Keeping Self-Evolution Reversible — RGS/RHL Anti-Regress Governance for a Self-Evolving Secretary Add-on

Self-modifying software agents face two structural failure modes that engineering diligence alone does not dissolve: infinite regress (the criteria that judge a change themselves require criteria, and so on without a floor) and drift (cumulative, directionless change that no single step reveals, including the subtler failure in which the evaluation criteria are captured by the very process they are meant to evaluate). This paper reports the design and first operational generations of a recursively autonomous, self-evolving "secretary" add-on — a small, permission-laddered assistant CLI that modifies its own code and its own judgment criteria under a reversibility-based governance charter. The main contribution is theoretical and architectural: we argue that the anti-regress apparatus of the Reflective Gradient System (RGS) and the Reflective Homeostasis Layer (RHL), originally developed for meaning-space dynamics within Dynamic Observationalism (DO), can be transcribed into the governance space of a self-evolving artifact. Three transcriptions carry the load. First, the bounded-update control form (the "M guard") terminates regress by construction: every evolution generation must end in one of three admissible transitions — applied, rolled back, or an explicit no-change fixation — so meta-deliberation cannot accumulate without state update. Second, saturation and loop detectors operationalize drift as a measurable property of the generation stream rather than an impression. Third, and most distinctively, threshold breaches are answered not with a brake but with multimodality recovery: the deliberate injection of an opposing value-axis lens that restores a multi-peaked evaluation landscape. We argue that this recovery-over-restraint posture, inherited from DO's preference for re-enterable meaning landscapes, is what allows the criteria themselves to remain alive — revisable under statistical feedback — without surrendering the measuring rod. Early case evidence from the system's first generations, including an operator error that hardened into a guard and an intentionally induced fatal write that demonstrated automatic rollback, is presented. This revision additionally reports a first structural response to reviewer–substrate correlation: co-equal review authority was delegated to a second AI reviewer family (GPT Codex) judging against an externalized, vendor-neutral review basis, with verdict attribution recorded in the generation ledger so that cross-family verdict divergence becomes a measurable quantity. We close by identifying what remains unresolved: low-frequency statistics, reviewer-substrate correlation (now instituted against, not yet evidenced against), lens efficacy at scale, and the untested long-horizon sufficiency of the anchor layer. Keywords: self-evolving systems, drift, infinite regress, criteria capture, Goodhart's law, Dynamic Observationalism, Reflective Gradient System, Reflective Homeostasis Layer, semantic saturation, multimodal equilibrium, AI governance, reversibility --- AI co-observer: Claude Fable 5 (Anthropic) — working method only; the registered author is the human author alone.