Zihan Liu

An algebraic proof of Colombo's difference-power determinant conjecture

Kun Li, Li Tie, Peng Wang, et al.

Effective Learning Rate Governs Loss Dynamics in Language Model Pretraining

Zihan Liu, Ruiheng Zheng, Shaobo Zhang, et al.

Kaleido: Algorithm-Hardware Co-Design for Video Diffusion Transformers by Exploiting Latent Space Correlations

Wenxuan Miao, Haosong Liu, Weiming Hu, et al.

Unified Audio Intelligence Without Regressing on Text Intelligence

Zhifeng Kong, Sang-gil Lee, Jaehyeon Kim, et al.

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Xing Ma, Yangjie Zhou, Wu Sun, et al.

A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations

Zihan Liu, Yizhen Wang, Rui Wang, et al.

Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation

Zhuolin Yang, Zihan Liu, Yang Chen, et al.

Nemotron-Cascade 2 is a compact 30-billion parameter AI model that achieves exceptional reasoning and problem-solving abilities comparable to much larger models, winning gold-medal-level performance on prestigious math and coding competitions. The researchers improved upon their previous model by expanding a training technique called Cascade RL across more domains and using a multi-domain distillation method that learns from specialized teacher models, allowing the smaller model to match the capabilities of much larger systems. The model represents a major breakthrough in creating efficient, intelligent AI systems with significantly fewer parameters than competitors.