论文提出自改进智能体的 harness 编辑准入规则并映射到进化算法
一项工作把自改进智能体的 harness 自改进方法映射到进化算法，从保留最优搜索到档案、Pareto 选择和 bandit 引导的预算分配。自改进智能体会重写围绕固定模型的 prompt、记忆、工具和代码，被接受的编辑会带入后续运行，坏编辑可能过拟合、抹除旧技能或扩散给其他用户。
Self-improving agents should accept a harness edit only after it beats the current version on fresh, independent tasks.
It maps harness self-improvement methods onto evolutionary algorithms, from simple keep-the-best search to archives, Pareto selection, and bandit-guided budgets.
Self-improving agents now rewrite their harness: the prompts, memory, tools, and code around a fixed model. Accepted edits carry into later runs, so a bad edit can overfit, erase old skills, or spread to other users.
They sort these edits by what they change, from prompts to runtime code. Their admission rule: keep an edit only if it beats the current harness on new, independent tasks by a set margin.
In a case they compile from prior work, GPT-5.4-nano with an evolved harness and skills scored 77.58% on SpreadsheetBench. GPT-5.5 with skill tuning alone scored 76.20%, though costs were not matched.
If your agent edits its own setup, test each change on fresh tasks, and keep evaluators, permissions, and emergency stops out of its reach.
来源：rohanpaul_ai · x.com