MathLedger: A Verifiable Learning Substrate with Ledger-Attested Feedback
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
Cite
@article{arxiv.2601.00816,
title = {MathLedger: A Verifiable Learning Substrate with Ledger-Attested Feedback},
author = {Ismail Ahmad Abdullah},
journal= {arXiv preprint arXiv:2601.00816},
year = {2026}
}
Comments
14 pages, 1 figure, 2 tables, 2 appendices with full proofs. Documents v0.9.4-pilot-audit-hardened audit surface with fail-closed governance, canonical JSON hashing, and artifact classification. Phase I infrastructure validation; no capability claims