Generalist agents reach published data-selection baselines but require scaffolds forcing method adaptation to autonomously compose a policy that outperforms baselines at one-tenth the data budget.
Adadedup: Adaptive hybrid data pruning for efficient large-scale object detection training
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
citing papers explorer
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Can Generalist Agents Automate Data Curation?
Generalist agents reach published data-selection baselines but require scaffolds forcing method adaptation to autonomously compose a policy that outperforms baselines at one-tenth the data budget.
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Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.