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One Diffusion Model Speaks Protein, Structure and Language
Trion-1 uses one discrete token space for sequence, three-dimensional structure and natural language, then applies bidirectional diffusion across all three.
Summary
Trion-1 uses one discrete token space for sequence, three-dimensional structure and natural language, then applies bidirectional diffusion across all three.
Trion-1 represents amino-acid sequence, protein structure and natural language in a unified discrete vocabulary. Its diffusion-language-model backbone replaces strict left-to-right generation with bidirectional or semi-bidirectional interactions, supporting folding, inverse folding, understanding and design tasks in one system. The authors report broadly competitive benchmark results, including a folding TM-score above 0.8 from one sample under strict homology control. They also compare language understanding with named frontier systems. These are preprint benchmark claims, not experimental validation of designed proteins or a guarantee of biological function.
Why it matters
Trion-1 uses one discrete token space for sequence, three-dimensional structure and natural language, then applies bidirectional diffusion across all three.
Limits and context
- These are preprint benchmark claims, not experimental validation of designed proteins or a guarantee of biological function.
Key claims
Trion-1 uses one discrete token space for sequence, three-dimensional structure and natural language, then applies bidirectional diffusion across all three.
Qualification: These are preprint benchmark claims, not experimental validation of designed proteins or a guarantee of biological function.
Evidence: source-2026-10-24-013
Sources
- arXiv preprint 2610.31215, original v1arXiv · primary research
Corrections
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