Anthropic post centers on Claude and a nine-loop physics challenge

Anthropic published a research post titled “Yes, Claude can do Nine Loops” on Sep 25, 2026, featuring physicist and science writer Matt von Hippel’s account of a challenge he gave AI companies. The post frames the challenge as a test of whether AI could solve a difficult problem from his former field of theoretical physics using computing resources available to academics. The text provided describes the challenge and the physics behind it, but does not give details of a Claude calculation or an independent check.

Von Hippel’s proposed targets were either N=8 supergravity at seven loops or N=4 super Yang-Mills at nine loops. The post says Anthropic chose the latter, a simplified “toy model” that researchers use to test calculation techniques rather than explain real-world phenomena. Its account focuses on scattering amplitudes: mathematical formulas that help physicists estimate how subatomic particles will interact, using their momenta and energies. More accurate predictions can be compared with experiments, and mismatches could point toward a new theory.

The number of loops in a calculation measures the complexity of the interactions included, according to the post. Adding loops can move a calculation closer to the real answer, while increasing the computational difficulty. The post says most scattering amplitude formulas have been worked out only to two loops, a few to three, and the most precise particle-physics prediction familiar to many people used five. That gap helps explain why a nine-loop calculation would be a demanding benchmark, though the provided text does not document how Claude performed one.

The selected theory, N=4 super Yang-Mills, is not presented as a description of the world around us. The post explains that Yang-Mills theories describe three fundamental forces: electromagnetism, the strong nuclear force and the weak nuclear force. Its N=4 version incorporates supersymmetry, in which particles are paired with partners of another type, but the post says this model is unrealistic and is instead useful for developing amplitude-calculation methods. For AI builders, the account sets out a concrete computational challenge and the specialized context needed to interpret it; the available text does not establish a result, its verification, or Dixon’s involvement.