Orateur
Description
Nowadays, AI agents that can write and run scientific code on their own. From coding assistants such as Claude Code, Cursor and Codex to integrated research systems such as Kosmos and Gemini Co-Scientist or Claude Science, they all are rapidly changing how analyses are built in physics. This speed also makes an existing problem worse. Methodological choices, assumptions and scientific justifications stay buried in code that is hard to inspect, reproduce or extend. For communities such as particle physics, astroparticle physics and cosmology, results depend on long chains of analysis decisions, and without suitable infrastructure AI-assisted research risks deepening the reproducibility crisis rather than solving it.
We present Lightcone Research, an open-source initiative, anchored between UC Berkeley and the CNRS, that aims to build the missing ecosystem for reproducible, composable and verifiable research in this new setting. At its core is ASTRA, an open declarative format that captures the scientific intent of an analysis in a standard that both humans and AI agents can read. That intent covers the problem, the inputs, the decisions, the analysis parameters, the outputs. Analyses that follow ASTRA are reproducible by default. More importantly, they are inspectable, composable and citable. Every result can be traced back to the assumptions that produced it, and individual decisions can be swapped without rewriting the whole pipeline. Analyses can also reference and build on one another.
The ecosystem is organised in four layers: (1) the ASTRA protocol for reproducible science; (2) an agentic layer for assisted analysis authoring, that works with (3) a command-line interface for validating analyses and running them seamlessly on HPC infrastructure, including NERSC and French computing centres; and (4) a modern web platform based on the Jupyter ecosystem for inspecting the results and their full provenance. We describe the architecture and showcase the platform at CC-IN2P3 and the reproductions of recent analyses in cosmology. We also report lessons learned from a beta-testing phase with researchers and discuss how the adoption of such system could be spread across IN2P3 and IRFU communities.