Trion-1: Unifying Language, Protein Sequence, and Structure with Diffusion Language Models
Trion-1 is presented as the first native unified diffusion language model for proteins, using one discrete token space for sequence, structure and natural language over a bidirectional dLLM backbone. It covers folding, inverse folding, sequence and structure understanding and design while keeping general LLM language ability; under strict homology control it reaches a TM-score above 0.8 from a single sample, matching a tri-modal baseline that needs 200 samples.
Why it matters
Moves protein foundation models off left-to-right decoding to a single generative model of sequence, structure and text.
Importance: 3/5
New model class for protein modelling with a strong single-sample result.