Pith. sign in

REVIEW 5 cited by

Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.17885 v4 pith:FLX6QP2Q submitted 2024-10-23 cs.AI cs.CV

Linger Deng , Linghao Zhu , Yuliang Liu , Yu Wang , Qunyi Xie , Jingjing Wu , Gang Zhang , Yingying Zhu
show 1 more author
Xiang Bai
This is my paper · ORCID
classification cs.AIcs.CV
keywords geometricreasoningmodelsreversechain-of-thoughtdatafaceproperties
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large Multimodal Models (LMMs) face limitations in geometric reasoning due to insufficient Chain of Thought (CoT) image-text training data. While existing approaches leverage template-based or LLM-assisted methods for geometric CoT data creation, they often face challenges in achieving both diversity and precision. To bridge this gap, we introduce a two-stage Theorem-Validated Reverse Chain-of-Thought Reasoning Synthesis (TR-CoT) framework. The first stage, TR-Engine, synthesizes theorem-grounded geometric diagrams with structured descriptions and properties. The second stage, TR-Reasoner, employs reverse reasoning to iteratively refine question-answer pairs by cross-validating geometric properties and description fragments. Our approach expands theorem-type coverage, corrects long-standing misunderstandings, and enhances geometric reasoning. Fine-grained CoT improves theorem understanding and increases logical consistency by 24.5%. Our best models surpass the baselines in MathVista and GeoQA by 10.1% and 4.7%, outperforming advanced closed-source models like GPT-4o.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SyncLoop jointly evolves multimodal training data and model capability through alternating SFT and RL, selecting error-prone samples to improve geometry reasoning.

  2. SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis

    cs.LG 2025-06 conditional novelty 6.0 of 10

    SynthRL synthesizes harder, answer-preserving visual math questions from easy seed questions and reports small but mixed out-of-domain RLVR gains for Qwen2.5-VL-7B.

  3. MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning

    cs.CV 2025-06 conditional novelty 5.0 of 10

    MINT-CoT-7B interleaves fine-grained visual tokens into each math reasoning step and reports 73.70 on MathVista-Math, 64.72 on GeoQA, and 69.6 on MMStar-Math.

  4. A Survey of Deep Learning for Geometry Problem Solving

    cs.CL 2025-07 conditional novelty 4.0 of 10

    This survey organizes deep learning work on geometry problem solving into task, method, benchmark, and evaluation categories, and highlights open challenges.

  5. Towards Geometry Problem Solving in the Large Model Era: A Survey

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A survey that organizes geometry problem-solving research into benchmark construction, parsing, and reasoning, and proposes a unified parse-then-reason paradigm for the large-model era.

Pith tools