1 citations · 1 across the 3 of their papers we have counts for
5 papers
PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud
Chenghua Wang, Daliang Xu, Dongqi Cai +24
Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Altho…
Photon: Federated LLM Pre-Training
Lorenzo Sani, Alex Iacob, Zeyu Cao +8
Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distr…
Beyond Scaling: Agents Are Heading to the Edge
Chunlin Tian, Dongqi Cai, Wanru Zhao +1
The bottleneck of useful agentic intelligence has shifted from compressing world knowledge into a single model to executing a coordinated system. This position paper argues that pe…
Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?
Zexi Li, Xiangzhu Wang, William F. Shen +5
Large language Model (LLM) unlearning, i.e., selectively removing information from LLMs, is vital for responsible model deployment. Differently, LLM knowledge editing aims to modif…
DEPT: Decoupled Embeddings for Pre-training Language Models
Alex Iacob, Lorenzo Sani, Meghdad Kurmanji +5
Language Model pre-training uses broad data mixtures to enhance performance across domains and languages. However, training on such heterogeneous text corpora requires extensive an…