Coherence Physics Pitches AI Science Workflow

This reads like a solid prompt-ops manifesto wrapped in scientific language, but it only becomes “real science” if the framework is tied to falsifiable experiments and outside validation.

• –External codex files are a practical way to reduce drift, preserve assumptions, and make reasoning auditable across sessions.
• –Adversarial passes are the strongest part of the method: forcing an AI to attack its own output is a good guardrail against confident nonsense.
• –The failure-first framing is useful; tracking recovery time, stability margins, and collapse modes is more scientific than optimizing for polished answers.
• –The weak point is epistemic overreach: structure and discipline help, but they do not replace experimental design, data, or peer review.
• –As written, this is closer to a disciplined LLM-assisted research workflow than a validated scientific framework.