RESEARCH · RESEARCH · #2459
Preprint: scoring proposed code edits to guide recursive self‑improvement of coding agents
The arXiv preprint (arXiv:2610.29121v1) proposes methods for cheaply scoring proposed scaffold edits of a coding agent before executing them, fitting a linear readout on a parent's steering-split solve rate plus edit features and using a Huxley–Gödel Machine selection scheme (Thompson sampling) to choose which edits to expand. Evaluated on HumanEval+ and Polyglot splits, twelve GPT-4o runs returned a node with 87.0% held-out HumanEval+ score (vs. 84.2% for a parent-rate ablation); Huxley–Gödel selection reached 85.1% while Claude Sonnet 4.5 reached 86.6% on the same split, with comparable baselines reported for root and calibration-best edits.
KEY POINTS
- The arXiv preprint (arXiv:2610.29121v1) proposes methods for cheaply scoring proposed scaffold edits of a coding agent before executing them, fitting a linear readout on a parent's steering-split solve rate plus edit features and using a Huxley–Gödel Machine selection scheme (Thompson sampling) to choose which edits to expand.
- Evaluated on HumanEval+ and Polyglot splits, twelve GPT-4o runs returned a node with 87.0% held-out HumanEval+ score (vs.
- 84.2% for a parent-rate ablation); Huxley–Gödel selection reached 85.1% while Claude Sonnet 4.5 reached 86.6% on the same split, with comparable baselines reported for root and calibration-best edits.
WHY IT MATTERS
This gives a practical, low-cost way to rank and select self-modifying code edits for coding agents and shows the method is competitive with current LLMs, which matters for automation and safety of recursive self‑improvement.