Autoregressive sequential recommendation trains transformer models on tokenized user behavior via cross-entropy. The Muon optimizer projects gradient matrices onto the Stiefel manifold through Newton-Schulz (NS) orthogonalization, ensuring uniform utilization of all parameter directions. However, we identify that this...
Jin-Xin Hu, Hao Deng, Hai-Bo Xing et al.· Proceedings of the 20th ACM...· 0 citations
This work proposes Astar, a training-based approach that learns a specialized evolution-guiding model from the abundant iteration histories of industrial systems, and proposes a pipeline that turns noisy historical commits into a large, clean evolutionary corpus via pairwise sample expansion and noise filtering.
Jin-Xin Hu, Hao Deng, Hai-Bo Xing et al.· 2 citations
CMRec is proposed, a cross-country GR framework that injects cross-country supervision at the data level via dual-constrained, context-aware code-mixing that substantially improves recommendation quality in data-sparse countries while preserving performance in data-rich countries.
Yuan Gao, Hao Deng, Hai-Bo Xing et al.· 0 citations
SAYRE is presented, a scene-aware document synthesis framework for generating scalable KIE training data without hand-crafted template design, and error analysis shows that synthesized training reduces field-level errors by improving schema-aware extraction over dense tables, business identifiers, and contract clauses.
Zhipeng Xu, Zulong Chen, Qing Liu et al.· 0 citations
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