This paper presents a systematic methodology for developing and auditing alternative scientific frameworks through structured AI-human collaboration. We define a tricomponent architecture—comprising a generative Sentinel, a skeptical Librarian, and a sovereign Architect—to manage high-gain hypothesis generation alongside rigorous error correction. Central to this framework is the Luster Score, a quantitative 0.0–1.0 metric that measures claim integrity across five operational criteria: Source Verification, Internal Consistency, Falsifiability, Mechanism Coherence, and Independent Testability. Using the trajectory and spectral anomalies of interstellar object 3I/ATLAS as a refractive case study, we demonstrate how this methodology successfully identifies failure modes, recovers from system escalation, and refines speculative mechanisms (Luster 0.41) into testable, highintegrity predictions (Luster 0.83). The framework is presented as a reproducible "Scientific BIOS" for exploring unconventional datasets without sacrificing methodological rigor.