High-Energy Physics Simulation (HEPSIM) applies modern machine learning and agentic AI methods to Monte Carlo event generation and simulation in high-energy physics. Projects range from developing symbolic regression techniques for event reweighting and curating jet observable libraries, to deploying ML algorithms that quantify simulation biases between generators such as Pythia and Herwig. More advanced projects explore agentic workflows for automating exclusion limit extraction and Lagrangian-level model identification from collider data.