Challenge: Existing methods for solving geometry math problems struggle with accurately interpreting geometry diagrams, posing a challenge for problem-solving.
Approach: They propose a model that extracts geometric relations from diagrams and converts them into natural language descriptions.
Outcome: The proposed model outperforms the previous best method on the UniGeo dataset by 12.7% and 42.1% in calculation and proving subsets.

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Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)

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Challenge: Existing methods for solving geometric problems are either small in scale or not publicly available.
Approach: They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning.
Outcome: The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step.
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving (2024.findings-acl)

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Challenge: Recent advances in large language models (LLMs) and multi-modal models (MMs) have demonstrated remarkable capabilities in problem-solving, but their proficiency in tackling geometry math problems has not been thoroughly evaluated.
Approach: They propose a benchmark to evaluate the performance of large language models and multi-modal models in solving geometry math problems.
Outcome: The proposed model achieves 55.67% accuracy on main subset but only 6.00% accuracy on hard subset.
Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models (2024.findings-emnlp)

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Challenge: Recent advances in Large Language Models (LLMs) demonstrate increasing proficiency in complex mathematical and algorithmic tasks, yet their geometric reasoning skills are underexplored.
Approach: They propose a framework that enhances LLMs’ reasoning potential through a multi-agent system conducting internal dialogue.
Outcome: The proposed framework enhances LLMs’ reasoning potential through a multi-agent system conducting internal dialogue.
Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)

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Challenge: Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks.
Approach: They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations.
Outcome: The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks.
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models (2025.findings-naacl)

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Challenge: Various vision-language models (VLMs) have made significant progress in multimodal tasks, but they still struggle with geometry problems.
Approach: They propose a vision-language model that leverages modular code-finetuning to generate and execute code using a predefined geometry function library.
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GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation (2024.emnlp-main)

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Challenge: Existing datasets are too challenging for direct model learning or suffer from misalignment between text and images.
Approach: They propose a pipeline that leverages GPT-4 and GPT4V to generate geometry problems with aligned text and images, facilitating model learning.
Outcome: The proposed pipeline generates 4.9K geometry problems with aligned text and images, facilitating model learning.
A Survey of Deep Learning for Geometry Problem Solving (2026.acl-long)

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Challenge: Recent surge in deep learning technologies has significantly accelerated research in this area.
Approach: They propose a comprehensive summary of the relevant tasks in geometry problem solving and a review of related deep learning methods.
Outcome: The proposed method is based on a systematic review of related methods and evaluation metrics and methods.
When Does Auxiliary Modality Matter in Solving Geometric Problems? A Comprehensive Study of Textual, Formal, and Visual Modalities (2026.eacl-short)

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Challenge: Large Language Models (LLMs) face challenges in integrating linguistic and spatial reasoning, which limits their performance on geometry problems.
Approach: They compare four auxiliary modalities on open- and closed-source multimodal LLMs . they show that DES boosts the accuracy of open-source LLM models .
Outcome: The proposed modalities improve performance on open- and closed-source LLMs.
Are NLP Models really able to Solve Simple Math Word Problems? (2021.naacl-main)

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Challenge: Existing solvers for math word problems often achieve high performance on benchmark datasets . existing models rely on shallow heuristics to achieve high accuracy .
Approach: They restrict their attention to English MWPs taught in grades four and lower . they propose a challenge dataset to test the accuracy of MWp solvers .
Outcome: The proposed model can solve a large fraction of MWPs even with shallow heuristics . the proposed model is much lower on the challenge dataset SVAMP .
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding (2022.coling-1)

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Challenge: Existing methods for solving geometric problems are limited due to lack of high-quality datasets and efficient neural solvers.
Approach: They propose to annotate 2,518 geometric problems with richer types and greater difficulty using a benchmark dataset.
Outcome: The proposed method improves the accuracy of automatic geometric problem solving to 66.09%.

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