FORGE reveals an information spectrum encoded in RNA tertiary-structure geometry

Coarse RNA coordinate representations are increasingly used for inverse folding and structural annotation, but the biological information encoded in such representations is not well quantified. We introduce FORGE (Feature-engineered RNA Geometry Evaluation), a feature-engineering framework that extracts 935 interpretable descriptors from six backbone atoms and one glycosidic-anchor atom per residue. In a temporal test on 4,135 post-2025 PDB RNA chains, FORGE recovered 64.6% of native nucleotides; a six-atom control without the glycosidic nitrogen retained 58.5%, and abstaining from the least-confident half of calibration positions retained 94.4% accuracy. The same representation predicted base-pair state (79.2% accuracy), a RibonanzaNet-inferred DMS reactivity proxy ($R^2=0.329$) and protein-proximal context (AUC approximately 0.67). Native-decoy, OpenKnot and solved AI-designed pseudoknot tests further show that nucleotide identifiability, foldability and design score are distinct objectives. FORGE provides a reproducible audit layer for RNA structural interpretation.