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Standard code retrieval reliably lands agents in the right file but leaves them blind to the right function鈥攁 gap closed by turning flat snippet results into structured, navigable file scaffolds.
Minimizing distortion, not maximizing codebook utilization, is the key to achieving superior reconstruction fidelity in visual tokenization.
HA-DLC reveals that effectively modeling intra-tumoural heterogeneity can dramatically improve tumour classification accuracy from multiparametric MRI.
