Collaboration can backfire when modalities vanish: Lluna keeps a direct per-model prediction and gains 4.26% on average across eight datasets
Collaboration among pretrained unimodal models can become less accurate than a surviving unimodal model when modalities are missing. Lluna keeps each model's direct prediction as a private path, adds a graph-based common branch as a residual correction, and lets richer availability states guide poorer ones along single-modality removal chains. Across eight datasets in affective computing, scene understanding and medical prediction, it improves Mean-Missing performance by 4.26% on average over the strongest evaluated baseline, with positive collaboration gains at minimal inputs on all eight tasks.