LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning

Researchers introduce LaDiR (Latent Diffusion Reasoner), a novel framework that combines continuous latent representation with iterative refinement capabilities to enhance Large Language Models' reasoning abilities. The system uses a Variational Autoencoder to encode reasoning steps and a latent diffusion model for parallel generation of diverse reasoning trajectories, showing improved accuracy and interpretability in mathematical reasoning benchmarks.