Speaker
Description
Natural science increasingly relies on complex models embedded in large computational frameworks. Motivated by the DEMOS (DEmocratizing MOdelS) project's goal of making research models interoperable and encoding them into framework-independent formats, we address a necessary prerequisite: reconstructing an analysis-specific model and translating it between frameworks without changing its numerical predictions.
As a case study in hadron physics, we reimplement the $B^+ \to D^{*\pm}D^{\mp}K^+$ decay amplitude -- originally analyzed with the TF-PWA framework by the LHCb collaboration -- into CascadeDecays.jl, serving as a standard for the DEMOS project. Due to the complexity of the amplitude analysis framework through deeply nested, hardware optimized function calls, manual code tracing is extremely slow and error-prone. For the same reason, agentic forward tracing of the execution flow from input parameters to the final amplitude fails due to the combinatorial explosion of execution paths.
To overcome this, we introduce a method that recursively applies a standardized analysis protocol to each function layer: starting with the single top-level amplitude function, the AI agent identifies all underlying dependencies and sequentially progresses down the call tree to the input parameters and kinematics.
This reconstruction enabled the identification of convention mismatch factors, resulting in a framework alignment within numerical precision. The validated implementation establishes the basis for serializing this decay model, while the AI-driven backward-tracing approach provides a general method for deciphering codebases that collapse nested model definitions into single numerical outputs.