What is the impact of expert capacity imbalance on AnyExperts' performance degradation when evaluated on domai

Vision-language foundation models achieve promising performance in natural image classification, yet their direct application to medical imaging is limited by severe domain shifts, resolution mismatches, and the multi-label nature of clinical diagnosis. Training dedicated medical foundation models from scratch, however, is costly and data-intensive. Here, we propose MedBridge, a lightweight adaptation framework that opens a new direction in domain-gap mitigation by jointly combining domain alignment, resolution preservation, and multi-label reasoning via complementary VLM experts for medical i

Research goal: What is the impact of expert capacity imbalance on AnyExperts' performance degradation when evaluated on domain-shifted datasets such as migrating from COCO-based training to Conceptual Captions testing?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.