1 citations · 1 across the 6 of their papers we have counts for
6 papers
S^2F-Net:A Robust Spatial-Spectral Fusion Framework for Cross-Model AIGC Detection
Xiangyu Hu, Yicheng Hong, Hongchuang Zheng +2
The rapid development of generative models has imposed an urgent demand for detection schemes with strong generalization capabilities. However, existing detection methods generally…
From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges
Yihan Hong, Huaiyuan Yao, Bolin Shen +3
Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the same criteria inconsistently, pro…
PolarZero: A Reinforcement Learning Approach for Low-Complexity Polarization Kernel Design
Yi-Ting Hong, Stefano Rini, Luca Barletta
Polar codes with large kernels can achieve improved error exponents but are challenging to design with low decoding complexity. This work investigates kernel construction under rec…
PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong +2
Existing tool-augmented agentic systems are limited in the real world by (i) black-box reasoning steps that undermine trust of decision-making and pose safety risks, (ii) poor mult…
Reinforcement Learning-Aided Design of Efficient Polarization Kernels
Yi-Ting Hong, Stefano Rini, Luca Barletta
Polar codes with large kernels achieve optimal error exponents but are difficult to construct when low decoding complexity is also required. We address this challenge under recursi…
FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation
Yuxin Miao, Xinyuan Yang, Hongda Fan +6
Gastric cancer is one of the most commonly diagnosed cancers and has a high mortality rate. Due to limited medical resources, developing machine learning models for gastric cancer…