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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…
cs.LG2026
What Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Study
Keyu Lv, Manyi Zhang, Xiaobo Xia +6
Reasoning models excel at complex tasks such as coding and mathematics, yet their inference is often slow and token-inefficient. To improve the inference efficiency, post-training…