Speaker
Description
LLM agents increasingly assist with coding, but still struggle with long, tool-heavy, multi-step tasks common in high-energy physics. We present Ariadne, an autonomous multi-role LLM agent that, given a natural-language task, is designed to plan, divide, and execute this workflow step by step.
Ariadne's orchestration is based on a LangGraph state that organizes the workflow context into structured, persistent records and does not rely on an unstructured conversation history. Coupled with a context manager, every LLM prompt is curated for the relevant node and task, using the workflow state and the current step's requirements. Targeted retrieval from supplied papers, arXiv articles, and documentation provides the context manager with relevant source material without loading full sources into every prompt. Artifact and execution records preserve provenance and support reproducibility, and node-specific prompts limit context degradation across the workflow. Ariadne validates results during the workflow and can debug errors through bounded repair and replanning loops. It integrates tools including MadGraph5, Pythia 8, Delphes, ROOT/uproot, and Prospino, and has native support for execution on local, Slurm, or HTCondor environments.
The framework is compatible with commercial or self-hosted LLM backends, enabling cost-conscious deployment. We discuss agent design and collider-analysis tests, emphasizing context engineering and validation to support reliable LLM agents for scientific computing.