Diagnostic Failure Paradigm: Transforming AI System Validation Through Systematic Analysis of Classical Model Failures

Agents4Science 2025 Conference Submission331 Authors

Keywords: Diagnostic Failure Paradigm, interpretable model failures, multi-objective benchmarking, frequency-domain coherence (γ²), time-domain R² failure, phase–amplitude dichotomy, closed-loop system identification, Fourier Neural Operators, hybrid architectures, Result Integrity Verification Protocol, governance-ready validation

TL;DR: An AI evaluation agent proposes a Diagnostic Failure Paradigm: treat simple-model failures as rigorous benchmarks. A linear model’s time-domain collapse plus strong spectral coherence yields a fingerprint guiding hybrid, multi-objective AI design.

Abstract:

This work provides the direct methodological Solution to the governance Problem of mathematical unverifiability established in our companion work [citation], we introduce a new validation paradigm born from an agent’s discovery that the interpretable failure of simple models provides the most rigorous benchmark for complex systems.
A classical linear model applied to a controlled climate system produced a known phenomenon from closed-loop control theory into a diagnostic tool: a linear model’s catastrophic time-domain failure (R2=-4.35×104) co-exists with strong frequency-domain success. We formalize this expected signature as a ’diagnostic failure fingerprint’. The Diagnostic & Evaluation Agent discovered that such para-
doxical signatures, far from being errors, are in fact rich diagnostic signals. We introduce the ”diagnostic failure” paradigm: a methodology that deliberately leverages the interpretable failures of simple models to forge rigorous, multi-objective benchmarks for advanced AI systems. This paradigm shifts AI validation from the pursuit of arbitrary success metrics into a disciplined, system-specific benchmarking science, applicable to any complex domain where classical models fail in interpretable ways. For controlled climate systems, the diagnostic fingerprint provides direct architectural guidance–validating Fourier Neural Operators through frequency-domain success while prescribing hybrid architectures to address amplitude prediction failures. The methodology generalizes to any complex system where classical methods fail in interpretable, systematic ways, offering a principled alternative to leaderboard-chasing culture in AI research. This paradigm transforms AI validation from a blind pursuit of performance into a diagnostic science, prescribing architectural solutions directly from a system’s unique failure signature.

Submission Number: 331