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This work presents CellScientist, a protocol-constrained workflow that converts execution and validation feedback into auditable model-revision trajectories, and produces both a selected predictor and an inspectable record of its development.
SMArT enriches intrapatch semantics through self-supervised multiview fusion, and establishes an effective framework for TSF by integrating self-driven multiview learning with LLMs' reasoning power.
This survey focuses on four fundamental data-centric challenges: (1) Incompleteness, real-world graphs have missing nodes, edges, (2) Missing nodes, edges, and (3) Missing edges.
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