DRA: State-Conditioned Evidence Allocation for Missing-Modality Fine-Tuning

When a modality disappears, the value of evidence still available but unread can change. We study state-conditioned evidence allocation: using observed modality availability to direct a limited budget of additional text encoding. A Bayes-risk characterization relates this choice to information substitutes and complements:the preferred state can reverse with the loss, and at an interior budget, state-blind allocation is optimal only at equal value per cost. DRA (Decide, Read, Adapt)implements the allocation with a state-gated residual reader that reuses an adapted CLIP text tower; we retain shared adaptation and validation thresholds as interfaces. On MM-IMDb, same-movie image removal with symmetric reader training gives a positive log-loss interaction of 0.00839 nats per label (movie-cluster95% CI [0.00767, 0.00905]). With the reader and extra-window budget fixed, targeting image-missing rows improves macro-F1 by +0.18 and AP by +0.33 over state-blind allocation. In retrained controls, DRA comes within 0.19 F1 of all-row reading using 53% of the extra windows. Under published evaluation rules across MM-IMDb, UPMC-Food101 and Hateful Memes, DRA exceeds reported MoRA in 23 of 27 benchmark settings