11 citations · 25 across the 30 of their papers we have counts for
11 papers · 1 filter
Training and Scaling Compute-Optimal Physiological Waveform Foundation Models
Pingzhi Li, Jie Peng, Shuqing Luo +2
We investigate the scaling laws and compute-optimal training of physiological waveform foundation models (FMs). We train Aether, a family of over one hundred FMs ranging from 20M t…
Test-Time Unlearning via Sparse Autoencoder
Pingzhi Li, Jinhao Duan, Vaishnav Tadiparthi +6
Machine unlearning aims to remove specific knowledge from a trained large language model (LLM) without retraining from scratch. Existing methods modify model weights via gradient a…
B-PWL: GPU-Batched Branch-and-Bound for Piecewise-Linear Optimization with SOS2 Constraints
Yilin Guan, Shuqing Luo, Pingzhi Li +2
Piecewise-linear (PWL) optimization problems arise in many mixed-integer programming (MIP) optimization applications, including portfolio optimization, workforce scheduling, and re…
Vision Language Model Helps Private Information De-Identification in Vision Data
Tiejin Chen, Pingzhi Li, Kaixiong Zhou +2
Visual Language Models (VLMs) have gained significant popularity due to their remarkable ability. While various methods exist to enhance privacy in text-based applications, privacy…
Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges
Tiejin Chen, Pingzhi Li, Kaixiong Zhou +2
Privacy risks in text-only Large Language Models (LLMs) are well studied, particularly their tendency to memorize and leak sensitive information. However, Multi-modal Large Languag…
TRUST: A Framework for Decentralized AI Service v.0.1
Yu-Chao Huang, Zhen Tan, Mohan Zhang +3
Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four limitations: (1) Robustness,…
Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration
Sukwon Yun, Jie Peng, Pingzhi Li +5
With an ever-growing zoo of LLMs and benchmarks, the need to orchestrate multiple models for improved task performance has never been more pressing. While frameworks like Mixture-o…
Mozart: Modularized and Efficient MoE Training on 3.5D Wafer-Scale Chiplet Architectures
Shuqing Luo, Ye Han, Pingzhi Li +7
Mixture-of-Experts (MoE) architecture offers enhanced efficiency for Large Language Models (LLMs) with modularized computation, yet its inherent sparsity poses significant hardware…
CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning
Xinyu Zhu, Yihao Feng, Yanchao Sun +5
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based post-training…
FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics
Pingzhi Li, Hongxuan Li, Zirui Liu +2
Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain s…
Towards Building Non-Fine-Tunable Foundation Models
Ziyao Wang, Nizhang Li, Pingzhi Li +3
Open-sourcing foundation models (FMs) enables broad reuse but also exposes model trainers to economic and safety risks from unrestricted downstream fine-tuning. We address this pro…