Embedding Inversion via Conditional Masked Diffusion: recover original text from embedding vectors using parallel denoising. Live demo + training pipeline + technical report.

Pytorch Implementation of MD4: Simplified and Generalized Masked Diffusion for Discrete Data

The simplest masked-diffusion language model you can actually train, debug, and learn from — ~1100 lines of plain PyTorch, char-level, with an honest head-to-head against a matched autoregressive baseline. Watch text crystallize from noise.

🛠 Reconstruct original text from text embeddings using conditional masked diffusion to reveal reversible embedding representations efficiently and accurately

Minimal masked diffusion LM in PyTorch. Pretraining + SFT + a scaling family from 50M to 350M, LLaDA recipe.

DiffLM Lab — Interactive Diffusion Language Model Laboratory. 6 modules: denoising playground, forward absorbing process, sampling strategies, AR-vs-diffusion race, block diffusion, masked-diffusion ELBO. Real in-browser denoiser.

Empirical study of logit-level guidance on LLaDA-8B masked diffusion model. Extends Diffusion-LM (Li & Liang 2022) classifier guidance to 8B scale with gradient-free energy vectors. 13 experiments, +39% semantic steering.

Steer masked diffusion LLMs toward a topic without mentioning it in the prompt — energy fields injected at each denoising step

PyTorch implementation of "Train for the Worst, Plan for the Best." Investigating adaptive token ordering in Masked Diffusion Models (MDMs) to sidestep hard subproblems and elicit reasoning in discrete domains.