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arxiv: 2606.19965 · v1 · pith:HIN4TTLBnew · submitted 2026-06-18 · 💻 cs.CV · cs.AI

ROSE: Benchmarking the Perception-to-Action Gap in Multimodal Models

Yihao Wang , Zijian He , Jie Ren , Keze Wang This is my paper

Pith reviewed 2026-06-26 18:35 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords ROSE benchmarkmultimodal large language modelsperception-to-action gapvisual reasoningcoordinate groundingMLLMs evaluationreference-conditioned execution
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The pith

Multimodal models lose up to 44.5 points when shifting from counting objects to executing region-conditioned actions on the same scenes.

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

The paper introduces the ROSE benchmark to measure how reliably multimodal large language models convert fixed visual evidence into different required actions depending on task context. It pairs counting tasks with coordinate-based symbolic execution while holding scenes and regions constant, revealing large performance drops that persist even when the model produces the correct count. Controls for global clicking and matched local regions show that coordinate grounding accounts for only part of the loss. Human performance stays near ceiling at 98.8 percent, highlighting a model-specific bottleneck between perception and context-dependent action.

Core claim

Across nine recent MLLMs, performance drops by as much as 44.5 percentage points from counting-oriented tasks to region-conditioned action on the same visual evidence, and the gap remains on paired scenes where the model returns the correct count; global-click and matched-local controls indicate that coordinate grounding explains only part of the loss, exposing a distinct perception-to-action bottleneck.

What carries the argument

The ROSE benchmark, which couples counting tasks with coordinate-action tasks on fixed scenes while varying region constraints and required symbolic outputs to test reference-conditioned execution.

If this is right

  • Coordinate grounding alone does not explain the full performance loss on action tasks.
  • The perception-to-action gap is model-dependent and appears even when the underlying count is correct.
  • Global-click controls reduce but do not eliminate the drop, pointing to an additional context-integration failure.
  • Human performance near 98.8 percent on the same tasks indicates the gap is not inherent to the visual evidence.

Where Pith is reading between the lines

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

  • Training regimes may under-emphasize explicit reference conditioning when mapping counts to actions.
  • The benchmark design could be reused to test whether scaling or new architectures close the gap without task-specific fine-tuning.
  • Similar controlled pairings could expose analogous bottlenecks in non-visual modalities or in embodied settings.

Load-bearing premise

The benchmark tasks and controls are assumed to isolate a distinct perception-to-action bottleneck rather than prompt sensitivity or training-data overlap with test scenes.

What would settle it

A model that shows no accuracy drop between the paired counting task and the region-conditioned action task on identical scenes and regions would falsify the claimed gap.

Figures

Figures reproduced from arXiv: 2606.19965 by Jie Ren, Keze Wang, Yihao Wang, Zijian He.

Figure 1
Figure 1. Figure 1: Overview of the ROSE benchmark. (a) Given a grid scene without an explicit target name, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The primary counting-to-action gap in ROSE. (a) Counting Avg. averages G-Cnt and [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: VALID–PASS decoupling across ROSE subsets. The dashed line denotes the validity [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Click error typology on ROSE action tasks. Bars show the composition of failed click [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative failure cases in ROSE. In each example, the model’s independently queried [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Representative Chinese glyph pairs rendered under three verified fonts. Each column [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Representative pairs from EMOJISTYLE and EMOJICONTENT. The first row shows the same emoji identities rendered by different providers, whose names are indicated below the corresponding elements. The second row shows visually related but distinct emoji contents rendered in a shared style, with their emoji names provided for reference. difference may affect a small object part, boundary, internal pattern, acc… view at source ↗
Figure 8
Figure 8. Figure 8: Representative pixel-art sources and constructed pairs. The first row shows examples [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Scene-coupled consistency under correct global counting. G. denotes global-count PASS. [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Subset–template PASS heatmaps for representative models. Rows denote visual subsets [PITH_FULL_IMAGE:figures/full_fig_p027_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Difficulty scaling in ROSE. (a,b) Counting and action performance across grid-size [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
read the original abstract

Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts. How reliably can a model turn the same visual evidence into the action required by the current context? To answer this question, we introduce \textsc{ROSE} (\textbf{R}eference-conditioned \textbf{O}ddity and \textbf{S}ymbolic \textbf{E}xecution), a controlled benchmark that holds the visual scene fixed while varying region constraints and required symbolic outputs. Through coupled counting and coordinate-action tasks, \textsc{ROSE} tests whether models can infer an implicit majority reference and act on the resulting fine-grained visual evidence under changing contexts. Across nine recent MLLMs, performance drops by as much as 44.5 percentage points from counting-oriented tasks to region-conditioned action, despite 98.8\% human performance. The gap persists on paired scenes and regions for which the same model returns the correct count, while global-click and matched local controls show that coordinate grounding explains only part of the loss, revealing a distinct, model-dependent bottleneck in turning shared visual evidence into context-specific actions.

