7 papers · 1 filter
MindCraft: How Concept Trees Take Shape In Deep Models
Bowei Tian, Yexiao He, Wanghao Ye +3
Large-scale foundation models demonstrate strong performance across language, vision, and reasoning tasks. However, how they internally structure and stabilize concepts remains elu…
Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models
Ziyao Wang, Bowei Tian, Yexiao He +6
The recent advancement of foundation models (FMs) has increased the demand for fine-tuning these models on large-scale cross-domain datasets. To address this, federated fine-tuning…
Predictive Auditing of Hidden Tokens in LLM APIs via Reasoning Length Estimation
Ziyao Wang, Guoheng Sun, Yexiao He +3
Commercial LLM services often conceal internal reasoning traces while still charging users for every generated token, including those from hidden intermediate steps, raising concer…
FedHQ: Hybrid Runtime Quantization for Federated Learning
Zihao Zheng, Ziyao Wang, Xiuping Cui +6
Federated Learning (FL) is a decentralized model training approach that preserves data privacy but struggles with low efficiency. Quantization, a powerful training optimization tec…
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
Bowei Tian, Xuntao Lyu, Meng Liu +2
Representation Engineering (RepE) has emerged as a powerful paradigm for enhancing AI transparency by focusing on high-level representations rather than individual neurons or circu…
Towards counterfactual fairness through auxiliary variables
Bowei Tian, Ziyao Wang, Shwai He +5
The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motiva…