Against Unbounded Autonomous Discovery Loops: Enforcing Human-in-Command (HIC) and Deterministic Cryptographic Interlocks over Legacy Infrastructure Refactoring and Recursive AI Systems

The global software infrastructure faces a dual crisis: the structural impossibility of instantaneously rewriting multi-million-line legacy codebases (such as C, C++, and PHP foundations) into memory-safe languages like Rust without inducing catastrophic logical regressions, and the simultaneous emergence of unbounded "Human-out-of-the-Loop" autonomous AI engineering paradigms—exemplified by recursive hypothesis-experiment-implementation systems such as Discovery Loop. While proponents of unbounded autonomous loops claim that automated AI iteration can bypass human engineering bottlenecks across software architecture, semiconductor design, and scientific discovery, this monograph demonstrates that removing human command from recursive code synthesis induces irreversible epistemic opacity, logical state-space divergence, and uncompensated intellectual property extraction. We formulate a rigorous mathematical and thermodynamic critique of unverified autonomous loops, proving that high-frequency automated refactoring without human-anchored formal boundaries leads to exponential growth in unobservable logical defects and non-auditable failure modes. To resolve this, we establish a deterministic Human-in-Command (HIC) governance architecture anchored in RTO P2.0, Q-SAFA, and the QFS-zkVM operating over the 64-bit Goldilocks prime field (Fₚ, p = 2⁶⁴ - 2³² + 1). Every automated code mutation or architectural transition routed through PHOENIX_BRIDGE and PHOENIX_ROUTER requires a zero-knowledge cryptographic proof of boundary compliance and explicit Human-in-Command authorization before execution. Furthermore, we demonstrate that deploying unbounded autonomous loops in critical infrastructure violates statutory pre-trade/pre-execution risk mandates analogous to SEC Rule 15c3-5 and breaches fiduciary board oversight duties under Delaware Caremark jurisprudence, whereas enforcing the Strict No-Learn Covenant (NLL-v1.0-2026) prevents the unauthorized ingestion of human engineering heritage into automated loops.