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hep-ph2026
Agentic Re-Casting using Agentic Re-Simulations
Sascha Diefenbacher, Tilman Plehn, Daniel Schiller +1
Analysis re-casting at the LHC is highly standardized and nevertheless requires resources, time, and physics input. Building on the new MadAgents.v3, we show how a global SFitter a…
hep-ph2026
One Generator, Any Process: LLM-Conditioning for the LHC
Henning Bahl, Tilman Plehn, Daniel Schiller +1
Neural network training for LHC event generation should, ideally, benefit from common high-level patterns in different processes. We propose novel conditioning schemes for continuo…
hep-ph2026
MadAgents
Tilman Plehn, Daniel Schiller, Nikita Schmal
We uncover an effective and communicative set of agents working with MadGraph. Agentic installation, learning-by-doing training, and user support provide easy access to state-of-th…