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 / 1 minor

Summary. The paper introduces the ROSE benchmark to measure a perception-to-action gap in multimodal LLMs. It pairs counting-oriented tasks with region-conditioned action tasks on fixed visual scenes, reporting performance drops of up to 44.5 percentage points across nine MLLMs (versus 98.8% human performance). The gap persists even on scenes where the model counts correctly; global-click and matched-local controls are said to show that coordinate grounding accounts for only part of the loss, indicating a distinct model-dependent bottleneck.

Significance. If the benchmark isolates a genuine perception-to-action bottleneck rather than prompt or format confounds, the result would be useful for diagnosing limitations in current MLLMs on context-dependent action tasks. The empirical scope (nine models, human baseline, paired-scene analysis) is a strength; the work is falsifiable and could inform targeted improvements in instruction following and grounding.

major comments (2)
  1. [Abstract] Abstract: The central claim that the observed drop reflects a distinct perception-to-action bottleneck (rather than prompt sensitivity) rests on the assertion that 'global-click and matched local controls show that coordinate grounding explains only part of the loss.' No details are supplied on whether the counting and action prompts were matched for lexical complexity, instruction length, output format, or pre-training alignment; without such matching the gap need not indicate a new bottleneck.
  2. [Abstract] Abstract / paired-scenes analysis: The persistence claim on 'paired scenes and regions for which the same model returns the correct count' assumes that the two tasks are otherwise equivalent once the count is known. The manuscript provides no evidence that other task-specific factors (e.g., required output vocabulary or reasoning depth) have been equalized, leaving open the possibility that the residual gap is still attributable to prompt differences.
minor comments (1)
  1. [Abstract] The abstract states '98.8% human performance' but does not specify the number of human subjects, inter-annotator agreement, or exact task instructions given to humans; these details belong in the main text or appendix.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments on potential prompt confounds and the paired-scene analysis. We respond point by point below, drawing on the manuscript's methods and controls while noting where additional documentation will be added for clarity.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the observed drop reflects a distinct perception-to-action bottleneck (rather than prompt sensitivity) rests on the assertion that 'global-click and matched local controls show that coordinate grounding explains only part of the loss.' No details are supplied on whether the counting and action prompts were matched for lexical complexity, instruction length, output format, or pre-training alignment; without such matching the gap need not indicate a new bottleneck.

    Authors: Section 3.2 and Appendix B describe the prompt templates, which were constructed with parallel phrasing, comparable sentence lengths, and identical reference descriptions to reduce lexical and structural differences. Output formats necessarily differ by task (numeric vs. coordinate lists) but are processed with the same parser. The global-click and matched-local controls hold prompt wording fixed while varying only the coordinate requirement. We agree that an explicit side-by-side comparison of prompt metrics would strengthen the presentation and will insert a table of token counts, sentence lengths, and vocabulary overlap in the revision. revision: partial

  2. Referee: [Abstract] Abstract / paired-scenes analysis: The persistence claim on 'paired scenes and regions for which the same model returns the correct count' assumes that the two tasks are otherwise equivalent once the count is known. The manuscript provides no evidence that other task-specific factors (e.g., required output vocabulary or reasoning depth) have been equalized, leaving open the possibility that the residual gap is still attributable to prompt differences.

    Authors: The paired analysis fixes the visual scene, reference object, and model-generated count, varying only the required output action. Both tasks require the same reference inference step; the additional coordinate output is isolated by the matched-local control. While output vocabulary differs by design, this is the variable under test. We will add quantitative prompt-similarity metrics and an explicit enumeration of reasoning steps in the methods section to document equivalence more transparently. revision: partial

Circularity Check

0 steps flagged

Empirical benchmark with no derivation chain or circular steps

full rationale

This is a purely empirical benchmark paper that introduces ROSE, runs controlled tests on nine MLLMs, and reports measured performance gaps (e.g., up to 44.5 pp drop) against human baselines. No equations, fitted parameters, predictions derived from inputs, or load-bearing self-citations appear in the provided text. The central claims rest on direct task execution and paired-scene controls rather than any reduction to prior author work or definitional equivalence. The results are externally falsifiable via replication on the benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark paper with no mathematical derivations or fitted parameters described in the abstract.

pith-pipeline@v0.9.1-grok · 5743 in / 1012 out tokens · 17315 ms · 2026-06-26T18:35:35.763928+00:00 · methodology

discussion (0)

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Reference graph

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