Researchers introduce SciReasoner, a multimodal scientific foundation model designed for native structural reasoning across proteins, small molecules, and inorganic crystals. The model discretizes coordinates, topologies, and periodic connectivities into a unified structure-aware vocabulary, treating structural tokens as addressable evidence units during reasoning.
- In homology-controlled Gene Ontology prediction, SciReasoner increases F_max from 0.42 to 0.55 for Cellular Component annotation of low-homology proteins.
- It raises single-step retrosynthesis accuracy from 0.63 to 0.72 while generating fragment-level disconnection and precursor-verification traces.
- Its representations separate elemental and compound phases and resolve high- and low-band-gap regimes in materials science.
- SciReasoner achieves state-of-the-art performance on 67 out of 86 benchmarks.
- Double-blind expert evaluation rates its reasoning traces as preferred or comparable to a frontier large language model in 98% of cases.
By making structure an inspectable substrate for reasoning under scientific constraints, SciReasoner connects accurate prediction with interpretable scientific inference.