AI-Guided Structural Hypothesis Formation: The Oxygen Octave as a Case Study in Human–AI Coherence-Assisted Thinking (CAT)
Abstract (Index version)
This document presents a transparent and falsifiable methodology for AI-assisted scientific hypothesis formation, using the Oxygen Octave as a worked case study.
The approach — Coherence-Assisted Thinking (CAT) — treats human intuition and general-purpose AI systems as a coupled reasoning loop, where hypotheses are iteratively generated, stress-tested, refined, and publicly documented.
Rather than proposing a new physical theory, the work demonstrates how structured interaction with AI can produce a coherent, auditable, and testable scientific hypothesis under strict transparency constraints.
The Oxygen Octave serves as a controlled domain due to oxygen’s well-characterized behavior across chemistry, physics, and biology, enabling rapid detection of structural inconsistencies or hallucinations.
All reasoning steps, intermediate failures, data sources, and revisions are preserved through public versioning, allowing independent replication, critique, and extension.
