Lorin A. Crawford

Multimodal cell foundation models usually freeze each modality's encoder and train only a fusion interface, yet whether fusion adds information beyond the best single modality has lacked a test. Researchers introduce the Synergistic Information Score (SIS), which measures accessible synergy as the gain of a fused representation over the strongest unimodal one under a linear probe. Across lung, thymus and breast spatial transcriptomics, unimodal baselines suffice in redundancy-dominated tasks, while non-spectral fusion reaches higher SIS in the resolution-mismatched thymus task.

Multimodal cell foundation models usually freeze each modality's encoder and train only a fusion interface, yet whether fusion adds information beyond the best single modality has lacked a test. Researchers introduce the Synergistic Information Score (SIS), which measures accessible synergy as the gain of a fused representation over the strongest unimodal one under a linear probe. Across lung, thymus and breast spatial transcriptomics, unimodal baselines suffice in redundancy-dominated tasks, while non-spectral fusion reaches higher SIS in the resolution-mismatched thymus task.