Beyond a Better Score: Long-Horizon Agentic ML Development and Evaluation Protocol for Physics Time Series
SIDERIUS is an LLM agent system designed to move beyond optimizing numerical scores in machine learning for physics time series data. It introduces a rigorous evaluation protocol that checks model validity and scientific integrity, ensuring agents explore the model space strategically rather than just chasing high scores. This framework establishes a new metric for measuring progress in agentic scientific ML.