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VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal Understanding in Video

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Multimodal ReasoningVideo Question AnsweringMathematical UnderstandingTemporal ReasoningVisual Grounding
Abstract:

Mathematical reasoning in real-world video presents a fundamentally different challenge than static images or text. It requires interpreting fine-grained visual information, accurately reading handwritten or digital text, and integrating spoken cues, often dispersed non-linearly over time. In such multimodal contexts, success hinges not just on perception, but on selectively identifying and integrating the right details from a rich and noisy stream of content. To this end, we introduce VideoMathQA, a benchmark designed to evaluate whether models can perform such temporally extended cross-modal reasoning on videos. The benchmark spans 10 diverse mathematical domains, covering videos from 10 seconds to over 1 hour. We employ graduate-level experts to ensure high quality, for over 920 man-hours of annotation. To reflect real-world scenarios, questions are designed around three core reasoning challenges: direct problem solving, conceptual transfer, which requires applying learned methods to new problems; and deep instructional comprehension, involving multi-step reasoning over extended explanations and partially worked-out solutions. Each question includes multi-step reasoning annotations, enabling fine-grained diagnosis of model capabilities. Through this benchmark, we establish an evaluation framework for models that must reason, rather than merely perceive, jointly ground concepts across visual, audio, and textual modalities, across temporally extended mathematical problem settings.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces VideoMathQA, a benchmark for evaluating mathematical reasoning in educational videos across multimodal inputs. It resides in the 'Benchmark Development for Video-Based Mathematical Reasoning' leaf, which contains only two papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under 'Multimodal Mathematical Reasoning and Comprehension,' one of nine major branches in a field that spans teacher training, instructional design, special education, and cognitive mechanisms. The small sibling count suggests this specific focus on benchmark creation for video-based mathematical reasoning remains an emerging area.

The taxonomy reveals neighboring work in AI tutoring systems and video generation paradigms, both exploring multimodal reasoning but through different lenses—adaptive instruction versus generative modeling. Broader branches like 'Instructional Interventions' and 'Technology-Enhanced Platforms' contain substantially more papers, reflecting mature research on pedagogical strategies and learning outcomes. VideoMathQA's position emphasizes computational evaluation over intervention design, distinguishing it from the field's dominant focus on classroom implementation and teacher development. The scope notes clarify that benchmark studies exclude human learning outcomes and pedagogical design, reinforcing this boundary.

Among 27 candidates examined, the contribution-level analysis shows mixed novelty signals. The core benchmark contribution (Contribution A) examined 7 candidates with no clear refutations, suggesting relative novelty in this limited search scope. However, the fine-grained annotation contribution (Contribution B) examined 10 candidates and found 2 refutable cases, indicating more substantial prior work on temporal grounding and multi-step reasoning annotations. The evaluation framework contribution (Contribution C) examined 10 candidates with no refutations. These statistics reflect a focused search, not an exhaustive literature review, and suggest the benchmark's novelty lies more in its integrated design than individual components.

Given the limited search scope of 27 candidates, the analysis captures immediate neighbors but cannot confirm broader field coverage. The sparse taxonomy leaf and low refutation counts suggest the integrated benchmark approach may offer value, though the temporal annotation component appears less distinctive. The field's fragmentation across pedagogical and computational branches means related work may exist outside the semantic search radius, particularly in adjacent areas like worked-example analysis or interactive problem-solving environments.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: mathematical reasoning in educational videos. The field encompasses a broad spectrum of research directions, organized into nine major branches that reflect distinct emphases on technology, pedagogy, and learner needs. Multimodal Mathematical Reasoning and Comprehension focuses on how learners integrate visual, auditory, and symbolic information from video content, often developing benchmarks and computational models to assess understanding. Teacher Professional Development and Noticing examines how educators use video to refine their instructional awareness and pedagogical skills, while Instructional Design and Video Features investigates the structural and aesthetic choices—such as pacing, worked examples, and dynamic visualizations—that shape learning outcomes. Other branches address targeted interventions (including technology-enhanced platforms and assistive tools for special education), cognitive mechanisms underlying video-based learning, game-based and interactive environments, and the design of video tasks that promote authentic mathematical practice. Together, these branches illustrate a field that bridges computational analysis, instructional theory, and practical classroom application. Recent work highlights contrasting priorities: some studies emphasize automated assessment and multimodal benchmarks (e.g., VideoMathQA[0] and VideoMathQA[1]), while others explore how video supports teacher noticing (Teacher Noticing Video[2], Proportional Reasoning Noticing[11]) or how specific design features—such as short-form content (Short Math Videos[9]) or worked examples (Worked Example Videos[21])—affect engagement and comprehension. VideoMathQA[0] sits squarely within the benchmark development cluster, contributing a dataset and evaluation framework for video-based mathematical reasoning that complements similar efforts in multimodal comprehension. Compared to neighboring work like VideoMathQA[1], which also targets video question answering, VideoMathQA[0] emphasizes rigorous evaluation of reasoning capabilities across diverse problem types. This positioning reflects a growing interest in scalable, data-driven approaches to understanding how learners extract and apply mathematical concepts from dynamic visual media, bridging computational modeling with educational assessment.

Claimed Contributions

VideoMathQA benchmark for video-based mathematical reasoning

The authors present VideoMathQA, a new benchmark comprising 420 video-question pairs spanning 10 mathematical domains and three reasoning types (direct problem solving, conceptual transfer, deep instructional comprehension). It evaluates models' ability to integrate visual, textual, and audio cues over time for mathematical reasoning.

7 retrieved papers
Fine-grained multi-step reasoning annotations with temporal grounding

The benchmark includes 2,945 expert-annotated reasoning steps with temporal timestamps, allowing evaluation of both intermediate inference steps and final answers. This enables detailed diagnosis of where models succeed or fail in the reasoning process.

10 retrieved papers
Can Refute
Evaluation framework with multiple strategies and error analysis

The authors develop a comprehensive evaluation framework including multiple-choice, multi-binary, chain-of-thought, and step-wise reasoning evaluation strategies. The framework includes structured error analysis across seven categories to diagnose model limitations and reasoning gaps.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VideoMathQA benchmark for video-based mathematical reasoning

The authors present VideoMathQA, a new benchmark comprising 420 video-question pairs spanning 10 mathematical domains and three reasoning types (direct problem solving, conceptual transfer, deep instructional comprehension). It evaluates models' ability to integrate visual, textual, and audio cues over time for mathematical reasoning.

Contribution

Fine-grained multi-step reasoning annotations with temporal grounding

The benchmark includes 2,945 expert-annotated reasoning steps with temporal timestamps, allowing evaluation of both intermediate inference steps and final answers. This enables detailed diagnosis of where models succeed or fail in the reasoning process.

Contribution

Evaluation framework with multiple strategies and error analysis

The authors develop a comprehensive evaluation framework including multiple-choice, multi-binary, chain-of-thought, and step-wise reasoning evaluation strategies. The framework includes structured error analysis across seven categories to diagnose model limitations and reasoning gaps.

Table of Contents