8 papers
When to Compile a Computer-Use Agent? Measuring Payback and Making Compilation Decisions for Token Efficiency
Yulong Ming, Jie Xu, Zihan Wu +1
Compiling GUI procedures that agents execute repeatedly into programs can reduce their token costs. However, measuring payback and deciding when to compile have two challenges. Fir…
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
Shiyuan Zuo, Jiashuo Li, Rongfei Fan +2
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks. Existing robust approac…
SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models
Chao Ding, Mouxiao Bian, Tianbin Li +12
Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because governance requires auditable reasonin…
GasLiteAA: Optimizing ERC-4337 for Efficient and Secure Gas Sponsorship
Hongxu Su, Mingzhe Liu, Jie Xu +2
ERC-4337, the Ethereum account abstraction standard, simplifies account management and transaction fee payment in decentralized applications by introducing programmable smart contr…
PRAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top- Retrieval
Yulong Ming, Mingyue Wang, Jijia Yang +4
Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy concerns for both data owners and users…
MulVul: Retrieval-augmented Multi-Agent Code Vulnerability Detection via Cross-Model Prompt Evolution
Zihan Wu, Jie Xu, Yun Peng +2
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability patterns undermines the effectiv…
Dynamic Influence Tracker: Measuring Time-Varying Sample Influence During Training
Jie Xu, Zihan Wu
Existing methods for measuring training sample influence on models only provide static, overall measurements, overlooking how sample influence changes during training. We propose D…
LiveVal: Time-aware Data Valuation via Adaptive Reference Points
Jie Xu, Zihan Wu, Cong Wang +1
Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing met…