In a new paper, a team of Apple researchers details a creative framework that improves LLM answers in math reasoning, code generation, and more. Here are the details. Diffusion and autoregression, united In a newly-revised study titled LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning , Apple researchers, alongside researchers from the University of California, San Diego, detail an interesting way to improve the quality of answers generated by large language models (LLMs) in certain domains. In the past, we’ve discussed diffusion models , which generate text by iterating over many tokens in parallel with each pass, in contrast to autoregressive models, which work by calculating and predicting tokens one by one. Apple has even looked at diffusion models applied to protein folding prediction and coding , which is endlessly interesting. What LaDiR does, in a nutshell, is combine both approaches: it adopts diffusion during the reasoning process, and then generates the final output autoregressively.…