The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators

Key points

Controlled utility evolution, with a fixed criterion within each epoch and replacement only at epoch boundaries, preserves self-improvement guarantees even under nonstationary utility.
On coding tasks such as Polyglot, adding agent-as-a-judge code review signals improves pass rate by 1.8 percentage points over the previous SOTA while using 1.35x to 1.72x fewer tokens.
In the paper-writing setting, the co-evolved writer's acceptance rate rises from 21.8 percent to 40.5 percent, which is a 1.86x improvement.
In IMO-level proof grading, the co-evolved grader reaches higher accuracy with three times less search cost than the fixed baseline.
By injecting adversarial goals at epoch boundaries, it corrects reviewer self-preference bias toward AI-generated papers, where AI papers are accepted 1.91 times too often.
As the evaluator gets stronger, it also creates a curriculum effect for the task agent, which leads to archive reranking and gradual strengthening.

External research summaries. These are not HDATF publications or measured product results.