3 citations · 5 across the 6 of their papers we have counts for
9 papers
A Real-Time Privacy-Preserving Behavior Recognition System via Edge-Cloud Collaboration
Huan Song, Shuyu Tian, Junyi Hao +4
As intelligent sensing expands into high-privacy environments such as restrooms and changing rooms, the field faces a critical privacy-security paradox. Traditional RGB surveillanc…
The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models
Dakuan Lu, Jiaqi Zhang, Cheng Yuan +2
Recent advances in large language models (LLMs) have been largely driven by scaling laws for individual models, which predict performance improvements as model parameters and data…
Theoretical Foundations of Scaling Law in Familial Models
Huan Song, Qingfei Zhao, Ting Long +4
Neural scaling laws have become foundational for optimizing large language model (LLM) training, yet they typically assume a single dense model output. This limitation effectively…
ScRPO: From Errors to Insights
Lianrui Li, Dakuan Lu, Jiawei Shao +1
We introduce Self-correction Relative Policy Optimization (ScRPO), a novel reinforcement learning framework designed to empower large language models with advanced mathematical rea…
CAS-Spec: Cascade Adaptive Self-Speculative Decoding for On-the-Fly Lossless Inference Acceleration of LLMs
Zhiyuan Ning, Jiawei Shao, Ruge Xu +4
Speculative decoding has become a widely adopted as an effective technique for lossless inference acceleration when deploying large language models (LLMs). While on-the-fly self-sp…
Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
Ruanjun Li, Ziheng Liu, Yuanming Shi +3
Large language models (LLMs) deliver impressive generation quality, but incur very high inference cost because each output token is generated auto-regressively through all model la…