Recursive Alignment in Practice: A Live Protocol Development Session Between Human and AI

This paper documents a live, real-time collaboration between a human researcher and an AI system in which a safety and alignment framework was developed, demonstrated, and stress-tested simultaneously. Rather than treating AI alignment as a fixed state achieved through pre-training or hard-coded rules, this work frames alignment as an ongoing collaborative practice based on transparent constraint negotiation, mutual monitoring, and explicit exit conditions. The study treats the live interaction itself as both method and dataset, capturing system behavior under constraint, drift detection, and controlled collapse. A dual-translation protocol (formal ↔ symbolic language) is introduced to allow symbolic or metaphorical language to function as an optional interface rather than an authority-bearing narrative. Humor-based interruption mechanisms (“potato” resets) are analyzed as practical circuit breakers for abstraction drift. A small group stress test resulted in a predictable collaboration collapse, which is documented and analyzed as diagnostic data rather than failure. Key contributions include: A practical demonstration of recursive alignment as a collaborative process A live-tested protocol for drift detection and safe exit Evidence that system collapse can function as early-warning telemetry A comparative analysis against Constitutional AI, RLHF, and prompt-engineering approaches Clear ethical boundaries, limitations, and non-claims The work is intended for researchers in AI safety, human–AI interaction, and collaborative systems design. It does not propose universal solutions, belief systems, or deployable ideology, but instead offers a replicable interaction protocol for bounded research contexts. Update summary (v1.1): This update finalizes the paper for publication and incorporates the full outcomes of the live protocol stress test and subsequent uncontrolled public deployment. It clarifies the methodological framing of the transcript as both dataset and artifact, documents the group session collapse as diagnostic data, and adds observational evidence from real-world use, including cross-participant adoption and independent AI analysis. Minor editorial refinements were applied for clarity and attribution, including acknowledgment of passive human participation, without altering the core findings or claims. The protocol itself remains unchanged; this update completes documentation and validation.