A recent experience with ChatGPT 5.5 Pro (gowers.wordpress.com)

A recent deep dive into ChatGPT 5.5 Pro's capabilities has revealed significant advancements in mathematical problem-solving, particularly within the realm of combinatorics. The author was able to generate a PhD-level research output in under an hour, tackling complex problems related to a paper by Mel Nathanson that explores integer sumsets. ChatGPT successfully provided a construction yielding a quadratic upper bound on sizes of defined sets, a task that typically requires extensive contemplation and background knowledge. This performance is noteworthy as it highlights the evolving role of large language models (LLMs) in creative and analytical tasks previously exclusive to human mathematicians. The implications of such advancements are profound for the AI/ML community. As LLMs gain the ability to generate original ideas or tackle open questions in mathematics, like improving upper bounds on established problems, they challenge traditional notions of originality and authorship in research. The results produced by ChatGPT, while still needing human validation, suggest that these tools can serve as collaborative partners in research, offering valuable insights and problem-solving strategies. Moving forward, there is a pressing need for frameworks to assess and house AI-generated content, emphasizing verification by human experts to ensure rigor and reliability in mathematical discourse.