arXiv:2608. 08964v1 Announce Type: new Abstract: The generation of mathematically precise diagrams from tex- tual prompts has emerged as a critical yet underexplored capability of Large Language Models (LLMs).
By Harish Kashyap, Kiran Byadarhaly, Sriram Chakaravarthy, Sanyukta Tuti, Aryan Mistry
arXiv:2606. 04244v1 Announce Type: new Abstract: Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then reason over the tool's output, specifically when they rely on visual aids.
By Amirhossein Dabiriaghdam, Shayan Vassef, Mohammadreza Bakhtiari, Yasamin Medghalchi, Ilker Hacihaliloglu, Mesrob Ohannessian, Lele Wang, Giuseppe Carenini
The paper introduces a framework that combines a Geometric Vision Parser and a Symbolic Solver to enable a Large Language Model to solve complex plane geometry problems. By translating diagrams into symbolic representations and performing formal deductions, the approach reduces hallucinations and produces interpretable, human-like solutions. Experiments on a new benchmark from 2025 Chinese Zhongkao exams show performance comparable to Gemini 2.5 Pro.
By Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou, Yan Cao, Xin Shen, Dongcai Lu, Yi Zhou
The paper introduces a new benchmark for diagrammatic reasoning in olympiad geometry, comprising 954 self‑contained problems and a 297‑problem hard subset. Each problem is paired with a human‑authored, high‑fidelity diagram in Asymptote code and a suite of metrics for evaluating diagram construction. Experiments show that current foundation models excel at solving the problems but produce markedly less faithful diagrams, with an average compile success rate of only 36.14%.
By Hsien Xin Peng, Anthony Kim, Alvin Li, Calvin Supasanya, Shivank Garg, Kevin Zhu
arXiv:2610.19571v1 Announce Type: new
Abstract: Real-world engineering drawings are often large-format and information-dense. Answering practical engineering questions therefore requires both fine-gr...
By Ke Li, Tianyi Wang, Enning You, Chaoqun Wan, Zhen Huang, Shuxian Liang, Xian-Sheng Hua
arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.
By Jana Zeller, Thadd\"aus Wiedemer, Fanfei Li, Thomas Klein, Prasanna Mayilvahanan, Matthias Bethge, Felix Wichmann, Ryan Cotterell, Wieland Brendel