Can Control Learning with Bandit Optimization Accelerate Convergence in Multimodal Fusion of Medical Data?

Multimodal fusion has become a central paradigm in medical AI, integrating heterogeneous sources such as imaging and multi-omics for diverse downstream tasks. However, its training-time efficiency remains underexplored. As training progresses, modalities and tasks contribute unevenly to learning, yet prior methods often optimize the shared multi-task loss using fixed or heuristic open-loop weighting. This mismatch limits adaptation to evolving learning dynamics and can delay convergence to strong validation performance. To address this challenge, we propose Bandit with Control-Learning Aware optimizatioN (BCLAN), a novel parameter-free hybrid closed-loop controller that augments standard optimizers without architectural changes or additional trainable backbone parameters. Driven by validation feedback, BCLAN couples adversarial bandit exploration over a compact set of loss-weight templates with proportional–integral–derivative (PID)-based refinement in logit space, enabling it to search for improved task– modality allocations while stabilizing their adaptation under noisy, non-stationary feedback. We provide theoretical insights into this coupling, establishing bounded loss-weight refinement and identifying conditions for faster convergence. Across twelve heterogeneous-modality benchmarks in the medical domain, BCLAN improves downstream performance by up to ≈ 2.7% and reduces training time by up to ≈ 50.4% compared with the corresponding baselines, achieving a favorable training-time efficiency–performance trade-off.