## MathLedger: A New Approach to Verifiable Machine Learning A new system called MathLedger promises to address the growing concern about the opacity and non-verifiability of modern AI systems. The proposed solution integrates formal verification, cryptographic attestation, and learning dynamics into a single 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. Early experiments with MathLedger have validated the measurement and governance infrastructure under controlled conditions. ## Validation and Governance CAL-EXP-3 tests validated the measurement infrastructure, including Delta p computation and variance tracking. Separate stress tests confirmed that fail-closed governance triggers correctly under out-of-bounds conditions. The developers emphasize that no claims are made about the convergence or capability of the system. The main contribution of MathLedger is infrastructural: a working prototype of ledger-attested learning that enables full auditability of the process at scale. This represents a step forward towards more reliable and transparent AI systems, an increasingly central theme in public debate and technological development.