Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation

Researchers propose a new symbolic-mechanistic approach to evaluate AI models that goes beyond accuracy metrics to detect whether models truly generalize or rely on shortcuts like memorization. Their method combines symbolic rules with mechanistic interpretability to reveal when models exploit patterns rather than learn genuine capabilities, demonstrated through NL-to-SQL tasks where a memorization model achieved 94% accuracy but failed true generalization tests.

• →Traditional accuracy-based evaluation cannot distinguish genuine AI generalization from shortcuts like memorization or data leakage.
• →The proposed symbolic-mechanistic evaluation combines task-relevant rules with mechanistic interpretability for better model assessment.
• →A memorization-based model achieved 94% field-name accuracy while failing core schema generalization rules.
• →Standard evaluation metrics can provide false confidence in AI model capabilities, especially in small-data scenarios.
• →The new approach provides algorithmic pass/fail scores showing exactly where models succeed versus exploit patterns.