Self-Evolving Coding Rules for AI Coding Agents

Your coding assistant makes changes that still need substantial repair before merging.

What you would change

Try the technique on held-out issues from a repository with reliable tests.

Try the technique on held-out issues from a repository with reliable tests.

Compare against

The same coding agent, repository snapshot, tools, and token budget.

1. Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.
2. Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.
3. Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.
4. Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test.

What to measure

Correct patches, regression failures, human repair time, and total cost per accepted change.

When to keep it

Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation.

recommendation

test

research area

coding agents

technical change

Self-evolving coding rule optimization for coding agents

published

2026-09-30

The method runs through third-party APIs and requires no training or clusters, making it feasible on constrained infrastructure, but the claimed gains are benchmark-specific and must be validated against the reader's own codebase, models, and judge quality before production adoption.

1. claim 01Paperraft inference
   Read the brief interpretation

   Replaces hand-crafted, static coding rules (system prompts and guidelines) for AI coding agents with an iterative mutate-and-judge loop that evolves a pool of rule candidates using an LLM mutator and an LLM judge. Costs include an offline optimization phase with repeated LLM calls for mutation and evaluation, which consumes API budget or GPU time, plus ongoing complexity of maintaining the candidate pool and evaluation harness; inference-time cost may decrease if evolved rules reduce token usage. Can fail if the judge's evaluations do not transfer to the reader's actual tasks and repositories, if evolved rules overfit the benchmarks used during optimization, or if the optimization budget is too small to converge on rules better than a well-written manual prompt.

   This is a Paperraft inference. It is not pinned to a verified PDF passage.

   Inspect the source
2. claim 02Paperraft inference

   RuleEvolve outperforms manual engineering and existing prompt optimization baselines on functional correctness, code length, and/or generation cost across two coding-agent frameworks, four backbone LLMs, and three benchmarks; no single multiplicative factor is stated in the abstract.

   This is a Paperraft inference. It is not pinned to a verified PDF passage.

   Inspect the source