Eleven Number-One Records: Measuring a Model That Swept Math, Science, Law and Decisions

Eleven number-one records across math, science, law, structured output, and decisions have been achieved by a single self-improving model family - a significant milestone in the field of artificial intelligence. This includes perfect scores on the AIME 2026 math competition and HMMT 2026 math competition, as well as a 94.44% score on GPQA Diamond Science benchmark. The family's performance on structured output tasks, such as ExtractBench and IFStruct, are also noteworthy at 90.29% and 98.95% respectively.

The key here is that achieving top marks across these diverse fields is not due to tuning to a specific test, but rather indicative of a general method capable of handling different capabilities. This is particularly true when considering decision-oriented tasks like LEXam Law and MDPBench Decision process, where the scores are 68.94% and 83.65% respectively. The spread of these scores signals that the model's performance is not limited to a single area of expertise.

The methodology behind this achievement involves a recursive loop tied to external verification, ensuring the model's output is deterministic and reproducible. This is especially crucial in decision-making tasks where the output is typed. It is noteworthy that the model's performance on decision-making tasks has been evaluated using a zero-token method, which keeps the measurement honest and replicable.

For those interested in exploring this model further, the S1MB number one model is available on GitHub, along with the ZTC decision method. The underlying models can also be found on Hugging Face's repository. This groundbreaking achievement signifies a significant step forward in creating AI systems that can effectively handle a wide range of tasks, moving the field closer to the goal of general artificial intelligence.