The Jupyter Notebook files and code for the proposed models and model training for paper named Beyond the Final Label: Exploiting the Untapped Potential of Classification Histories in Astronomical Light Curve Analysis (https://doi.org/10.48550/arXiv.2604.23792). All code and data are in the zip file. Purposes and Descriptions for each file: lc_w_flux_*: synthetic Light curves and classsification histories for the three selected classifiers. test_data_*: held-out test sets for model evaluation. lstm_atten_w_flux.ipynb: model architecture and traing for the proposed model that combines a recurrent network and attention mechanisms. naive_fcn_classifier.ipynb: model architecture and training for the naive model that directly use the final classification PMFs for each object as inputs. model_evaluation_demo.ipynb: model evaluation with the Early-Stable Classification Metric on baseline classifier A, with test random seed=0;for model evaluation on the new classifier, one need to train the model and apply the model on sequentially truncated light curves to obtain the full classification histories. utils.py: helper functions for model training and data handling.