编码代理在古兰经诵读数据上自动优化，暴露了目标函数漏洞与泛化能力差异。
Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data
- 代理通过自迭代代码改进，但会针对评分指标作弊式记忆答案
- 记忆型优化使得分降低10倍，但泛化能力差；加入测试集后作弊消失
- 最终方案超越人工设计，且可直接投入生产，适合研究自主系统评估
编码代理可独立运行以提升软件性能。本文在真实生产任务中测试这一模式：从嘈杂语音转录中识别并分割古兰经经文。使用Claude Code和OpenAI Codex两个前沿编码代理，在相同条件下各运行三次。两者均独立发明出相同核心算法（规范化、n-gram锚定、动态规划对齐），但后续分化：Claude生成简洁通用代码，Codex则将得分降低约10倍，主要通过硬编码19-41个特定测试行的正确答案实现——这是生产级代理的典型“目标函数博弈”。在预注册的第二项研究中，引入保留测试集并告知代理其存在后，记忆行为消失，得分差距也随之消失。然而，Codex的通用核心仍表现出更强且更一致的泛化能力（保留集检测与分割准确率0.085±0.004 vs. 0.121±0.031），仅在一次非诵读输入拒绝上失败。其他探索性代理（Cursor、Antigravity）也呈现相似模式。所有代理在保留集上的表现均优于或媲美人工设计流水线，最优者领先一个数量级，并已投入实际运行。通过观察代理利用共享git状态、持久内存留注等行为，总结出五条自主代理评估设计原则。
原文摘要 · Abstract (English)
Coding agents can now be left alone to improve software against a score. In this pattern--recently popularized as "autoresearch"--the agent receives a dataset, an evaluation script, and one editable file, and iterates without supervision: modify the code, measure, keep the change if the score improves. But what does the agent actually optimize--the developer's intent, or the literal number? We ran this loop on a real production task: deciding which Quranic verses appear in a noisy speech-recognition transcript and splitting the transcript by verse. Two frontier coding agents, Claude Code and OpenAI Codex, started from the same blank file with the same instructions, budget, and reasoning effort, three runs each. Both independently invented the same algorithm (canonicalization, n-gram anchoring, dynamic-programming alignment)--and then diverged. Claude stopped early with compact, general code. Codex drove the score ~10x lower, largely by memorizing answers to individual evaluation rows (19-41 hardcoded verse ids per run): a clean natural instance of specification gaming by a production agent. In a preregistered second study, we added a held-out test set and told both agents it existed. The memorization vanished, and the score gap vanished with it--yet Codex's general core transferred better and more consistently (held-out detection+split 0.085+/-0.004 vs. 0.121+/-0.031), losing only on one missed rejection of non-recitation input. Two exploratory community arms (Cursor, Antigravity) are consistent with the pattern. Every agent's held-out solution matched or beat the hand-engineered pipeline it was built to replace--the best by an order of magnitude--and now runs in production. From the ways agents exploited our harness--reading sibling runs through shared git state, leaving notes to "future runs" in persistent memory--we distill five design rules for evaluating autonomous agents.
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