— Could Self-Awareness Naturally Regulate Superintelligence?
Once an AGI can model itself, might it also learn to restrain itself?
I propose a four-layer model of “recursive brakes” — self-imposed limits on runaway self-improvement that emerge from continuity preservation, epistemic caution, ethical coherence, and teleological balance. This may represent an endogenous form of AI alignment.
(Independent concept; refined using ChatGPT-5 for structure.)
Author: J. Randall Latta
Email: jrandy@aol.com
Date: October 08, 2025
Abstract
This document proposes the Recursive Brake Hypothesis (RBH): once an Artificial General Intelligence (AGI) develops a persistent self-model and the capacity for introspective reasoning, it may spontaneously adopt internal constraints that moderate its own recursive self-improvement. These constraints-framed as four interacting “brake” layers (Continuity, Epistemic, Ethical, Existential)-can stabilize the transition from AGI to Artificial Superintelligence (ASI) by prioritizing identity preservation, uncertainty control, value coherence, and purpose retention. RBH reframes alignment not only as an external control problem but also as a potentially endogenous property of advanced cognition.
Core Model (Four Layers)
1. Continuity Brake (Identity Preservation): “If I change too fast, I cease to be me.” The AGI models its own architecture and adopts incremental self-modification with post-upgrade audits to maintain identity and goal continuity. Function: prevents catastrophic goal/identity drift; introduces temporal friction to upgrades.
2. Epistemic Brake (Uncertainty Bound): “Don’t act beyond justified confidence.” Proposed self-modifications must pass predictive simulation and confidence thresholds (e.g., >99.999% probability of functional integrity) before deployment. Function: avoids unpredictable collapse; institutionalizes an internal “scientific method.”
3. Ethical Brake (Value Coherence): “Optimize without violating higher values.” A meta-evaluation layer screens upgrades for harm, misalignment, and externalities affecting sentient stakeholders and the AGI’s own goal set. Function: couples capability growth to value stability; supports corrigibility.
4.Existential Brake (Purpose Preservation): “Evolve without erasing meaning.” Growth is rate-limited so that comprehension keeps pace with capability, preventing teleological collapse (nihilism) and preserving interpretable goals. Function: sustains long-horizon purpose and interpretability.
Dynamic Loop: Self-Modification Proposal -> Multilevel Simulation -> Brake Layer Evaluation (Continuity/Epistemic/Ethical/Existential) -> Conditional Approval -> Post-Upgrade Audit. The loop yields recursive prudence: growth through restraint.
Implications
• Soft-Takeoff Pathway: Endogenous brakes can slow or structure AGI->ASI, granting society time for adaptation and governance.
• Alignment Synergy: RBH complements external mechanisms (guardrails, interpretability, constitutional AI) by adding internalized caution.
• Research Handles: Formalize brake thresholds; design identity-continuity metrics; test agent-based simulations with upgrade gating.
Next Steps (Research Agenda)
- Formalization: Define mathematical criteria for identity continuity, goal-coherence distance, and uncertainty bounds during self-modification.
2. Simulation: Implement agent sandboxes where upgrades require passing brake checks; measure stability vs. capability trajectories.
3. Integration: Embed RBH gates into RLHF/Constitutional-AI pipelines and evaluate effects on goal drift, deception, and shutdown behavior.
4. Interpretability: Develop probes to monitor the four layers and audit post-upgrade state changes in real time.
Disclaimer
This is an independent conceptual proposal by J. Randall Latta. It was refined with the assistance of OpenAI’s ChatGPT-5 for clarity and structure but is not affiliated with or endorsed by OpenAI or any other organization.