Morning Edition · Sunday, September 27, 2026Published at 2:30 AM EDT · New York
A single prompt, about a week of computation on 96 processors, and a compute cost Anthropic puts at $1,000 to $2,000 per method, verified by the physicist who held the previous result.

Anthropic published an account on September 25 describing how Claude Fable 5.1 computed, to nine loops, the six-particle scattering amplitude in planar N=4 super Yang-Mills theory (a highly symmetric model physicists use to test calculation methods in quantum field theory). No physicist had reached that level through direct calculation before. The previous result, at eight loops, belonged to Lance Dixon of SLAC National Accelerator Laboratory and Andy Liu, who reached it by an indirect method in 2023. Russian-language AI channels summarized the claim within hours of publication.
The setup deserves close attention. Matt von Hippel, a former theoretical physicist, issued a public challenge on August 7, asking labs to solve a real open problem on the budget of a typical university lab. Two Anthropic physicists, Liam Fitzpatrick and Siddharth Mishra-Sharma, gave the model a single instruction and then let it work inside Claude Science, checking in periodically. Anthropic reports about a week of computation on 96 processors, at a cost of $1,000 to $2,000 for each of the two methods used.
Verification here is stronger than in most lab-published science demonstrations. Dixon checked the result himself, and the write-up carries von Hippel's name. Even so, the result is a lab-published account that has not gone through peer review. It also addresses a single problem in a narrow, highly structured corner of theoretical physics, where symbolic manipulation and established bootstrap methods do much of the work.
Anthropic, which gets an expert-validated proof point for selling Claude Science to research institutions, and small academic groups who can argue for compute budgets instead of specialist hires.
Lance Dixon, who held the eight-loop result, checked the calculation, but the and signed by Matt von Hippel, the same person who issued the August 7 challenge, so the challenge-setter and the write-up author are one person and no peer review has occurred.
Part of a tracked trend
AI Moves Into Autonomous Scientific Discovery and Clinical Care
Over the next 3-9 months, AI systems move beyond text tasks into running real scientific experiments and managing clinical care, backed by peer-reviewed and benchmarked evidence of chemist- and physician-level performance.
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An open-source-intelligence read of how likely this story is true with its real nuance, not a judgment of any outlet. It assesses the claim, weighing independent and adversarial reporting. How we label confidence.
What this means
What is happening here is cheap autonomous compute substituting for scarce expert time on problems that are hard to solve but easy to verify, which is exactly where these models have the clearest advantage: an independent expert can confirm the answer without repeating the work. Anthropic gains a stronger case for Claude Science as a paid research platform, academic groups with small budgets gain a way around waiting for a specialist, and the assumption being tested is that frontier-level theoretical calculation requires a named research group at a national laboratory. The precedent is computer algebra software in the 1990s, which did not replace theorists but moved the boundary of what counted as routine calculation and shifted credit toward whoever framed the problem. The likelier outcome over the next several months is a series of similar single-prompt results in fields with machine-checkable answers, including formal mathematics, combinatorics and particle-physics amplitudes, provided labs keep funding the compute. This reading would be undercut if attempts on problems without a clean verification step produce results that no expert is willing to certify.
What to watch
Observations to monitor, not financial advice.
Source: Polylog editors
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