Learning from What It Learned: Decoding Post-Convergence States for Unified Action Quality Assessment

Unified action quality assessment (AQA) trains a single model across heterogeneous sports, enabling cross-sport representation sharing while converging to a shared solution shaped by heterogeneous sport-specific preferences. Post-convergence analysis reveals limited directional agreement among sport-specific gradients and substantial variation in dominant local curvature around the shared solution, while the raw-to-EMA trajectory contains an empirically favorable interior weight state. Motivated by these observations, we propose TRAILS, a post-convergence decoding framework that exploits the model states produced during unified training in weight and statistics spaces. In weight space, Trajectory Interpolation (TI) searches the raw-to-EMA path for a favorable shared weight configuration. In statistics space, Statistics Recalibration (SR) re-estimates sport-specific BatchNorm statistics from unlabeled test samples, with sport identities inferred automatically from frozen visual features. TRAILS retains a single shared AQA weight configuration without gradient-based retraining of the AQA model. Experiments on three heterogeneous AQA benchmarks show that TRAILS achieves an average SRCC of $0.777$ and an R-${\ell }_2$ of $2.008$, improving the overall performance of unified AQA.