Nahual: A Sequence Model for Language and Atoms

Generative models play a rapidly growing role in the design and simulation of molecules. The most widely used applications of generative models are in large language models (LLMs), owing to the natural flexibility of language for specifying and answering queries. However, generalist LLMs do not effectively handle large sets of 3D coordinates, whereas specialist generative models of 3D molecules cannot reach the controllability afforded by language. To bridge these capabilities, we propose Nahual, a decoder-only autoregressive diffusion model for any sequence of text and 3D molecules. To enable autoregression on continuous coordinates to scale to long sequences, we propose next-token diffusion, whereby a standard causal transformer backbone is grounded in the clean current sequence while denoising the next sequence. We train Nahual on a curated set of twelve 3D chemistry tasks ranging from microsolvation to molecular conformer search to adsorption on metal surfaces, with associated evaluation metrics and baselines. The result is a single set of end-to-end-trained weights that simultaneously performs all tasks and surpasses state-of-the-art models specialized for crystal structure prediction and property-conditional molecule generation. Our results demonstrate the viability of scaling generalist decoder-only models for native multimodal understanding and generation across language and chemistry.