A statistics professor at the University of Pennsylvania recently used OpenAI's GPT-5.6 Sol Pro model to disprove a central open conjecture about the Benjamini-Hochberg method in approximately 90 minutes. This marks an astonishing milestone, as the predecessor model, GPT-5.5, had completely failed to find a solution even after 20 hours of continuous testing.
Detailed Developments
This event represents a significant milestone in the application of artificial intelligence to academic scientific research. According to a report by The Decoder, the Penn professor challenged the next-generation AI model with a complex problem that had baffled mathematicians for 30 years. While the previous GPT-5.5 model was helpless after nearly a day of testing, the upgraded GPT-5.6 Sol Pro took just 1.5 hours to find the flaw and deliver a convincing disproof of the conjecture.
Technical & Technology Analysis
The solution generated by GPT-5.6 Sol Pro is evaluated as a clever combination of known mathematical methods in an entirely new approach. This demonstrates the model's deep logical reasoning and superior search space optimization. The system did not merely retrieve existing data, but instead showed the ability to link complex data structures to resolve the mathematical constraints of the Benjamini-Hochberg method—a widely used statistical hypothesis testing tool.
Expert Opinions & Insights
The success of GPT-5.6 Sol Pro has sparked lively debates in the global academic community. The biggest question raised now is whether AI is truly capable of generating entirely new knowledge for humanity, or if this is still just a highly intelligent recombination of the massive datasets it was pre-trained on. Nonetheless, experts universally agree that the speed and accuracy of AI in supporting cutting-edge research are advancing at a breakneck pace.
Impact & Future
This achievement opens a new chapter for human-computer collaboration in basic scientific research. For the technology and mathematics community in Vietnam, this is clear evidence that large language models (LLMs) are transitioning from simple text-drafting tools into genuine scientific research assistants, shrinking the time required to solve century-old problems from decades to mere minutes.