DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI

DataFlow is an LLM-driven framework providing modular data preparation pipelines and automated operator synthesis that improves downstream LLM performance over human-curated and synthetic baselines.

Liang, Hao, Ma, Xiaochen, Liu, Zhou, Wong, Zhen Hao and 31 more

Published Dec 18, 2025 · 0 citations · ▲ 225 on Hugging Face · Code ★ 8,195

GradTrack: Detecting Noisy Labels via Temporal Trajectories of Class-wise Gradient Misalignment

Townim Faisal Chowdhury, Ta Duc Huy, Hieu Phan, Anton van den Hengel and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Subdata Selection: A Unified Framework for Optimal Selection and Statistical Efficiency Assessment

Min Yang, Wei Zheng, John Stufken, Ming-Chung Chang and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Guiding Data Allocation for Robust Subpopulation Generalization

Zhaoying Pan, Yipei Wang, Shenyu Lu, Xiaoqian Wang

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Readiness-Aware Sample Selection for Noisy Labels with Class Imbalance

Chihyeon Choi, Sangho Lee, Jiho Hong, Youngdoo Son and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

PatchScout: Thematic Web Data Collection via Information Foraging

Michael West, Eduard Dragut

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

FineVision: Open Data Is All You Need

FineVision unifies 24 million vision-language samples via rigorous curation and decontamination, and models trained on it outperform existing open mixtures across broad evaluations.

Luis Wiedmann, Orr Zohar, Amir Mahla, Xiaohan Wang and 5 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026 · ▲ 81 on Hugging Face

Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise

Exploiting data symmetries improves k-NN accuracy for selecting low-noise training subsets, yielding near-optimal performance despite high-dimensional label noise.

Kumar Shubham, Pavan Karjol, Kiran M K, Prathosh AP

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026