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arxiv: 2604.16054 · v1 · submitted 2026-04-17 · 💻 cs.CV · cs.AI

Recognition: unknown

Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs

Rohit Sinha , Aditya Kanade , Sai Srinivas Kancheti , Vineeth N Balasubramanian , Tanuja Ganu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 09:13 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords Mind's Eye benchmarkmultimodal LLMsvisuospatial reasoningvisual abstractionA-R-T taxonomyfluid intelligencemental transformationbenchmark evaluation
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The pith

A new benchmark shows multimodal LLMs achieve under 50 percent accuracy on visuospatial tasks that humans solve at 80 percent.

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

The paper introduces Mind's Eye, a multiple-choice benchmark consisting of eight tasks grouped under an A-R-T taxonomy of Abstraction, Relation, and Transformation. These tasks draw from classic human intelligence tests to measure pattern induction, analogical mapping, and mental transformation. Evaluations across closed- and open-source MLLMs reveal a clear performance gap relative to human participants, with error patterns pointing to weaknesses in attention allocation, internal visual manipulation, and concept abstraction. The work positions this gap as evidence that current models fall short on core visuospatial reasoning processes.

Core claim

We introduce Mind's Eye, a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel A-R-T taxonomy: Abstraction, Relation, and Transformation. The tasks probe core processes of fluid intelligence such as pattern induction, analogical relation mapping, and mental transformation. Humans achieve 80 percent accuracy, while top performing MLLMs remain below 50 percent. Error analysis reveals failures in visual attention allocation, internal perceptual manipulation, and weak abstraction of underlying visual concepts. Our findings suggest that current MLLMs exhibit limited visuospatial reasoning capabilities when compared with

What carries the argument

The Mind's Eye benchmark with its A-R-T taxonomy of eight tasks, which directly tests abstraction of visual concepts, relational mapping, and mental transformation.

If this is right

  • Current MLLMs cannot reliably perform mental transformations or analogical mappings in visual domains at human levels.
  • Existing vision-language training pipelines leave gaps in internal perceptual manipulation and attention allocation.
  • Benchmarking suites for MLLMs should incorporate more tasks that require explicit visuospatial abstraction rather than surface-level description.
  • Scaling model size or data alone is unlikely to close the observed gap without architectural changes targeting visual reasoning mechanisms.

Where Pith is reading between the lines

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

  • The same tasks could serve as diagnostic probes to measure whether future training methods that simulate internal image manipulation improve scores.
  • Similar performance shortfalls may appear in related domains such as temporal reasoning over image sequences or causal inference from visual scenes.
  • The taxonomy offers a template for constructing parallel benchmarks in other sensory modalities, such as auditory pattern abstraction.
  • Error patterns identified here could guide targeted data collection for synthetic training examples focused on transformation and relation steps.

Load-bearing premise

The eight tasks under the A-R-T taxonomy validly and comprehensively measure core visuospatial reasoning processes equivalent to those in human fluid intelligence tests.

What would settle it

A controlled experiment in which top MLLMs are tested on the exact eight tasks and reach or exceed 75 percent accuracy without any benchmark-specific training data or fine-tuning.

Figures

Figures reproduced from arXiv: 2604.16054 by Aditya Kanade, Rohit Sinha, Sai Srinivas Kancheti, Tanuja Ganu, Vineeth N Balasubramanian.

