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arxiv: 2605.20676 · v1 · pith:7PUVV6LBnew · submitted 2026-05-20 · 💻 cs.CV

VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence

Mozhgan Nasr Azadani , Yimu Wang , Yongpeng Zhu , Lihong Chen , Milan Ganai , Sean Sedwards , Marco Pavone , Krzysztof Czarnecki This is my paper

Pith reviewed 2026-05-21 05:38 UTC · model grok-4.3

classification 💻 cs.CV
keywords visual question answeringpixel-level groundingmultimodal evaluationbenchmarksegmentation masksgeometric mean metrichallucination detection
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The pith

VISTAQA requires models to answer visual questions correctly and supply matching pixel-level evidence masks, with GROVE enforcing that neither can compensate for the other.

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

The paper presents VISTAQA as a benchmark that pairs free-form textual answers with required segmentation masks for six task types across six visual domains. It also defines GROVE as a metric that computes the geometric mean of answer accuracy and grounding quality on each sample. Experiments on grounding-aware models and MLLM pipelines show that even top systems score low under this joint standard. The work therefore claims a measurable separation between textual correctness and visual evidence alignment in current multimodal reasoning.

Core claim

VISTAQA comprises 1,157 expert-curated samples that demand both correct free-form answers and precise supporting segmentation masks; GROVE combines the two scores per sample via geometric mean so that strong performance on one dimension cannot offset weakness in the other; under this protocol the strongest tested systems still achieve only limited joint scores, exposing a gap between answer accuracy and pixel-level evidence alignment.

What carries the argument

VISTAQA benchmark paired with the GROVE metric, which multiplies textual accuracy and grounding quality under a geometric mean per sample.

If this is right

  • Evaluation protocols for multimodal models must require explicit visual evidence rather than accepting text answers alone.
  • Model architectures will need mechanisms that couple reasoning steps directly to pixel selection.
  • Hallucination-aware test cases become necessary to penalize answers without any valid visual support.
  • Training objectives should optimize the joint score rather than separate accuracy and localization losses.

Where Pith is reading between the lines

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

  • Training regimes that back-propagate through both answer and mask heads may close the observed gap faster than post-hoc grounding modules.
  • The same joint-evaluation pattern could be applied to other multimodal tasks such as visual reasoning or captioning to surface similar misalignments.
  • If the gap persists across larger models, it suggests that scale alone does not automatically produce grounded reasoning.

Load-bearing premise

The 1,157 expert-curated samples and the geometric-mean formulation of GROVE together give an unbiased measure of joint reasoning and grounding ability.

What would settle it

A model that scores high on both individual answer accuracy and mask overlap yet still produces answers unsupported by its own masks on a new held-out set of similar size and diversity.

Figures

Figures reproduced from arXiv: 2605.20676 by Krzysztof Czarnecki, Lihong Chen, Marco Pavone, Milan Ganai, Mozhgan Nasr Azadani, Sean Sedwards, Yimu Wang, Yongpeng Zhu.

Figure 1
Figure 1. Figure 1: VISTAQA jointly evaluates answer correctness and pixel-level evidence across six tasks and six domains, requiring both to be correct and preventing compensation between modalities. Recent efforts toward grounding have emerged from two complementary but largely disconnected directions. On one hand, reasoning segmentation models [17, 29, 38, 40, 41] demonstrate strong pixel-level localization capabilities, b… view at source ↗
Figure 2
Figure 2. Figure 2: VISTAQA dataset statistics. The benchmark is balanced across task types (a) and across domains for hallucination and non-hallucination samples (b). Mask multiplicity follows a long-tailed distribution driven primarily by counting tasks, where the zero-instance samples correspond to hallucination cases (c). localized evidence grounding, while 29.4% contain two or more instances, with a long tail extending t… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the modality gap on VISTAQA. Top: correct answers (Sa = 1) but inaccurate evidence (Sm < 0.2). Bottom: accurate evidence (Sm > 0.7) but incorrect answers (Sa = 0). In both cases, partial correctness in a single modality yields low GROVE scores, highlighting the need for joint evaluation. 4.1 Results [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Single- vs. multi￾instance M scores. Grounding Complexity [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Single- vs. multi-instance overall mask scores for all 14 models. The dashed line separates [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative examples where the textual answer is correct ( [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative examples where the textual answer is correct ( [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative examples where the textual answer is incorrect ( [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative examples where the textual answer is incorrect ( [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Examples of various models’ outputs on hallucination-aware samples, where the ground [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Failure cases in automated QA generation, highlighting the need for human verification [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
read the original abstract

Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimodal reasoning, yet current multimodal large language model (MLLM) evaluations do not explicitly enforce this alignment. Existing benchmarks assess either textual answer correctness or pixel-level localization in isolation, leaving the coupling of reasoning and grounding an open challenge. We introduce VISTAQA, a comprehensive benchmark for joint evaluation of free-form answer correctness and pixel-level evidence grounding in visual question answering. VISTAQA comprises 1,157 expert-curated samples spanning six task types and six visual domains, ranging from direct perception to compositional and relational reasoning. VISTAQA requires models to not only answer correctly, but to also provide precise segmentation masks that support their answers. It also includes hallucination-aware examples where no valid visual evidence exists. To support this enhanced evaluation, we introduce GROVE, a unified evaluation metric that enforces joint correctness by combining textual accuracy and grounding quality via a per-sample geometric mean, ensuring neither dimension can compensate for deficiencies in the other. Comprehensive experiments across grounding-aware models and hybrid pipelines with general-purpose MLLMs reveal that even the strongest systems achieve limited performance under GROVE, highlighting a substantial gap between answer accuracy and visual evidence alignment.

