arxiv: 2407.04973 · v1 · submitted 2024-07-06 · 💻 cs.AI · cs.CL· cs.CV· cs.LG

Recognition: 2 theorem links

· Lean Theorem

LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts

Yijia Xiao , Edward Sun , Tianyu Liu , Wei Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-16 02:42 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CVcs.LG

keywords LogicVistamultimodal LLMslogical reasoningvisual contextsbenchmarkevaluationreasoning capabilitiesMLLM

0 comments

The pith

LogicVista provides a benchmark of 448 visual questions to evaluate logical reasoning in multimodal LLMs across five tasks and nine capabilities.

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

The paper introduces LogicVista to address the lack of systematic tests for logical reasoning in multimodal large language models when images are present. It covers five tasks that together test nine capabilities through 448 multiple-choice questions, each paired with a correct answer and human-written reasoning steps. This setup allows evaluation in both multiple-choice and open-ended formats. A sympathetic reader would care because logical reasoning combined with visuals underpins activities such as navigation and puzzle-solving, skills that current model assessments largely ignore. The authors test eight existing MLLMs on the benchmark and release the questions and annotations.

Core claim

LogicVista assesses the integrated logical reasoning capabilities of MLLMs in visual contexts across 5 logical reasoning tasks encompassing 9 different capabilities using a sample of 448 multiple-choice questions. Each question is annotated with the correct answer and the human-written reasoning behind the selection, enabling both open-ended and multiple-choice evaluation.

What carries the argument

The LogicVista benchmark, a set of 448 image-based multiple-choice questions with human reasoning annotations that test logical cognition in visual settings.

Load-bearing premise

The 448 questions and their human-written reasoning annotations accurately and comprehensively capture general logical cognition abilities in visual contexts without significant selection bias or coverage gaps.

What would settle it

A demonstration that high-scoring models on LogicVista fail at comparable logical tasks with new images or real-world visual scenarios would show the benchmark does not measure general visual-logic abilities.

read the original abstract

We propose LogicVista, an evaluation benchmark that assesses the integrated logical reasoning capabilities of multimodal large language models (MLLMs) in Visual contexts. Recent advancements in MLLMs have demonstrated various fascinating abilities, from crafting poetry based on an image to performing mathematical reasoning. However, there is still a lack of systematic evaluation of MLLMs' proficiency in logical reasoning tasks, which are essential for activities like navigation and puzzle-solving. Thus we evaluate general logical cognition abilities across 5 logical reasoning tasks encompassing 9 different capabilities, using a sample of 448 multiple-choice questions. Each question is annotated with the correct answer and the human-written reasoning behind the selection, enabling both open-ended and multiple-choice evaluation. A total of 8 MLLMs are comprehensively evaluated using LogicVista. Code and Data Available at https://github.com/Yijia-Xiao/LogicVista.

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

LogicVista adds a targeted benchmark for visual logical reasoning in MLLMs with 448 annotated questions, but lacks enough detail on question construction to confirm broad coverage.

read the letter

LogicVista puts together a new set of 448 multiple-choice questions aimed at logical reasoning in images, split across 5 tasks and 9 capabilities, with human-written reasoning steps attached to each. They run it on 8 MLLMs and release the data and code. That fills a practical gap since most existing multimodal tests lean toward perception or simple math rather than the chained logic needed for navigation or puzzles. The annotations for open-ended evaluation are a useful addition too. The paper does the straightforward work of defining the tasks, collecting the items, and reporting model scores without overclaiming. The main soft spot is the missing detail on how the questions were chosen and checked. The abstract gives no numbers on visual variety, sampling method, or agreement checks against standard logic categories, so it is hard to tell if the set overweights certain capabilities or carries annotator bias. If the full paper has a clear section on sourcing and validation, that would tighten things up; otherwise the results rest on an unexamined sample. This is the kind of paper that matters for groups building or testing MLLMs on applied reasoning tasks. It is worth discussing in a reading group to look at the actual questions and model breakdowns. I would not cite it unless I started using the benchmark myself. It should go to peer review so referees can press on the construction details and suggest expansions.

