Lessons from an AI-Assisted Independent Physics Laboratory: An Artifact-Based Retrospective Case Study

This preprint presents a methodological case study of Gravity Lab, an independent computational physics research program conducted through collaboration between a human investigator and multiple artificial intelligence systems. The project explored a range of computational and quantum-physics questions, including gravitational-wave modeling, quantum transport, phase reconstruction, and quantum hardware experiments. Rather than presenting these investigations as a sequence of discoveries, the paper examines the research process itself: experimental design, computational implementation, verification, adversarial testing, error correction, and the preservation of unsuccessful results. Particular attention is given to the opportunities and limitations of AI-assisted scientific investigation. The study discusses how AI systems can support hypothesis development, numerical analysis, software engineering, documentation, and scientific criticism, while also introducing risks involving unsupported interpretations, implementation errors, reproducibility failures, and excessive confidence in computational outputs. The paper describes a research workflow emphasizing human oversight, explicit experimental acceptance criteria, independent numerical checks, provenance tracking, preservation of negative results, and transparent reporting of unresolved uncertainties. The central contribution is a documented methodological framework and practical lessons from an independent research program, rather than a claim that artificial intelligence has independently discovered new physical laws or demonstrated superior scientific productivity. The study is intended for independent researchers, citizen scientists, computational physicists, and developers interested in responsible human–AI collaboration in scientific research. Publication status: Preprint; not peer reviewed. The findings are methodological and exploratory, and no formal validation of improved research productivity is claimed.