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New benchmarks assess MLLMs' geometric reasoning and visual perception

By PulseAugur Editorial · [3 sources] ·

Researchers have developed new benchmarks to evaluate the geometric reasoning capabilities of Multimodal Large Language Models (MLLMs). The CapGeo-Bench, proposed in one study, uses high-quality figure-caption pairs and a fine-grained keypoint-based metric to assess perception and reasoning, finding that captions significantly boost performance, indicating a visual perception bottleneck. A separate study introduced ChitraMiti-12.8k and NCTB-500 for Bengali geometric reasoning, revealing that structured descriptions can serve as a proxy for diagram evaluation, though models struggle with cross-modal verification. AI

IMPACT These benchmarks aim to improve MLLMs' ability to understand and reason about geometric information, potentially leading to more capable AI systems in fields requiring spatial understanding.

RANK_REASON Two research papers introduce new benchmarks for evaluating multimodal large language models on geometric reasoning tasks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New benchmarks assess MLLMs' geometric reasoning and visual perception

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Raj Jaiswal, Sree Krishna Uppalapati, Dhruvkumar Patel, Ria Khatoniar, Tanuja Ganu, Rajiv Ratn Shah ·

    Lost in Perception: Isolating Perceptual and Reasoning Failures in Multimodal Physics and Geometry Reasoning

    arXiv:2609.18991v1 Announce Type: new Abstract: Multimodal LLMs report strong performance on scientific reasoning benchmarks, yet most treat perception and reasoning as a single measurable process. We introduce a five-task diagnostic experiment across physics and geometry benchma…

  2. arXiv cs.AI TIER_1 English(EN) · Yuying Li, Siyi Qian, Hao Liang, Leqi Zheng, Ruichuan An, Linzhuang Sun, Jiajun Zhang, Wentao Zhang ·

    CapGeo-Bench: Decoupling Visual Perception from Reasoning and Evaluating Geometric Understanding

    arXiv:2510.09302v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, even advanced closed-source models such as GPT-o3 still struggle with geometric prob…

  3. arXiv cs.CV TIER_1 English(EN) · Khan Raiyan Ibne Reza, Sanjana Aktar Maria, Sumaiya Tabassum Nimi, Md Adnan Arefeen ·

    ChitraMiti: Benchmarking Visual Grounding and Modality Reliance in Bengali Geometric Reasoning

    arXiv:2609.12509v1 Announce Type: new Abstract: Evaluation of vision-language models (VLMs) for multimodal mathematical reasoning remains limited for low-resource languages and for geometry problems that require reading a diagram and a question together. We introduce ChitraMiti-1…

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