Daniel Kang

Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering

Soorya Ram Shimgekar, Michelle Hu, Dorisa Shehi, et al.

Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports

Yufan Wang, Anit Kumar Sahu, Yan Fei Ng, et al.

ReViSQL: Achieving Human-Level Text-to-SQL

Yuxuan Zhu, Tengjun Jin, Yoojin Choi, et al.

Behavioral Fingerprints for LLM Endpoint Stability and Identity

Jonah Leshin, Manish Shah, Ian Timmis, et al.

This paper introduces Stability Monitor, a system that tracks whether AI language model endpoints behave consistently over time by periodically testing them with fixed prompts and analyzing how their outputs change. Rather than just checking if a service is running (uptime), it detects when a model's actual behavior shifts due to updates, hardware changes, or other modifications. The system successfully identified various types of changes in controlled tests and found that the same model can behave quite differently depending on which company is hosting it.