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cs.LG2026
From Objectives to What Models Learn: A Landau Theory of Invariant Learning
Pinli Wang, Yue He, Peng Cui
Invariant-learning objectives pursue similar goals yet produce qualitatively different regularization paths, leaving unclear when shortcuts can be suppressed without damaging stabl…
cs.LG2026
Closed-Form Spectral Regularization for Multi-Task Model Merging
Yongxian Wei, Runxi Cheng, Xingxuan Zhang +4
Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-developme…
cs.LG2026
LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
Yuanrui Wang, Xingxuan Zhang, Han Yu +7
Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar tokenization, each feature injec…