Speaker
Description
Modern neutrino experiments depend on complex and highly iterative analysis workflows involving reconstruction, simulation, calibration, background studies, validation, and documentation. In many cases, the bottleneck is not a single algorithm, but the efficient, reproducible, and auditable execution of expert-defined procedures. This talk presents the application of the Dr.Sai agentic scientific workflow in JUNO, focusing on system architecture and practical analysis outcomes rather than machine-learning methodology.
Dr.Sai is a project launched at IHEP to transform expert scientific procedures into structured agentic workflows. In JUNO, we adopt specification-driven development as the technical path. Scientific tasks are broken into small, testable units; atomic operations become reusable skills; and agents and researchers jointly assemble them into work plans. Requirements, validation criteria, unit tests, end-to-end tests, and acceptance tests are encoded in specification files. Together, skills, plans, and specifications form the domain-specific layer of the agentic system, and are iteratively updated during the analysis as new lessons are learned.
Two JUNO applications will be discussed. The first is the (^{8})B solar-neutrino analysis, where skill- and specification-driven workflows help organize repeated analysis cycles, configuration management, validation steps, and result traceability. The second is the acceleration of OMILREC, JUNO’s data-driven vertex and energy reconstruction framework, where agent-assisted profiling, benchmarking, and validation loops shorten the optimization cycle while preserving physics-level checks.
This work demonstrates that agentic workflows can be integrated into real, production-level neutrino analyses. The JUNO experience shows that such systems can improve reproducibility, efficiency, and scalability while keeping scientific judgment under human control.