arXiv AI

GeoReform: Reflective Formalization Evolution for Multimodal Geometry Problem Solving

GeoReform is a reflective formalization evolution framework designed to improve multimodal geometry problem solving by treating formalization as an optimizable policy rather than a fixed parser. It iteratively runs the reasoning pipeline, collects failures, diagnoses representation defects, and mutates the policy to better select, ground, group, and present geometric entities, relations, constraints, and targets. On the Geometry3K benchmark, GeoReform raises Qwen3VL-2B accuracy from 42.0% to 56.0%, demonstrating that effective formalization is crucial for enhancing multimodal geometry reasoning.

arXiv Computation and Language

Sep 10

From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning

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

llms multimodal benchmarks safety

arXiv AI

Jul 15

FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation

arXiv:2607. 12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remains largely underexplored, primarily due to the scarcity of annotated samples.

By Ruoran Xu, Wending Gao, Qiufeng Wang

llms multimodal

arXiv Computer Vision

6d ago

From Pixel to Coding: Evaluating the Figure Reproduction Capabilities of MLLMs

arXiv:2610.10066v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing...

By Zijian Chen, Zhengyu Chen, Bohan Liang, Lirong Deng, Yushuo Zheng, Yanwei Jiang, Qi Jia, Kaiwei Zhang, Wenjun Zhang, Guangtao Zhai

llms multimodal benchmarks

arXiv AI

Jun 4

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models

arXiv:2606. 04381v1 Announce Type: cross Abstract: Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space.

By Chen Chu, Bita Azarijoo, Li Xiong, Khurram Shafique, Cyrus Shahabi

llms multimodal benchmarks

arXiv AI

Aug 26

TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning

arXiv:2601.16520v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual recognition and semantic understanding, yet precise comp...

By Daixian Liu, Jiayi Kuang, Yinghui Li, Yangning Li, Di Yin, Haoyu Cao, Xing Sun, Ying Shen, Hai-Tao Zheng, Liang Lin, Philip S. Yu

llms multimodal benchmarks

Hugging Face Trending Papers

Jun 3

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models

Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space. Because LLMs operate on discrete tokens, they lack native support for continuous spatial representations, explicit geometric computation, and structured spatial operators.

llms multimodal benchmarks

arXiv AI

Aug 18

MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems

arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.

By Penghao Yin, Haomin Wang, Qihong Tang, Xiaoye Qu, Hongjie Zhang, Xiao-Ping Zhang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Sep 15

Spatial Reasoning via Modality Switching Between Language and Symbolic Representations

arXiv:2606.31285v3 Announce Type: replace Abstract: Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone. We often externalize our reasoning by ske...

By Shreya Rajpal, Tanawan Premsri, Parisa Kordjamshidi

llms multimodal

arXiv AI

Jul 1

Spatial Reasoning via Modality Switching Between Language and Symbolic Representation

arXiv:2606. 31285v1 Announce Type: new Abstract: Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone.

By Shreya Rajpal, Tanawan Premsri, Parisa Kordjamshidi

llms multimodal

arXiv AI

Jun 18

Hilbert-Geo: Solving Solid Geometric Problems by Neural-Symbolic Reasoning

arXiv:2605. 16385v3 Announce Type: replace-cross Abstract: Geometric problem solving, as a typical multimodal reasoning problem, has attracted much attention and made great progress recently, however most of works focus on plane geometry while usually fail in solid geometry due to 3D spatial diagrams and complex reasoning.

By Ruoran Xu, Haoyu Cheng, Bin Dong, Qiufeng Wang

llms multimodal benchmarks

arXiv AI

Sep 4

GraFT: A Training-Free Framework for Spatial Reasoning in Multimodal Large Language Models via 3D Scene Graphs

GraFT is a training‑free framework that enhances spatial reasoning in multimodal large language models by integrating a compact 3D scene graph (3DSG). It offers deterministic geometry via symbolic tools, allocentric layout through bird’s‑eye‑view rendering, and visual‑attribute grounding using egocentric frames. Experiments on ScanQA and VSI‑Bench show significant performance gains, with CIDEr increasing by 27% and improvements up to 65% over baseline models.

By Junqing Du, Fernando Ropero, Erkin Turkoz, Yanfeng Zhang, Lu Liu

llms fine-tuning multimodal

arXiv Computer Vision

Sep 21

DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning

arXiv:2609.21675v1 Announce Type: new Abstract: Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural...

By Wan Xu, Yuanfan Guo, Kevin Han, LaLa Chen, Wangmeng Zuo

llms reinforcement-learning multimodal benchmarks safety

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