Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education

• URL: http://arxiv.org/abs/2605.31212v1
• Date: Fri, 29 May 2026 12:18:08 GMT
• Title: Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education
• Abstract summary: We introduce equation-to-visual generation, a task that requires producing meaningful visuals from arithmetic equations.<n>We construct E2V-Bench, a benchmark spanning four pedagogically grounded visual types, along with automatic metrics for evaluating visual correctness.<n>Our evaluation reveals that recent text-to-image (T2I) models frequently fail on this task, with errors dominated by incorrect object counts and broken relational structure.
• Score: 57.241885377686486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully represent the pedagogical concepts they are intended to teach. Thus, we introduce equation-to-visual generation, a task that, in contrast to conventional image generation, requires producing pedagogically meaningful visuals from arithmetic equations while precisely preserving their numerical and relational structure. Informed by interviews with teachers and an analysis of educational materials, we construct E2V-Bench, a benchmark spanning four pedagogically grounded visual types, along with automatic metrics for evaluating visual correctness. Our evaluation reveals that recent text-to-image (T2I) models frequently fail on this task, with errors dominated by incorrect object counts and broken relational structure. Building on this, we explore benchmark-guided enhancement strategies. These strategies improve representative models, while the remaining gap calls for stronger numerical and relational grounding in future T2I models.

Related papers

• DynEval: Holistic Evaluations of T2I Generative Models in the Wild [6.430113734582911]
  We introduce DynEval, a Dynamic Evaluation framework designed to assess text-to-image alignment and image quality of T2I models.<n>To support scalable training beyond human-annotated data, we construct two large datasets.
  arXiv  Detail & Related papers  (2026-07-13T07:50:54Z)
• Are Text-to-Image Models Inductivist Turkeys? A Counterfactual Benchmark for Causal Reasoning [39.26120643494408]
  We investigate whether text-to-image models can generate images under rules that systematically contradict real-world priors.<n>CF-World organizes each scenario into three progressive levels: factual generation under ordinary world knowledge, explicit counterfactual generation with direct visual instructions, and implicit counterfactual generation requiring causal deduction from altered rules.
  arXiv  Detail & Related papers  (2026-06-23T13:15:07Z)
• Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models [49.93401412148884]
  We introduce FEPBench, a benchmark built from carefully selected high-quality scientific illustrations.<n>We evaluate T2I models along three dimensions: instruction faithfulness, reasoning enrichment, and semantic precision.<n>Results show that even state-of-the-art closed-source models, such as GPT Image 2 and Nano Banana Pro, still suffer from text-rendering bottlenecks, limited reasoning enrichment, and difficulty balancing generation richness with precision.
  arXiv  Detail & Related papers  (2026-06-04T09:49:02Z)
• AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models [58.85362281293525]
  We introduce AcT2I, a benchmark designed to evaluate the performance of T2I models in generating images from action-centric prompts.<n>We experimentally validate that leading T2I models do not fare well on AcT2I.<n>We build upon this by developing a training-free, knowledge distillation technique utilizing Large Language Models to address this limitation.
  arXiv  Detail & Related papers  (2025-09-19T16:41:39Z)
• Structure-aware Contrastive Learning for Diagram Understanding of Multimodal Models [0.609170287691728]
  We introduce a novel training paradigm to enhance the comprehension of diagrammatic images within vision-language models.<n>Our method enables models to develop a more structured and semantically coherent understanding of diagrammatic content.
  arXiv  Detail & Related papers  (2025-09-02T05:02:23Z)
• Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models [40.80541801381895]
  We present Math2Visual, an automatic framework for generating pedagogically meaningful visuals from text descriptions.<n>We construct an annotated dataset of 1,903 visuals and evaluate Text-to-Image (TTI) models for their ability to generate visuals that align with our design.
  arXiv  Detail & Related papers  (2025-06-04T09:08:11Z)
• RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning [88.14234949860105]
  RePrompt is a novel reprompting framework that introduces explicit reasoning into the prompt enhancement process via reinforcement learning.<n>Our approach enables end-to-end training without human-annotated data.
  arXiv  Detail & Related papers  (2025-05-23T06:44:26Z)
• GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning [47.592351387052545]
  GoT-R1 is a framework that applies reinforcement learning to enhance semantic-spatial reasoning in visual generation.<n>We propose a dual-stage multi-dimensional reward framework that leverages MLLMs to evaluate both the reasoning process and final output.<n> Experimental results demonstrate significant improvements on T2I-CompBench benchmark.
  arXiv  Detail & Related papers  (2025-05-22T17:59:58Z)
• Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models [54.052963634384945]
  We introduce the Image Regeneration task to assess text-to-image models. We use GPT4V to bridge the gap between the reference image and the text input for the T2I model. We also present ImageRepainter framework to enhance the quality of generated images.
  arXiv  Detail & Related papers  (2024-11-14T13:52:43Z)
• KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities [93.74881034001312]
  KITTEN is a benchmark for Knowledge-InTensive image generaTion on real-world ENtities.<n>We conduct a systematic study of the latest text-to-image models and retrieval-augmented models.<n>Analysis shows that even advanced text-to-image models fail to generate accurate visual details of entities.
  arXiv  Detail & Related papers  (2024-10-15T17:50:37Z)
• Visual Programming for Text-to-Image Generation and Evaluation [73.12069620086311]
  We propose two novel interpretable/explainable visual programming frameworks for text-to-image (T2I) generation and evaluation. First, we introduce VPGen, an interpretable step-by-step T2I generation framework that decomposes T2I generation into three steps: object/count generation, layout generation, and image generation. Second, we introduce VPEval, an interpretable and explainable evaluation framework for T2I generation based on visual programming.
  arXiv  Detail & Related papers  (2023-05-24T16:42:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.