16 citations · 73 across the 29 of their papers we have counts for
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cs.LG2026
EGiME: Entropy-Guided Model Evaluation in Few-Shot Learning
Dequan Jin, Tao Li, Lifeng Xing +2
The feature-extraction quality of pre-trained models sets the ceiling of downstream transfer learning, yet few-shot model selection remains difficult: supervised evaluation on scar…
cs.AI2026
Why Do Large Language Models Generate Harmful Content?
Rajesh Ganguli, Raha Moraffah
Large Language Models (LLMs) have been shown to generate harmful content. However, the underlying causes of such behavior remain under explored. We propose a causal mediation analy…
cs.AI2026
DAGverse: Building Document-Grounded Semantic DAGs from Scientific Papers
Shu Wan, Saketh Vishnubhatla, Iskander Kushbay +4
Directed Acyclic Graphs (DAGs) are widely used to represent structured knowledge in scientific and technical domains. However, datasets for real-world DAGs remain scarce because co…