--- format: "aidr-story-markdown/v1" id: "d194009aff36389e561b620503bc121ec1dce469e8e80a58c6164ac7751ce0f8" canonical_url: "https://aidr.today/d194009a?lang=en" title: "More questions about whether researchers can trust OpenAI with unpublished math" lang: "en" requested_lang: "en" available_langs: ["en","vi"] translation_fallback: null fallback_fields: [] published_at: "2026-09-10T06:49:43.000Z" category: "Regulation" topics: ["openai","trust","transparency","math-research","data-privacy","research-integrity"] source_urls: ["https://mathstodon.xyz/@andreasthom/117240535270608201","https://news.ycombinator.com/item?id=49639408"] summary: "@tristanbuckmaster@mastodon.social 1/3 I want to record a number of points that I realized after reading up on the controversy. In fact it recalls an exchange I had with OpenAI after its non-sofic-group announcement. It exposes a serious, unanswered question about whether researchers can trust OpenAI with unpublished mathematics. I wrote in an email to Mark Sellke and Sebastien Bubeck shortly after the surprising finding of a non-sofic group using the methods of Kun and myself: “Another point is that I and a colleague in Dresden were discussing the expander matching problem and various extensions of the work with Gabor Kun actively over the last months with ChatGPT, so that we are of course curious if that was part of the training data or accessible to the reasoning process. There is a certain (frankly unacceptable) lack of transparency here; and I fear it will damage the communal process of math more than the new AI-generated results will benefit the subject.” Mark Sellke’s complete answer was: “Regarding your conversations with ChatGPT: that did not happen.” I had explicitly asked about two different things: (1) whether our conversations entered training data, and (2) whether th…" --- # More questions about whether researchers can trust OpenAI with unpublished math > [Open the canonical story]() **Published:** 2026-09-10T06:49:43.000Z **Category:** Regulation **Topics:** openai, trust, transparency, math\-research, data\-privacy, research\-integrity ## Summary @tristanbuckmaster@mastodon\.social 1/3 I want to record a number of points that I realized after reading up on the controversy\. In fact it recalls an exchange I had with OpenAI after its non\-sofic\-group announcement\. It exposes a serious, unanswered question about whether researchers can trust OpenAI with unpublished mathematics\. I wrote in an email to Mark Sellke and Sebastien Bubeck shortly after the surprising finding of a non\-sofic group using the methods of Kun and myself: “Another point is that I and a colleague in Dresden were discussing the expander matching problem and various extensions of the work with Gabor Kun actively over the last months with ChatGPT, so that we are of course curious if that was part of the training data or accessible to the reasoning process\. There is a certain \(frankly unacceptable\) lack of transparency here; and I fear it will damage the communal process of math more than the new AI\-generated results will benefit the subject\.” Mark Sellke’s complete answer was: “Regarding your conversations with ChatGPT: that did not happen\.” I had explicitly asked about two different things: \(1\) whether our conversations entered training data, and \(2\) whether th… ## Sources - [Story source]() - [Discussion]()