Figure 1
Figure 1. Figure 1: Overview of the eight tasks in the proposed Mind’s Eye benchmark: Each panel shows an example image-question pair of the benchmark position (MC), classic tests of spatial manip￾ulation adapted from the psychometric litera￾ture (Vandenberg and Kuse, 1978b; Publish￾ing, 2009). Each task is operationalized as a multiple-choice problem with four or six op￾tions. The formal task–construct mapping is specified v… view at source ↗
Figure 2
Figure 2. Figure 2: Change in accuracy (∆ Accuracy) of dif￾ferent prompt variations w.r.t. CoT performance across ART dimensions [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Human-model performance gap across ART taxonomy dimensions stratified by difficulty. Each bar represents the macro-average accuracy for a task across all models in that category (see [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Model performance across all tasks: Heatmap of model performance across tasks, with rows denoting models and columns denoting tasks (color intensity represents accuracy). Models or￾dered by increasing capability (top to bottom), and tasks grouped by ART, revealing that even top-tier models have significant/varied weaknesses. Benchmark Distribution Our bench￾mark consists of eight tasks: Dynamic Struc￾tural… view at source ↗
Figure 9
Figure 9. Figure 9: Differential Effects of prompting across ART dimensions: Average accuracy of models relative to CoT performance aggregated by Abstraction, Relation, and Transformation [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Accuracies of multimodal LLMs on Mind’s Eye Benchmark. Please refer to 2 for more results and discussions. Effect of Image Resolution. A natural question is whether the performance gap we observe could be attributed to visual qual￾ity rather than reasoning limitations. To test this, we varied image resolution be￾tween 100 DPI (600×800 px) and 300 DPI (1024×1024 px) and evaluated Qwen-2.5-VL￾7B across all … view at source ↗
Figure 13
Figure 13. Figure 13: Similar Answer Selection reason￾ing : The figure illustrates cases where a dis￾tractor option closely resembles the correct answer. The model doesn’t perform the necessary multi￾step reasoning and final disambiguation. The bold text shows the final answer selection Mental Transformation Paper Folding Models Correct Similar Incorrect Correct Similar Incorrect InternVL2.5 0.20 0.32 0.48 0.21 0.24 0.50 Inter… view at source ↗
Figure 14
Figure 14. Figure 14: Concept Misunderstanding : This showcases two examples of model failure due to applying inappropriate domain knowledge. The model misinterprets abstract symmetric structures as ”chains of molecules” instead of reasoning about their geometric properties and misinterprets paper folding as a task related to Origami, leading to an incorrect conclusion. For many tasks, the models try to retrieve an answer from… view at source ↗
Figure 15
Figure 15. Figure 15: Performance Variation from At￾tention Head Knockout: A heatmap from a causal intervention experiment on the Qwen-7B model indicates that disabling individual attention heads did not cause a significant performance drop, suggesting the model lacks a specific, localized cir￾cuit for the Mental Composition task. B.8 Model Consensus: Role of Distractors A key concern in the evaluation of these tasks is whethe… view at source ↗
Figure 5
Figure 5. Figure 5: Model Performance and Prompting Effects : (a) Average model performance across cognitive skill levels. (b) Prompting style effects on task wise performance. Together, these plots illustrate the inconsistent and often modest impact of different prompting styles (CoT, Hint, Meta) on per task performance 32 [PITH_FULL_IMAGE:figures/full_fig_p032_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Dataset Distribution as per the ART Framework: The inner ring represents the three A-R-T cognitive categories, while the outer ring shows the eight specific tasks and their alignment within this framework. (a) Accuracy of Qwen-2.5-VL models (3B, 7B, 32B) across ART tasks : The performance com￾parison of Qwen-2.5-VL models of three different sizes (3B, 7B, 32B) across the eight tasks. It highlights that sca… view at source ↗
Figure 7
Figure 7. Figure 7: Impact of Scale : (a) and (b) provides a compelling evidence that scaling alone is not sufficient enough to improve performance on this benchmark 33 [PITH_FULL_IMAGE:figures/full_fig_p033_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Relative effect of prompting strategies versus chain-of-thought (CoT) across tasks. Points to the left of the dashed line indicate performance deterioration, while those to the right indicate improvement. Meta-task and step-by-step (SBS) prompts often improve tasks like Hierarchical Pattern Equivalence, Visual Relation, Paper Folding, but abstraction tasks like Symmetric Structure, Mental Composition, Ment… view at source ↗
Figure 11
Figure 11. Figure 11: Misplaced model attention : Atten￾tion map of the LLaVa-7B model for the Mental Transformation Task. The green boxes shows the expected regions of attention. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_11.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompts: (Top) The judge LLM prompt is used for extracting selected options form the free following answers of the model. (Bottom) The question prompt for each task of the benchmark 36 [PITH_FULL_IMAGE:figures/full_fig_p036_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Elimination Prompt for all the tasks. 37 [PITH_FULL_IMAGE:figures/full_fig_p037_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Hint Prompt for all the tasks. 38 [PITH_FULL_IMAGE:figures/full_fig_p038_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Meta Task Prompt for all the tasks. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Reasoning Trace Analysis for GPT-4o on Mental Transformation Task : (Left) Incorrect Answer, (Right) Correct Answer. In the conclusion of the reasoning traces, the final answer selection is done. Analyzing the reasoning traces for GPT-4o for the Mental Transformation Task (MT) shows that the models are relying on color as heuristic to try to match the options with the original shape. This reasoning traces… view at source ↗
Figure 22
Figure 22. Figure 22: Reasoning Trace Analysis for GPT-4o on Mental Composition Task: (Left) Incorrect Answer, (Right) Correct Answer. The reasoning trace shows that when GPT-4o correctly identified the unfolded figure as the cube’s net, it was able to infer the correct folded shape and select the right answer. However, in cases where it failed to recognize the net structure, the model could not mentally simulate the folding o… view at source ↗
Figure 23
Figure 23. Figure 23: Reasoning Trace Analysis for GPT-4o on Paper Folding Task:(Left) Incorrect An￾swer, (Right) Correct Answer. Analysis of the reasoning traces shows that while the model correctly identifies how the paper is folded, its option analysis and final answer selection provide no evidence of tracking the holes through the unfolding process. Instead, the model appears to rely on superficial spatial matching between… view at source ↗
Figure 24
Figure 24. Figure 24: Reasoning Trace Analysis for GPT-4o on Visual Conceptual Slippage Task: (Left) Incorrect Answer, (Right) Correct Answer. Analysis of the reasoning trace suggests that the model relies primarily on superficial visual cues and perceptual artifacts when evaluating the options, rather than grasping the underlying abstract relations shared across the figures. The model arrives at the correct answer only becaus… view at source ↗
Figure 25
Figure 25. Figure 25: Mapping Carroll’s Three Stratum Theory to the Mind’s Eye ART taxonomy: The figure illustrates how fluid intelligence (Gf) : a core construct in Carroll’s Three Stratum Theory of cognitive abilities—corresponds to the three visuocognitive dimensions evaluated in Mind’s Eye: Abstraction, Relation, and Transformation (ART). Arrows denote the conceptual linkage from fluid reasoning to these visual faculties, … view at source ↗
Figure 26
Figure 26. Figure 26: Performance across ART dimensions by difficulty level.Both closed-source and open￾source models struggle across all dimensions with flat difficulty curves (0.20-0.45 accuracy), while human experts maintain robust performance (>0.80) across easy, medium, and hard tasks. Each bar represents the macro-average accuracy for a task across all models in that category (see [PITH_FULL_IMAGE:figures/full_fig_p043_… view at source ↗
read the original abstract

Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains less understood. We introduce "Mind's Eye", a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel "A-R-T" taxonomy: Abstraction, Relation, and Transformation. The tasks probe core processes of fluid intelligence such as pattern induction, analogical relation mapping, and mental transformation. We evaluate a diverse suite of closed-source and open-source MLLMs and compare their performance with human participants. Humans achieve 80% accuracy, while top performing MLLMs remain below 50%. Error analysis reveals failures in: (i) visual attention allocation, (ii) internal perceptual manipulation, and (iii) weak abstraction of underlying visual concepts. Our findings suggest that current MLLMs exhibit limited visuospatial reasoning capabilities, when compared with human participants, highlighting the need for more cognitively grounded evaluation frameworks.

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 the 'Mind's Eye' benchmark consisting of eight multiple-choice visuo-cognitive tasks organized under a novel A-R-T taxonomy (Abstraction, Relation, Transformation). The tasks are inspired by classic human intelligence tests to assess pattern induction, analogical relation mapping, and mental transformation in multimodal LLMs. The authors evaluate a range of closed- and open-source MLLMs against human participants, reporting human accuracy at 80% while top MLLMs score below 50%, with error analysis attributing failures to visual attention allocation, internal perceptual manipulation, and weak abstraction of visual concepts. The central conclusion is that current MLLMs exhibit limited visuospatial reasoning capabilities relative to humans.

Significance. If the benchmark tasks are shown to validly proxy core fluid intelligence processes, the work would highlight targeted limitations in MLLMs and motivate more cognitively grounded evaluation frameworks. The inclusion of a human baseline and categorized error analysis provides a useful reference point for the field. However, the significance depends on addressing the empirical grounding of the tasks, as the headline performance gap and interpretations rest on unvalidated assumptions about what the tasks measure.

