3 papers
cs.AI2026
EnterpriseRAG: Benchmarking LLM Instruction Adherence and Robustness under Non-Ideal Enterprise Retrieval
Huiqi Miao, Xinbao Sun, Bo Wang +6
Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requirements simultaneously, revealin…
cs.AI2026
JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data
Junlan Feng, Fanyu Meng, Chong Long +12
We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehe…
cs.CL2025
JT-Safe: Intrinsically Enhancing the Safety and Trustworthiness of LLMs
Junlan Feng, Fanyu Meng, Chong Long +12
The hallucination and credibility concerns of large language models (LLMs) are global challenges that the industry is collectively addressing. Recently, a significant amount of adv…