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OPERA proposes a multi-agent ensemble framework that treats expert weight assignment as an offline policy learning problem for universal biomedical image analysis, enabling test-time adaptation without retraining and consistently improving performance across 9 datasets and 30+ baselines.
Group Prompting introduces a training-free framework for cell instance segmentation that requires only one click per cell type, using the Segment Anything Model's feature space to recursively expand prompts, achieving competitive performance without training.