Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment

Pretrained biological language models encode rich probabilistic structure through native token distributions, but multimodal adaptation typically aligns hidden representations or replaces the native likelihood interface with task-specific heads. We introduce LogiCA (Logit-space Contrastive Alignment), whose central methodological change is to align the context-conditioned native token distributions themselves rather than pooled latent representations. Using native-head-preserving cross-modal adapters, LogiCA couples models with distinct vocabularies and tokenization schemes without requiring a shared tokenizer, decoder, or embedding space. Contrastive learning is performed directly through each model's original token-likelihood interface, retaining token-level scoring, interpretation, and generation. Across protein–ligand, TCR–peptide, and drug-resistance tasks, LogiCA outperforms matched latent-contrastive and conditional-MLM baselines, particularly in low-homology, mutation-local, and data-scarce regimes. These results support native-logit alignment as a general strategy for making pretrained biological token distributions context-sensitive without sacrificing their probabilistic semantics.