4 citations · 8 across the 11 of their papers we have counts for
11 papers
Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarks
Yiliang Song, Hongjun An, Jiangan Chen +4
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Tes…
Ruyi2 Technical Report
Huan Song, Shuyu Tian, Junyi Hao +5
Large Language Models (LLMs) face significant challenges regarding deployment costs and latency, necessitating adaptive computing strategies. Building upon the AI Flow framework, w…
CreditAudit: 2 Dimension for LLM Evaluation and Selection
Yiliang Song, Hongjun An, Jiangong Xiao +3
Leaderboard scores on public benchmarks have been steadily rising and converging, with many frontier language models now separated by only marginal differences. However, these scor…
Theoretical Foundations of Scaling Law in Familial Models
Huan Song, Qingfei Zhao, Ting Long +4
Neural scaling laws have become foundational for optimizing large language model (LLM) training, yet they typically assume a single dense model output. This limitation effectively…
Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence
Hongjun An, Yiliang Song, Jiawei Shao +2
Adverse social interactions, such as bullying, harassment, and other illicit activities, pose significant threats to individual well-being and public safety, leaving profound impac…
Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity
Hongjun An, Yiliang Song, Jiangan Chen +3
Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and interaction-friendly. However, this preference-ori…
AI Flow: Perspectives, Scenarios, and Approaches
Hongjun An, Wenhan Hu, Sida Huang +11
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…
Large model enhanced computational ghost imaging
Yifan Chen, Hongjun An, Zhe Sun +4
Ghost imaging (GI) achieves 2D image reconstruction through high-order correlation of 1D bucket signals and 2D light field information, particularly demonstrating enhanced detectio…
Physics in Next-token Prediction
Hongjun An, Yiliang Song, Xuelong Li
We discovered the underlying physics in Next-token Prediction (NTP). We identified the law of information conservation within NTP and proposed the First Law of Information Capacity…
SentenceVAE: Enable Next-sentence Prediction for Large Language Models with Faster Speed, Higher Accuracy and Longer Context
Hongjun An, Yifan Chen, Zhe Sun +1
Current large language models (LLMs) primarily utilize next-token prediction method for inference, which significantly impedes their processing speed. In this paper, we introduce a…
StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
Xuelong Li, Hongjun An, Haofei Zhao +6
In this paper, we introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on our self-developed Underwater Carrier LiDAR-Radar (UCLR) that embeds Se…