mlsanityData to 13 Oct 2026

LANS: A Layout-Aware Neural Solver for Plane Geometry Problem

arXiv 2311.16476 ↗First posted 25 Nov 2023cs.CV cs.AIcited by 24

On the map: LLM reasoning → Spatial and embodied reasoning

Abstract (arXiv)

Geometry problem solving (GPS) is a challenging mathematical reasoning task requiring multi-modal understanding, fusion, and reasoning. Existing neural solvers take GPS as a vision-language task but are short in the representation of geometry diagrams that carry rich and complex layout information. In this paper, we propose a layout-aware neural solver named LANS, integrated with two new modules: multimodal layout-aware pre-trained language module (MLA-PLM) and layout-aware fusion attention (LA-FA). MLA-PLM adopts structural-semantic pre-training (SSP) to implement global relationship modeling, and point-match pre-training (PMP) to achieve alignment between visual points and textual points. LA-FA employs a layout-aware attention mask to realize point-guided cross-modal fusion for further boosting layout awareness of LANS. Extensive experiments on datasets Geometry3K and PGPS9K validate the effectiveness of the layout-aware modules and superior problem-solving performance of our LANS solver, over existing symbolic and neural solvers. The code will be made public available soon.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 33 of its 54 references that are in the map. First posted on arXiv 25 Nov 2023.

Builds on

33 of 54 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram22 Feb
  2. 2022UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression6 Dec

Other references · 31

  1. 2017Attention Is All You Needcited by 60,106
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,492
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,730
  7. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,314
  8. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,896
  9. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,595
  10. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,450
  11. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  12. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,227
19 more
  1. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 551
  2. 2019Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-trainingcited by 451
  3. 2021Unifying Vision-and-Language Tasks via Text Generationcited by 411
  4. 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 406
  5. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 405
  6. 2021Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoningcited by 378
  7. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 347
  8. 2019Deep Modular Co-Attention Networks for Visual Question Answeringcited by 338
  9. 2021IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoningcited by 264
  10. 2021DocFormer: End-to-End Transformer for Document Understandingcited by 254
  11. 2021GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoningcited by 225
  12. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  13. 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 122
  14. 2021BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documentscited by 104
  15. 2021LayoutReader: Pre-training of Text and Layout for Reading Order Detectioncited by 79
  16. 2021Document Collection Visual Question Answeringcited by 45
  17. 2022MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answeringcited by 36
  18. 2022Plane Geometry Diagram Parsingcited by 20
  19. 2023A Symbolic Character-Aware Model for Solving Geometry Problemscited by 20

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 9; 2025: 7; 2026: 8.

24 papers.

Sort:

By area:

  1. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320
  2. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 278
  3. 2024Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoningcited by 58
  4. 2024A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challengescited by 55
  5. 2024GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solvingcited by 45
  6. 2024"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreakcited by 25
  7. 2024GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-trainingcited by 23
  8. 2024SLANG: New Concept Comprehension of Large Language Modelscited by 20
  9. 2025Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integrationcited by 18
  10. 2024HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Routercited by 16
  11. 2025a1: Steep Test-time Scaling Law via Environment Augmented Generationcited by 13
  12. 2025Pi-GPS: Enhancing Geometry Problem Solving by Unleashing the Power of Diagrammatic Informationcited by 10
12 more
  1. 2024FGeo-HyperGNet: Geometric Problem Solving Integrating FormalGeo Symbolic System and Hypergraph Neural Networkcited by 8
  2. 2025GeoDANO: Geometric VLM with Domain Agnostic Vision Encodercited by 6
  3. 2025Large Language Models as Computable Approximations to Solomonoff Inductioncited by 6
  4. 2025A Survey of Deep Learning for Geometry Problem Solvingcited by 4
  5. 2026GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solvingcited by 1
  6. 2026Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement Learningcited by 1
  7. 2026Non-Parametric Structural Priors for Geometry Theorem Predictioncited by 1
  8. 2026BiNSGPS: Geometry Problem Solving via Bidirectional Neuro-Symbolic Interactioncited by 0
  9. 2026Verifiable Geometry Problem Solving: Solver-Driven Autoformalization and Theorem Proposingcited by 0
  10. 2026Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometrycited by 0
  11. 2026From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoningcited by 0
  12. 2026Dense Neuro-Symbolic Reasoning in a Unified Geometry Statecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram
  2. 2025Towards Geometry Problem Solving in the Large Model Era: A Survey
  3. 2025Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey
  4. 2025AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning
  5. 2024GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models
  6. 2026Hilbert-Geo: Solving Solid Geometric Problems by Neural-Symbolic Reasoning
  7. 2026Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving
  8. 2025GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines