dual-existence-proofs-enable-general-purpose-reliability

Antecedents (all must be IN):

• IN svm-existence-proof-reliable-ml-inaccessible — SVMs suggest that reliable ML may be achievable — their unusual theory-practice unity demonstrates that mathematical rigor can produce a fully codified practical methodology — but ML's economic and research dynamics appear to select against such approaches, making reliability arguably demonstrable in principle yet difficult to reach through the field's current evolutionary trajectory.
• IN scientific-deployment-bypasses-crisis-via-external-validation — Scientific ML deployments (AlphaFold, GNoME, CERN simulations) succeed by substituting domain-specific physical validation for ML's absent reliability guarantees, while simultaneously compounding accountability concerns when the same implicit models (GANs) are deployed in high-stakes domains — revealing that successful ML deployment requires escaping ML's own evaluation methodology, which is precisely the escape route unavailable to domains without independent physical ground truth.

Unless (any of these IN defeats this justification):

• IN crisis-constitutive-of-capable-ml — ML's reliability crisis appears deeply connected to capable ML itself — deep learning's foundational mechanisms (weight sharing for geometry-matched compression, gradient flow for trainability) have been validated as mathematical necessities rather than design choices, and the crisis these mechanisms produce is both self-perpetuating and structurally unresolvable within ML's existing intellectual resources. This suggests that a reliability crisis may be a recurring structural feature of ML paradigms powerful enough to be useful, though the link between mathematical necessity of the mechanisms and inevitability of the crisis remains an inference rather than a proven entailment.