6 citations · 22 across the 17 of their papers we have counts for
6 papers · 1 filter
DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design
Xinling Yu, Yixing Li, Ziyue Liu +4
Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve. Operator-learning surrogates replace this solve with a fas…
ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload
Ziyue Liu, Zhengyang Wang, Ruijie Zhang +7
Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient training systems. Yet existing framewor…
When Emotion Becomes Trigger: Emotion-style dynamic Backdoor Attack Parasitising Large Language Models
Ziyu Liu, Tao Li, Tao Yang +4
Data-poisoning backdoors pose a practical threat to the fine-tuning of large language models (LLMs). Most existing attacks bind an attacker-selected behavior to fixed tokens, phras…
MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization
Yupeng Su, Ruijie Zhang, Ziyue Liu +2
The Muon optimizer has emerged as a compelling alternative to Adam for training large language models, achieving remarkable computational savings through gradient orthogonalization…
TEON: Tensorized Orthonormalization Beyond Layer-Wise Muon for Large Language Model Pre-Training
Ruijie Zhang, Yequan Zhao, Ziyue Liu +5
The Muon optimizer has demonstrated strong empirical performance in pre-training large language models by performing matrix-level gradient (or momentum) orthogonalization in each l…
MUON+: Towards More Effective Muon via One Additional Normalization Step for LLM Pre-training
Ruijie Zhang, Yequan Zhao, Ziyue Liu +4
Muon has recently emerged as a strong optimizer for large language model pre-training, orthogonalizing the momentum matrix via Newton--Schulz polar iterations. A natural intuition…