Referee Report

2 major / 2 minor

Summary. The paper introduces LogicVista, a new benchmark for assessing the integrated logical reasoning capabilities of multimodal large language models (MLLMs) in visual contexts. It consists of 448 multiple-choice questions spanning 5 logical reasoning tasks that together cover 9 distinct capabilities. Each question includes the correct answer and human-written reasoning annotations to support both multiple-choice and open-ended evaluation. The authors evaluate 8 MLLMs on the benchmark and release the code and data.

Significance. If the questions are shown to be representative and free of major selection bias, LogicVista would fill a clear gap by providing a systematic visual-context benchmark for logical reasoning, an area where current MLLM evaluations remain limited. The release of code, data, and human reasoning annotations is a clear strength that supports reproducibility and further research.

major comments (2)

[Benchmark Construction] The central claim that the 448 questions comprehensively cover the 9 capabilities across 5 tasks without significant selection bias or gaps is not supported by sufficient methodological detail. The manuscript provides no quantitative breakdown (e.g., number of questions per capability or task), sampling strategy, visual diversity metrics, or validation against external logical-reasoning taxonomies in the benchmark-construction section.
[Annotation Process] The human-written reasoning annotations are presented as enabling reliable open-ended evaluation, yet no inter-annotator agreement statistics or validation procedure for these annotations are reported, which is load-bearing for claims about the benchmark's utility beyond multiple-choice accuracy.

minor comments (2)

[Abstract and §3] The abstract states 'a sample of 448 multiple-choice questions' but does not clarify whether this is the full benchmark size or a subset; this should be stated explicitly in the main text.
[Figures and Tables] Figure captions and table headers should explicitly list the 5 tasks and 9 capabilities to improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on LogicVista. We agree that additional methodological details are needed to support the claims of comprehensive coverage and annotation reliability. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

Referee: [Benchmark Construction] The central claim that the 448 questions comprehensively cover the 9 capabilities across 5 tasks without significant selection bias or gaps is not supported by sufficient methodological detail. The manuscript provides no quantitative breakdown (e.g., number of questions per capability or task), sampling strategy, visual diversity metrics, or validation against external logical-reasoning taxonomies in the benchmark-construction section.

Authors: We acknowledge that the current manuscript lacks these details in the benchmark-construction section. In the revised version, we will expand this section to include: a table reporting the exact number of questions per task and per capability (totaling 448), a description of the sampling strategy (stratified selection to ensure balanced coverage of the 9 capabilities without over-representation), quantitative visual diversity metrics (e.g., distribution across image sources, types, and complexity levels), and explicit mapping/validation against established logical-reasoning taxonomies from cognitive science to demonstrate coverage and minimize gaps or bias. revision: yes
Referee: [Annotation Process] The human-written reasoning annotations are presented as enabling reliable open-ended evaluation, yet no inter-annotator agreement statistics or validation procedure for these annotations are reported, which is load-bearing for claims about the benchmark's utility beyond multiple-choice accuracy.

Authors: We agree that inter-annotator agreement statistics and validation details are necessary to substantiate the reliability of the human-written reasoning annotations for open-ended evaluation. In the revision, we will add a dedicated subsection describing the annotation process (including annotator qualifications and guidelines), report inter-annotator agreement metrics (e.g., Fleiss' kappa across reasoning steps), and outline the validation procedure (e.g., review rounds for consistency and accuracy). This will directly support the benchmark's utility claims. revision: yes

Circularity Check

0 steps flagged

No circularity: direct benchmark construction and evaluation with no derivations or self-referential steps

full rationale

The paper introduces LogicVista as a new benchmark dataset of 448 multiple-choice questions with human annotations, then directly evaluates 8 MLLMs on it across 5 tasks and 9 capabilities. No equations, parameter fitting, predictions derived from inputs, or load-bearing self-citations appear in the provided text. The central claim rests on the explicit creation and application of the dataset rather than any reduction of results to prior fitted values or self-defined constructs. This is a standard empirical benchmark paper with no mathematical derivation chain to inspect for circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen tasks and questions validly measure logical reasoning abilities; no free parameters or invented entities are introduced.

axioms (1)

domain assumption The five logical reasoning tasks and nine capabilities adequately represent general logical cognition in visual contexts.
Invoked in the abstract when defining the benchmark scope without further justification or validation details.

pith-pipeline@v0.9.0 · 5455 in / 1068 out tokens · 48400 ms · 2026-05-16T02:42:19.597729+00:00 · methodology

discussion (0)

Forward citations

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spending on IT hardware will decline

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