Accuracy-based evaluation cannot reliably distinguish genuine generalization from shortcuts like memorization, leakage, or brittle heuristics, especially in small-data regimes. In this position paper, we argue for mechanism-aware evaluation that combines task-relevant symbolic rules with mechanistic interpretability, yielding algorithmic pass/fail scores that show exactly where models generalize versus exploit patterns. We demonstrate this on NL-to-SQL by training two identical architectures under different conditions: one without schema information (forcing memorization), one with schema (enabling grounding). Standard evaluation shows the memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence. Our symbolic-mechanistic evaluation reveals this model violates core schema generalization rules, a failure invisible to accuracy metrics.
@article{arxiv.2603.23517,
title = {Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation},
author = {Reza Habibi and Darian Lee and Magy Seif El-Nasr},
journal= {arXiv preprint arXiv:2603.23517},
year = {2026}
}