# SciDC

Large language models still fabricate reasoning that contradicts domain rules in specialized scientific tasks. SciDC has a general model compile natural-language scientific knowledge into three layers of rules, top-level reasoning steps, middle-level conditional checks and bottom-level token masks, which a locally deployed domain model enforces during decoding. Across industrial formulation design, thyroid cancer TNM staging and single-step retrosynthesis, average accuracy rises 12% over vanilla generation, and ablations show removing the top or bottom layer sharply lowers formulation accuracy.

Large language models still fabricate reasoning that contradicts domain rules in specialized scientific tasks. SciDC has a general model compile natural-language scientific knowledge into three layers of rules, top-level reasoning steps, middle-level conditional checks and bottom-level token masks, which a locally deployed domain model enforces during decoding. Across industrial formulation design, thyroid cancer TNM staging and single-step retrosynthesis, average accuracy rises 12% over vanilla generation, and ablations show removing the top or bottom layer sharply lowers formulation accuracy.