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cs.LG2026
Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss
Ruixin Guo, Xinyu Li, Hao Zhou +2
Linear autoencoders (LAEs) have gained increasing popularity in recommender systems due to their simplicity and strong empirical performance. Most LAE models, including the Emphasi…
cs.LG2025
Can Large Language Models Understand Intermediate Representations in Compilers?
Hailong Jiang, Jianfeng Zhu, Yao Wan +4
Intermediate Representations (IRs) play a critical role in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. In t…