Lluna: Collaborative Unimodal Models for Missing-Modality Inference
Pretrained unimodal models offer a flexible route to multimodal prediction through collaboration, but this collaboration can become less accurate than a surviving unimodal model when modalities are missing. We study this setting using fixed surviving-model anchors and find that collaborative gains can weaken or reverse as inputs disappear, even when the anchor's own input is unchanged. Our diagnostics further reveal reduced recovery of modality-specific information after aggregation and state-dependent collaborative representations. We propose Lluna, which combines a direct private prediction path from adapted unimodal features with a common graph-based collaboration branch that provides a r
| Authors | Haoyue Bai, Yinyi Luo, Heqing Huang et al. |
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| Published | 14 October 2026 |
| Last updated at source | — |
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