5 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…
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…