<p>Recent advances in Machine Learning have transformed antibody development through in silico models, accelerating therapeutic candidate identification. However, challenges persist: rapid adaptation of property predictors to laboratory-specific assays with incomplete datasets; batch effects introducing systematic bias; assay costs necessitating efficient unseen property prediction. We introduce a novel multimodal architecture featuring specialized tokenization and embedding projection that integrates text and protein language models (pLM) and a learning strategy to enable context-conditioned multi-property prediction without learning shortcuts. Our framework enables prompting without dictionary merging across modalities, creating a compact model capable of context-conditioned learning for multi-property prediction. The orchestrating model avoids pLM-to-text projection while enabling inference-time adaptation without retraining. Using 876,898 antibody heavy chain sequences with batch effect simulation, our architecture achieved Spearman’s ρ &gt; 0.8 across multiple developability properties, significantly outperforming fine-tuned multimodal LLMs and showed the ability to leverage correlation between properties for prediction. This approach has the potential to address critical antibody development challenges.</p>