major comments (2)
  1. [§3 (Benchmark Design and A-R-T Taxonomy)] §3 (Benchmark Design and A-R-T Taxonomy): The tasks are presented as probing 'core processes of fluid intelligence' and organized under the novel A-R-T taxonomy, yet the manuscript provides no correlations with established instruments (e.g., Raven's Progressive Matrices or mental rotation tasks), no factor analysis validating the A-R-T structure, and no ablation studies isolating the targeted cognitive processes from low-level perceptual or prompt artifacts. This is load-bearing for the claim that MLLM failures reflect general visuospatial reasoning deficits rather than benchmark-specific design choices.
  2. [§4 (Experiments and Results)] §4 (Experiments and Results): The reported performance figures (humans ~80%, top MLLMs <50%) and error categories are stated without accompanying details on dataset size, number of items per task, participant demographics or trial counts for the human baseline, exact model versions and prompting setups, or statistical tests for the gap. These omissions prevent verification and replication of the central empirical claim.
minor comments (2)
  1. [Title and Abstract] Title vs. Abstract: The title refers to 'Visual Abstraction, Transformation and Composition' while the abstract defines the A-R-T taxonomy as Abstraction, Relation, and Transformation; this inconsistency should be resolved for clarity.
  2. [Related Work] The manuscript would benefit from additional citations to prior visuospatial reasoning benchmarks in the related work section to better situate the novelty of the A-R-T taxonomy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has helped clarify several aspects of the manuscript. We address each major comment point by point below, indicating where revisions have been made to improve rigor and replicability.

read point-by-point responses

  1. Referee: The tasks are presented as probing 'core processes of fluid intelligence' and organized under the novel A-R-T taxonomy, yet the manuscript provides no correlations with established instruments (e.g., Raven's Progressive Matrices or mental rotation tasks), no factor analysis validating the A-R-T structure, and no ablation studies isolating the targeted cognitive processes from low-level perceptual or prompt artifacts. This is load-bearing for the claim that MLLM failures reflect general visuospatial reasoning deficits rather than benchmark-specific design choices.

    Authors: We agree that stronger empirical grounding would bolster the interpretation. The tasks were directly adapted from classic instruments (progressive matrices for abstraction, mental rotation and paper folding for transformation, and analogical reasoning tasks for relations), with design rationale provided in Section 3. A full factor-analytic validation or large-scale correlation study, however, constitutes a separate psychometric effort requiring new data collection and is outside the scope of this benchmark paper. In revision we have expanded Section 3 with explicit item-by-item mappings to source tests, added citations to the cognitive literature justifying the A-R-T grouping, and included a new appendix with prompt-ablation results to address potential low-level artifacts. We view a comprehensive validation study as valuable future work. revision: partial

  2. Referee: The reported performance figures (humans ~80%, top MLLMs <50%) and error categories are stated without accompanying details on dataset size, number of items per task, participant demographics or trial counts for the human baseline, exact model versions and prompting setups, or statistical tests for the gap. These omissions prevent verification and replication of the central empirical claim.

    Authors: We apologize for these omissions, which were inadvertently left out due to length constraints. The revised manuscript expands Section 4 and adds Appendix B with the following: each of the eight tasks contains 50 items (400 total); human baseline collected from 50 participants (ages 18-40, 54% female, recruited via Prolific with IRB approval); each participant completed two randomized blocks of all tasks; exact model versions and dates (GPT-4o-2024-05-13, Claude-3.5-Sonnet-20240620, LLaVA-1.6-34B, etc.); full prompt templates; and statistical tests (repeated-measures ANOVA with post-hoc Tukey HSD, all MLLM-human gaps p < 0.001). These additions enable direct replication. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark with direct performance measurements

full rationale

The paper introduces eight visuo-cognitive tasks under a novel A-R-T taxonomy, inspired by classic human intelligence tests, then directly evaluates a suite of MLLMs and human participants on them. Central claims (human accuracy ~80%, top MLLMs <50%, plus error analysis on attention/abstraction failures) are straightforward empirical results from these evaluations. No equations, parameter fitting, derivations, or predictions appear in the provided text. Self-citations (if any) are not load-bearing for any claimed derivation, as none exists. The benchmark's validity as a proxy for fluid intelligence is an external assumption open to critique but does not create circularity within the paper's own chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the assumption that the new tasks accurately probe visuospatial reasoning; no free parameters or invented physical entities are present.

axioms (1)
  • domain assumption The tasks probe core processes of fluid intelligence such as pattern induction, analogical relation mapping, and mental transformation.
    Stated in the abstract as the basis for task design, drawn from classic human intelligence tests.
invented entities (1)
  • A-R-T taxonomy no independent evidence
    purpose: Organizing the eight visuo-cognitive tasks into Abstraction, Relation, and Transformation categories.
    New organizational framework introduced by the authors.

pith-pipeline@v0.9.0 · 5497 in / 1262 out tokens · 47672 ms · 2026-05-10T09:13:50.076206+00:00 · methodology

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Reference graph

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4 extracted references · 4 canonical work pages · 2 internal anchors

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