Schmidhuber's team proposes Huxley-Gödel Machine for optimal self-improving coding agents
burny_tech · x · 2026-08-07
Jürgen Schmidhuber and colleagues published the paper "Huxley-Gödel Machine," exploring the development of human-level coding agents via an approximation of the optimal self-improving machine.
- Core Problem: Existing self-improving coding agents assume that better performance on software engineering benchmarks implies greater subsequent self-improvement potential. However, the authors identify a "Metaproductivity-Performance Mismatch."
- Proposed Solution: Inspired by Huxley's concept of clade, they propose a new metric, CMP, which aggregates the benchmark performances of an agent's descendants to evaluate its self-improvement potential. Based on this, they introduce the Huxley-Gödel Machine (HGM).
- Results: HGM estimates CMP and uses it as guidance to search the tree of self-modifications. On SWE-bench Verified and Polyglot, HGM outperforms prior self-improving coding agent development methods.
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