary for the claim to hold. In the current manuscript, capability boundaries are computed per model by first running both reasoning modes on a disjoint calibration subset of tasks (distinct from the final evaluation set) and identifying the accuracy crossover point where one mode reliably outperforms the other; these fixed per-model thresholds are then applied to label the main benchmark tasks before MCC is calculated on the evaluation set. This two-stage procedure avoids direct fitting from the accuracy patterns used for final performance. That said, the manuscript does not currently include a quantitative check (e.g., correlation between calibration-set boundaries and evaluation-set accuracies) or a diagram of the pipeline. We will add both in the revision, along with an ablation that recomputes MCC using fixed (non-dynamic) difficulty labels to quantify how much the observed decoupling depends on the dynamic procedure. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in AdaptMMBench derivation chain

full rationale

The paper defines AdaptMMBench with MCC-based evaluation of mode selection rationality via dynamic per-model difficulty identification from capability boundaries. No quoted equations, definitions, or self-citations reduce the reported decoupling of selection from accuracy to a fitted input or self-referential construction. The isolation of meta-cognition is presented as an independent metric without the specific reduction (e.g., difficulty labels computed directly from the same accuracy scores used for final performance) required to flag circularity under the enumerated patterns. The benchmark remains self-contained with external falsifiability via its multi-dimensional process metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the domain assumption that MCC on dynamically labeled difficulties isolates meta-cognitive selection ability and that key-step coverage is a reliable proxy for reasoning quality; no free parameters or new entities are introduced.

axioms (1)

domain assumption Task difficulty can be meaningfully and dynamically determined from each model's own capability boundaries without circularity or selection bias
This is invoked to separate adaptive mode selection from general performance in the MCC calculation

pith-pipeline@v0.9.0 · 5539 in / 1255 out tokens · 33457 ms · 2026-05-16T07:47:31.580468+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.

AdaptMMBench utilizes a Matthews Correlation Coefficient (MCC) metric to evaluate the selection rationality of different reasoning modes, isolating this meta-cognition ability by dynamically identifying task difficulties based on models' capability boundaries.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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

key step coverage aligns with performance, though tool effectiveness remains highly inconsistent across model architectures

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