Two independent research teams solved the same open quantum cryptography problem using OpenAI's GPT-5.6 Sol Ultra within three hours of each other, highlighting how AI models are reshaping research workflows.
The case raises a critical question about what independent discovery means when all researchers access identical AI tools, and has sparked mixed reactions in the research community about AI's impact on the nature of scientific work.
What happened
MIT PhD student Seyoon Ragavan and professors Prabhanjan Ananth (UC Santa Barbara) and Amit Sahai (UCLA) independently solved the same open quantum cryptography problem using OpenAI's GPT-5.6 Sol Ultra, submitting papers to arXiv.org just three hours apart. Both tackled the same "uncloaking encryption" method that relies on quantum properties, though they took different approaches.
Why it matters
The simultaneous discovery underscores how AI models are reshaping the research process itself. As Professor Ananth notes, checking whether GPT solves an open problem has become a default first step for researchers. Ragavan described the shift starkly: "The way I do research now has nothing to do with how I did research two months ago." This raises a fundamental question about what constitutes independent discovery when all researchers have access to the same AI model.
What to watch
The two teams are considering merging their papers. The incident signals broader tension in the research community, particularly in mathematics—reactions range from enthusiasm about new possibilities to concern about erosion of research identity and discovery autonomy.
MIT PhD student Seyoon Ragavan and professors Prabhanjan Ananth from UC Santa Barbara and Amit Sahai from UCLA each independently tackled the same open problem in quantum cryptography, focusing on "unclonable encryption"—a method grounded in quantum properties. Using OpenAI's GPT-5.6 Sol Ultra, both teams developed solutions and submitted their papers to arXiv.org separated by just three hours, according to reporting by Scientific American. Though they applied different approaches to the same problem, the proximity of their solutions and timeline has prompted them to consider merging their papers into a joint submission.
The incident illuminates a broader shift in how scientific research is being conducted. Ananth captured the new default workflow concisely: "If someone mentions an open problem, the first thing is to see if GPT solves it." For Ragavan, the change has been dramatic enough to fundamentally alter methodology: "The way I do research now has nothing to do with how I did research two months ago." Mathematics appears to be experiencing the most acute effects of this transformation.
Reactions within the research community are mixed and conflicted. Some researchers express excitement about the new possibilities that AI-augmented methods unlock, while others report what Ragavan describes as "depression over a lost sense of identity." The case raises an unresolved question that institutions and researchers will increasingly face: when every researcher has access to the same AI model, what criteria should define an independent discovery? The boundary between using AI as a tool and outsourcing the core intellectual work remains contested and, for many, unsettling.
The near-simultaneous solution of the same quantum cryptography problem by two independent teams marks a inflection point in how scientific research operates. The fact that all three researchers—spanning different institutions and career stages—reached for the same AI model (GPT-5.6 Sol Ultra) and succeeded within a three-hour window suggests the tool has become not just a helper but a default research instrument. Professor Ananth's observation that checking whether GPT can solve an open problem is now a first instinct reveals how thoroughly the research workflow has already shifted. Ragavan's stark statement that research methodology has been unrecognizable over just two months reflects the speed of this change.
The tension surfaced in community reactions—ranging from excitement to what Ragavan characterizes as "depression over a lost sense of identity"—points to a deeper concern about authorship and discovery. When two teams can independently reach the same solution using identical tools, the traditional markers of independent intellectual work become blurred. This is not simply about faster research; it raises questions about what remains distinctly the researcher's contribution when the reasoning engine is externalized and shared. The research community, particularly in mathematics where the impact is most acute, must grapple with redefining what counts as original work and discovery in an era where AI reasoning is commodified.
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