SCARV improves global and local stability of sample rankings in redundant NLP datasets by layering robust multi-seed aggregation with structure-aware allocation over redundancy clusters.
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Machine learning research should prioritize ideas by testing their predicted behavioral signatures in modern models through custom experiments instead of leaderboard chasing or abstract theorems.
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SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets
SCARV improves global and local stability of sample rankings in redundant NLP datasets by layering robust multi-seed aggregation with structure-aware allocation over redundancy clusters.
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Position: Ideas Should be the Center of Machine Learning Research
Machine learning research should prioritize ideas by testing their predicted behavioral signatures in modern models through custom experiments instead of leaderboard chasing or abstract theorems.