Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection

Battle Count: 50

The paper directly addresses governance of self-adapting models in consequential decision contexts including algorithmic trading. The telemetry architecture, CUSUM triggers, and dual-regime principle are applicable to monitoring trading models that update via online learning. The Lyapunov stability framework and quadratic variation telemetry could monitor model drift in production trading systems. The adversarial concealment taxonomy is relevant to firms that might hide model changes from regulators. However, the paper is primarily a governance/regulatory framework rather than a trading strategy or risk model per se. The MRM guidance context (SR 11-7) is directly relevant to financial institutions deploying AI in trading.

The framework requires: (1) embedding probes within SGD optimizers for real-time KL and quadratic variation computation; (2) cryptographic Merkle chain construction with SHA-3-256; (3) continuous-time SDE discretization and Ito calculus; (4) Lyapunov function evaluation with projection onto safe manifolds; (5) CUSUM statistical testing with ARL calibration; (6) TEE attestation (Intel TDX/AMD SEV-SNP); (7) zero-knowledge proof generation (zk-SNARKs) for Tier 3 updates; (8) behavioral oracle with secret canary sets; (9) Law of the Iterated Logarithm envelope monitoring; (10) multi-layer defense-in-depth architecture. The theoretical depth (stochastic calculus, information theory, cryptography, game theory) combined with the engineering requirements (TEE, zk-SNARKs, real-time telemetry) makes this extremely complex to implement in practice.

All numerical studies use fixed, documented random seeds and are exactly reproducible. However, experiments are at toy scale (softmax classifier with dK=24 parameters, m=80 canary points), far from production dimensions (d~10^9-10^12). The counting argument in Section 13 is explicitly stated as a heuristic, not a theorem, and is not tested at the simulated scale. The paper provides detailed experimental designs, parameter settings, and theoretical predictions for comparison. No code repository is mentioned.