Meanwhile, with additional prompting, ChatGPT successfully adapted the mathematical argument to handle both large and small cases, ultimately generating a new result that aligned with the original intent of the question. The resulting proof was shared via a Google Drive link: https://drive.google.com/file/d/1xRw8_o2C8HwmxMDnBR5OJlxXaW7jlYbz/view?usp=sharing. While the proof contained minor inaccuracies, the AI tool Aristotle was able to automatically detect and correct these gaps, producing a formally verified proof in Lean, a language designed for mathematical rigor. Next, a third participant ran Aristotle again on the corrected Lean proof to generate a more concise version. This streamlined proof was then fed into an extended, iterative conversation with ChatGPT—available at https://chatgpt.com/share/695e7cbd-605c-8010-809b-ccba75560c76—where it was transformed into a substantially more detailed and cohesive article. The revised version not only explained the proof itself but also contextualized it within existing mathematical literature and structured it with a clearer narrative flow. The final output, accessible at https://drive.google.com/file/d/1MRQfcHhrYMfMTvlZcMC3zEK7aOrUyHiQ/view?usp=sharing, represented a significant improvement over typical AI-generated content. It no longer carried the hallmark traits of generic automation and instead read with a level of sophistication and coherence comparable to an acceptable draft for a research paper. While further refinement—particularly in precision and depth of scholarly engagement—would still be beneficial, the result demonstrated the potential of collaborative human-AI workflows in advancing mathematical research. A detailed evaluation of the final writeup can be found at https://www.erdosproblems.com/forum/thread/728#post-2852.