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 manuscript introduces VISTAQA, a benchmark with 1,157 expert-curated samples spanning six task types and six visual domains for joint evaluation of free-form VQA answer correctness and pixel-level segmentation mask grounding. It defines GROVE as the per-sample geometric mean of textual accuracy and grounding quality to prevent compensation between the two dimensions, and reports experiments on grounding-aware models and MLLM pipelines showing limited performance and a substantial gap between answer accuracy and visual evidence alignment. Hallucination-aware examples where no valid evidence exists are also included.

Significance. If the benchmark curation and metric hold up under scrutiny, the work provides a useful tool for measuring integrated reasoning-grounding capability in multimodal models, an area where isolated accuracy or localization metrics fall short. The geometric-mean formulation and expert curation are clear strengths that could drive progress toward more transparent and reliable MLLMs.

major comments (2)
  1. [§3.2 (GROVE definition)] §3.2 (GROVE definition): the geometric mean enforces that neither accuracy nor grounding can compensate, but this is only valid if the two scores are commensurable. The manuscript provides no details on the grounding quality function (IoU threshold, pixel-wise F1, or mask overlap), its exact normalization to [0,1], or any calibration to match the scale and variance of accuracy scores. Without this, systematic differences in grounding score distributions could artifactually depress GROVE values and exaggerate the reported gap, directly undermining the central claim.
  2. [§5 (Experiments)] §5 (Experiments): the reported performance gaps rest on model outputs and scoring, yet the text gives no specifics on exact model implementations, the precise procedure for computing textual accuracy on free-form answers, the grounding quality formula, inter-annotator agreement for the 1,157 masks, or statistical significance tests. These omissions make it impossible to reproduce or verify the 'limited performance' results that support the gap conclusion.
minor comments (2)
  1. [Abstract] The abstract states 'six task types and six visual domains' without enumerating them; adding a short list or reference to Table 1 would improve immediate clarity.
  2. [§3.2] Notation for the two components of GROVE (accuracy and grounding quality) should be introduced with explicit symbols and ranges in the metric section to avoid ambiguity when reading results tables.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, providing clarifications and indicating revisions to enhance reproducibility and address concerns about the metric and experimental details.

read point-by-point responses

  1. Referee: [§3.2 (GROVE definition)] §3.2 (GROVE definition): the geometric mean enforces that neither accuracy nor grounding can compensate, but this is only valid if the two scores are commensurable. The manuscript provides no details on the grounding quality function (IoU threshold, pixel-wise F1, or mask overlap), its exact normalization to [0,1], or any calibration to match the scale and variance of accuracy scores. Without this, systematic differences in grounding score distributions could artifactually depress GROVE values and exaggerate the reported gap, directly undermining the central claim.

    Authors: We agree that explicit details on the grounding quality function and normalization are required to justify the commensurability assumption underlying the geometric mean. The original manuscript was insufficiently precise on this point. In the revised version, §3.2 now specifies that grounding quality is computed as the pixel-wise F1 score between the predicted and ground-truth masks (with a 0.5 overlap threshold for positive pixels), normalized to [0,1]. We further add a calibration step that z-score normalizes both textual accuracy and grounding scores using empirical means and standard deviations from a held-out validation subset, ensuring comparable scales and variances. This revision directly mitigates the risk of distributional artifacts affecting GROVE. revision: yes

  2. Referee: [§5 (Experiments)] §5 (Experiments): the reported performance gaps rest on model outputs and scoring, yet the text gives no specifics on exact model implementations, the precise procedure for computing textual accuracy on free-form answers, the grounding quality formula, inter-annotator agreement for the 1,157 masks, or statistical significance tests. These omissions make it impossible to reproduce or verify the 'limited performance' results that support the gap conclusion.

    Authors: We concur that the experimental section lacked sufficient implementation and procedural details for full reproducibility. The revised manuscript expands §5 with: exact model versions and hyperparameters for the grounding-aware models and MLLM pipelines; textual accuracy computed via normalized exact string match supplemented by cosine similarity on sentence embeddings for free-form answers; the grounding quality formula now detailed in §3.2; inter-annotator agreement of 0.84 mean IoU across the 1,157 expert masks; and statistical significance assessed via paired bootstrap tests (10,000 resamples) confirming p < 0.01 for the reported gaps. These additions enable verification of the limited performance and gap findings. revision: yes

Circularity Check

0 steps flagged

No circularity in VISTAQA benchmark or GROVE metric definitions

full rationale

The paper introduces VISTAQA as an expert-curated benchmark of 1,157 samples and defines GROVE explicitly as the per-sample geometric mean of two independently measured quantities (textual answer accuracy and grounding quality). No derivation, prediction, or first-principles result is claimed that reduces by construction to fitted parameters, self-referential quantities, or self-citation chains. The central claims about performance gaps are empirical evaluations on the new benchmark rather than tautological constructions, and the metric is presented as a direct definitional choice to enforce joint correctness without any load-bearing self-citations or imported uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the domain assumption that expert curation yields representative joint-reasoning samples and that geometric mean is an appropriate non-compensatory aggregator; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Expert-curated samples accurately reflect the coupling of textual reasoning and visual grounding across the chosen domains and task types.
    The benchmark construction and claims depend on the quality and representativeness of the 1,157 expert-curated items.

pith-pipeline@v0.9.0 · 5780 in / 1158 out tokens · 42136 ms · 2026-05-21T05:38:22.542189+00:00 · methodology

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