Speaker
Description
LLM agents are beginning to take on real high-energy-physics workflows — tasks that demand orchestrating full toolchains of event generators, detector simulation, and analysis code rather than simply producing text. Making such agents reliable, auditable, and cheap enough to run on local hardware raises a distinct set of questions from those studied in general-purpose agent benchmarks.
This talk examines what currently limits these agents, how their failure modes shift with model capability, and where the computational cost of an agent loop actually accumulates — often not where conventional inference optimizations target it. On the quality side, we discuss scaffold design and fine-tuning on collider-specific tasks, using agent trajectories and validated simulation artifacts as training data. On the speed side, we introduce speculative decoding, a technique that lets a model emit highly repetitive text much faster, and show why the templated structure of generator and detector configuration files makes it especially well suited to physics workflows.
We discuss these questions concretely in the context of analysis-recasting benchmark tasks and locally served, in-house-optimized models.