SMART: Spectral Multimodal Adaptation via Routed Test-Time Tuning for Zero-Shot Molecular Elucidation

SMART adapts pretrained molecular generators to experimental spectra without target structural labels. Under heterogeneous acquisition shifts, supplying all spectra can reduce accuracy below that of a single informative view. We address this negative transfer by separating the supervision-source view from the Full inference view. A source-trained structural-utility router selects a frozen teacher; encoder InfoNCE preserves molecular correspondence, while shared-prefix decoder Jensen–Shannon alignment transfers conditional structural preferences. Persistent, formula-compatible predictions then support cross-view refinement, with a dynamic controller selecting checkpoints according to prediction drift and feasibility. End-to-end hybrid adaptation reaches 55.66% Top-1 on 3,669 SDBS molecules, 4.96 percentage points above the strongest zero-shot single-spectrum reference. Objective ablations and Chemotion evaluation examine complementary learning signals and stronger acquisition shift. These results show how selective supervision can recover the value of complete spectral observations.