**Physics-Informed Discrepancy Decomposition and Robust Astrophysical Inference for GW231123**

This paper proposes a novel, physics-informed framework to systematically decompose and attribute discrepancies among five gravitational-wave waveform models (NRSur7dq4, IMRPhenomXO4a, SEOBNRv5PHM, IMRPhenomXPHM, IMRPhenomTPHM) for the GW231123 event. Beyond standard multi-model posterior comparisons using advanced statistical metrics (e.g., Wasserstein distance, Jensen-Shannon Divergence) and high-dimensional degeneracy analysis (e.g., via ICA or UMAP for visualization), the core innovation lies in a **Physics-Informed Discrepancy Decomposition**. We will define physically motivated subsets of parameters (e.g., 'precession-sensitive parameters', 'higher-mode sensitive parameters') and quantify model disagreements specifically within these subspaces. By systematically analyzing how known differences in waveform model physics (e.g., precession treatment, higher-mode inclusion, NR calibration) correlate with observed discrepancies in these targeted parameter subsets, we will robustly identify the primary physical drivers of model disagreement. This approach will provide clear, interpretable insights into which astrophysical properties of GW231123 are robustly constrained across models and which remain sensitive to specific waveform model approximations.