DiffMath: Symbol- and Graph-Aware Latent Diffusion Transformer for Handwritten Mathematical Expression Generation

arXiv:2606.19939 · cs.CV · Submitted 2026-06-18 · Read on arXiv

cs.CV

Submitted: 2026-06-18

Updated: 2026-09-27

Code: https://github.com/awei669/DiffMath

Terminology

• Latent Space — 24× in this paper · explained in 7 episodes
• Transformer — 24× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Fidelity — 11× in this paper · explained in 2 episodes
• Latent Representation — 11× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Regularization — 8× in this paper · explained in 3 episodes
• Latent Representations — 7× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Handwritten Mathematical Expression Generation — 6× in this paper
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Diffusion Process — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• alpha — 4× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Gaussian Mixture Model — 3× in this paper
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• AdamW Optimizer — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Decoupling — 2× in this paper · explained in 2 episodes
• International Conference on Frontiers in Handwriting Recognition — 2× in this paper
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Character Detection Matching — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• International Conference on Document Analysis and Recognition — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• Generating Sequences With Recurrent Neural Networks
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Qwen3 Technical Report
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
• Qwen-Image Technical Report

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