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cs.LG2026
Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
Xueying Ding, Leman Akoglu
Training foundation models is computationally intensive and often slow to converge. We introduce PIQL,Privileged Information for Quick and Quality Learning, the first framework to…
cs.LG2026
VIP-COP: Context Optimization for Tabular Foundation Models
Yilong Chen, Xueying Ding, Leman Akoglu
Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new tabular tasks without task-specif…
cs.LG2026
Threshold Differential Attention for Sink-Free, Ultra-Sparse, and Non-Dispersive Language Modeling
Xingyue Huang, Xueying Ding, Mingxuan Ju +3
Softmax attention struggles with long contexts due to structural limitations: the strict sum-to-one constraint forces attention sinks on irrelevant tokens, and probability mass dis…