arXiv AI September 30, 2026 By Jiarui Li, Zixiang Yin, Samuel Landry, Zhengming Ding, Ramgopal Mettu

Explainability from Training with Applications to TCR-Epitope Prediction

The paper introduces Explainability from Training (EFT), a model‑agnostic method that tracks how deep learning models learn and organize evidence during training. EFT is applied to four leading T cell receptor‑epitope prediction models, revealing distinct learning trajectories for CNNs and transformers, conflicts between TCR alpha and beta chain evidence, and differences in feature preferences when using real versus predicted structural data. The authors also present a new benchmark, TCR‑XAI2, comprising 388 experimentally resolved TCR‑epitope structures and several predicted models to evaluate these insights.

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