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This research aims to establish a formal methodology for testing the failure limits of neuro-symbolic AI systems.

May 9, 2026Open Access

Canonical Audit Checkpoints as Formal Falsification: Verified Boundary Testing in Neuro-Symbolic Systems

Key Points

This research aims to establish a formal methodology for testing the failure limits of neuro-symbolic AI systems.
Introduced the concept of canonical audit checkpoints for failure limit testing.
Critiqued current AI evaluations that overlook failure characterization.
Developed an audit table with 53 specific checkpoints across seven layers.
Demonstrated the mathematical proof of failure limits using the Boundary Proof Principle.
Outlined the theoretical framework's integrity secured by the Master Law Hash.

Abstract

Abstract This paper introduces a formal methodology for verified boundary testing in neuro-symbolic AI systems via canonical audit checkpoints. Instead of focusing on success rates, this framework posits that the scientific value of a system depends on its ability to mathematically prove its own failure limits via the Boundary Proof Principle. Core Concepts: Unattacked Success: A critique of current AI evaluations that view results as proof of correctness without characterizing the failure limits. Falsification Checkpoints: Specific test cases where the correct outcome is REJECT. Audit Table: A formal specification of 53 checkpoints across seven layers of the verified stack. Verification & Integrity: The theoretical integrity of the associated implementation is sealed with the Master Law Hash: dd76dc54c1c23c46b5aebf9582237ab5acb672eaa59da67cb8dbe274c7866bd5 Related Publications: This document provides the theoretical basis for the results in: Main Paper: 10.5281/zenodo.20065953 Reproduction Log: 10.5281/zenodo.20063061

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