A Judge Agent Closes the Reliability Gap in AI-Generated Scientific Simulation

TL;DR

A Judge Agent automates mathematical validation of AI-generated scientific simulation code, drastically reducing silent failures and improving reliability across multiple scientific domains.

Contribution

Introduction of a Judge Agent that automates validation, formalizes scientific computation problems, and provides a structured specification format, enhancing reliability of AI-generated code.

Findings

Silent failure rate reduced from 42% to 1.5% across 134 test cases.

Automated validation achieves 89% success on blinded tasks, outperforming unassisted methods.

Pipeline reaches 99% of expert quality on clinical CT data.

Abstract

Large language models can generate scientific simulation code, but the generated code silently fails on most non-textbook problems. We show that classical mathematical validation -- well-posedness, convergence, and error certification -- can be fully automated by a Judge Agent, reducing the silent-failure rate from 42% to 1.5% across 134 test cases spanning 12 scientific domains. The headline result comes from a prospective benchmark: 72 blinded tasks submitted by 12 independent scientists yield an 89% success rate (95% CI: [80%, 95%]) with automated error bounds, versus 53% without the Judge. On clinical CT (the only powered experiment, n = 200), the pipeline reaches 99% of expert quality. The residual 1.5% concentrates at bifurcation points where certifiability breaks down. We formalize this boundary through the simulability class S and introduce spec.md, a structured specification format that makes any scientific computation problem machine-readable and solver-independent. Code, data, and all 72 benchmark tasks are publicly archived.