Pingzhi Li

According to our database, Pingzhi Li authored at least 35 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration.

Sukwon Yun, Jie Peng, Pingzhi Li, Wendong Fan, Jie Chen, James Zou, Guohao Li, Tianlong Chen

CoRR, April, 2026

Mozart: Modularized and Efficient MoE Training on 3.5D Wafer-Scale Chiplet Architectures.

Shuqing Luo, Ye Han, Pingzhi Li, Jiayin Qin, Jie Peng, Yang Zhao, Yu Cao, Tianlong Chen

CoRR, March, 2026

CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning.

Xinyu Zhu, Yihao Feng, Yanchao Sun, Xianzhi Du, Pingzhi Li, Olli Saarikivi, Yun Zhu, Yu Meng

CoRR, March, 2026

FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics.

Pingzhi Li, Hongxuan Li, Zirui Liu, Xingcheng Lin, Tianlong Chen

CoRR, February, 2026

Towards Building Non-Fine-Tunable Foundation Models.

Ziyao Wang, Nizhang Li, Pingzhi Li, Guoheng Sun, Tianlong Chen, Ang Li

CoRR, February, 2026

2025

TRUST: A Decentralized Framework for Auditing Large Language Model Reasoning.

Morris Yu-Chao Huang, Zhen Tan, Mohan Zhang, Pingzhi Li, Zhuo Zhang, Tianlong Chen

CoRR, October, 2025

Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures.

Pingzhi Li, Morris Yu-Chao Huang, Zhen Tan, Qingquan Song, Jie Peng, Kai Zou, Yu Cheng, Kaidi Xu, Tianlong Chen

CoRR, October, 2025

Can GRPO Help LLMs Transcend Their Pretraining Origin?

Kangqi Ni, Zhen Tan, Zijie Liu, Pingzhi Li, Tianlong Chen

CoRR, October, 2025

AsyncSpade: Efficient Test-Time Scaling with Asynchronous Sparse Decoding.

Shuqing Luo, Yilin Guan, Pingzhi Li, Hanrui Wang, Tianlong Chen

CoRR, October, 2025

DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation.

Pingzhi Li, Zhen Tan, Huaizhi Qu, Huan Liu, Tianlong Chen

CoRR, May, 2025

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference.

Shuqing Luo, Pingzhi Li, Jie Peng, Hanrui Wang, Yang Zhao, Yu Cao, Yu Cheng, Tianlong Chen

CoRR, May, 2025

GroverGPT-2: Simulating Grover's Algorithm via Chain-of-Thought Reasoning and Quantum-Native Tokenization.

Min Chen, Jinglei Cheng, Pingzhi Li, Haoran Wang, Tianlong Chen, Junyu Liu

CoRR, May, 2025

Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations.

Ajay Jaiswal, Jianyu Wang, Yixiao Li, Pingzhi Li, Tianlong Chen, Zhangyang Wang, Chong Wang, Ruoming Pang, Xianzhi Du

CoRR, April, 2025

ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion.

Rana Muhammad Shahroz Khan, Dongwen Tang, Pingzhi Li, Kai Wang, Tianlong Chen

CoRR, March, 2025

Make Optimization Once and for All with Fine-grained Guidance.

Mingjia Shi, Ruihan Lin, Xuxi Chen, Yuhao Zhou, Zezhen Ding, Pingzhi Li, Tong Wang, Kai Wang, Zhangyang Wang, Jiheng Zhang, Tianlong Chen

CoRR, March, 2025

GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching.

Haoran Wang, Pingzhi Li, Min Chen, Jinglei Cheng, Junyu Liu, Tianlong Chen

CoRR, January, 2025

A Decentralized Framework for Auditing Large Language Model Reasoning.

Morris Yu-Chao Huang, Zhen Tan, Mohan Zhang, Pingzhi Li, Zezhen Ding, Zhuo Zhang, Tianlong Chen

Proceedings of the 7th IEEE International Conference on Trust, 2025

Protecting Privacy against Membership Inference Attack with LLM Fine-tuning through Flatness.

Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Proceedings of the 2025 SIAM International Conference on Data Mining, 2025

Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design.

Mohan Zhang, Pingzhi Li, Jie Peng, Mufan Qiu, Tianlong Chen

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

Occult: Optimizing Collaborative Communications across Experts for Accelerated Parallel MoE Training and Inference.

Shuqing Luo, Pingzhi Li, Jie Peng, Yang Zhao, Yu Cao, Yu Cheng, Tianlong Chen

Proceedings of the Forty-second International Conference on Machine Learning, 2025

PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches.

Rana Muhammad Shahroz, Pingzhi Li, Sukwon Yun, Zhenyu Wang, Shahriar Nirjon, Chau-Wai Wong, Tianlong Chen

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion.

Rana Muhammad Shahroz, Dongwen Tang, Pingzhi Li, Kai Wang, Tianlong Chen

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety.

Mufan Qiu, Zheyu Shen, Pingzhi Li, Ang Li, Tianlong Chen

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Glider: Global and Local Instruction-Driven Expert Router.

Pingzhi Li, Prateek Yadav, Jaehong Yoon, Jie Peng, Yi-Lin Sung, Mohit Bansal, Tianlong Chen

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges.

Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Proceedings of the Findings of the Association for Computational Linguistics, 2025

Vision Language Model Helps Private Information De-Identification in Vision Data.

Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

HEXA-MoE: Efficient and Heterogeneous-aware MoE Acceleration with ZERO Computation Redundancy.

Shuqing Luo, Jie Peng, Pingzhi Li, Tianlong Chen

CoRR, 2024

PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches.

Rana Muhammad Shahroz Khan, Pingzhi Li, Sukwon Yun, Zhenyu Wang, Shahriar Nirjon, Chau-Wai Wong, Tianlong Chen

CoRR, 2024

Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark.

Pingzhi Li, Xiaolong Jin, Yu Cheng, Tianlong Chen

CoRR, 2024

Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent.

Pingzhi Li, Junyu Liu, Hanrui Wang, Tianlong Chen

CoRR, 2024

Privacy-preserving Fine-tuning of Large Language Models through Flatness.

Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

CoRR, 2024

Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography.

Pingzhi Li, Tianlong Chen, Junyu Liu

Proceedings of the 5th IEEE International Conference on Trust, 2024

Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild.

Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark.

Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy.

Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen

Proceedings of the Twelfth International Conference on Learning Representations, 2024