MathLedger: A Verifiable Learning Substrate with Ledger-Attested Feedback

TL;DR

MathLedger introduces a verifiable learning framework combining formal verification and cryptographic attestation, enabling scalable auditability of AI systems through a prototype that integrates feedback-driven updates and governance mechanisms.

Contribution

The paper presents MathLedger, a novel infrastructural system that enables verifiable, ledger-attested learning with a prototype demonstrating auditability and governance in AI.

Findings

Validation of measurement infrastructure under controlled conditions

Stress tests confirm correct fail-closed governance triggers

Prototype demonstrates scalable auditability of learning processes

Abstract

Contemporary AI systems achieve extraordinary performance yet remain opaque and non-verifiable, creating a crisis of trust for safety-critical deployment. We introduce MathLedger, a substrate for verifiable machine cognition that integrates formal verification, cryptographic attestation, and learning dynamics into a single epistemic loop. The system implements Reflexive Formal Learning (RFL), a symbolic analogue of gradient descent where updates are driven by verifier outcomes rather than statistical loss. Phase I experiments validate the measurement and governance substrate under controlled conditions. CAL-EXP-3 validates measurement infrastructure (Delta p computation, variance tracking); separate stress tests confirm fail-closed governance triggers correctly under out-of-bounds conditions. No convergence or capability claims are made. The contribution is infrastructural: a working prototype of ledger-attested learning that enables auditability at scale. Keywords: verifiable learning, formal verification, cryptographic attestation, reflexive feedback, fail-closed governance