独立规划文档 · 生成于 2026-09-21 · 依据 overleaf-aiq/iclr2027_conference.tex(510 行版本)+ 中文施工稿 zh_draft2.md 的相关工作骨架
references.bib 均未被修改。经确认后再由人工/后续任务把选中的条目加入 overleaf-aiq。论文当前状态:§2 Related Work 是空节(第 109 行),§1 第 86 行有一个明确的
#TODO 一些参考文献,介绍 auto-research 和 self-evolve,§5.1 第 321 行有
#TODO cite PUCT,§5.2 正文提到 McNemar 检验但没有引用。除此之外,正文多处断言(专家直觉、
基准构造效度、优化器/架构不对称、反事实翻转、ICL/TTS 无效、知识压缩)都需要引用支撑。
本文件按“位置 → 引哪几篇 → 为什么”组织:第 3 节是逐条引用计划(含行号与原句摘录), 第 4 节是 2024–2026 年 RSI / AI4AI 基准速览,第 5 节是每篇文献的元数据与角色说明 (123 条,其中 117 条已通过 arXiv API 程序化验证)。 第 6 节说明验证方法与可信度,第 7 节给出加入论文时的操作步骤。
优先级说明:T1 必引(不引会被审稿人抓); T2 建议引(显著增强论证);T3 可选(讨论/附录/备选)。
| 行号 | 章节 | 内容 | 是否与引用相关 |
|---|---|---|---|
L86 | §1 Introduction | 介绍 auto-research 与 self-evolve 的参考文献 | 是(引用类) |
L125 | §3.2 Main Results | 解释 arch-only 与 optimizer-only 两类题的区别 | 否(解释类) |
L241 | §4.2 Test-time Scaling | 人类学习曲线(human learning curve) | 否(补实验) |
L306 | §4.4 Analytical Reasoning | 未写明内容 | 待确认 |
L321 | §5.1 Online Learning | cite PUCT(公式来源) | 是(引用类) |
L390 | §5.2 KB Snapshots | 展示一些 KB 条目 | 否(补展示) |
一句话:ArchitectureIQ 测的是一种“实验直觉”——在付出训练成本之前,能不能看着一份训练配方预判它的结果。 每个问题给一个合成数据集和若干完整训练配方(模型与架构、优化器与超参、损失、batch size、训练步数), 配方同时以自然语言和可执行的 Python 代码给出;被测模型要选出最终指标最好的那一个。 Ground truth 只能靠渲染并执行同一份代码得到,因此 prompt 里展示的代码就是被执行的代码。
这是一个生成式实验仪器:一方面提供固定的 500 题面板用于公平比较,另一方面同一套生成器可以造新题做行为分析。 作者的问题不是“谁分数高”,而是“模型关于神经网络训练到底内化了什么知识、这些知识能不能被外部化成可验证的文本命题”。
| 章节 | 行号 | 内容 | 待补 |
|---|---|---|---|
| §1 Introduction | L83–L107 | 动机、问题定义(“训练前能否判断哪个配方最好”)、三条贡献 | 有 TODO(L86 引用类) |
| §2 Related Work | L109 | 空节 | 整节待写 |
| §3 ArchitectureIQ: A Generative and Verifiable Probe | L111–L204 | 任务定义与构造、主结果与人类基线、架构/优化器不对称、数据集翻转、选项数扩展 | 有 TODO(L125 解释类) |
| §4 What Ability Is Essential For ArchitectureIQ? | L206–L306 | ICL 无效、TTS 无效、结构化基线、解析推理 | 有 TODO(L241、L306) |
| §5 Online Knowledge Accumulation | L308–L397 | 命题库闭环、快照评测、可压缩性与天花板 | 有 TODO(L321 引用类、L390) |
| §6 Discussion | L399–L400 | 空节 | 待写 |
| §7 Conclusion | L402 | 空节 | 待写 |
| Appendix A–D | L415–L508 | 构造细节、基线细节、完整结果、配对翻转评测 | — |
| 维度 | 内容 |
|---|---|
| 任务 | 每个问题给一个合成数据集 + 若干完整训练配方(模型与架构、优化器与超参、损失、batch size、训练步数),同时以自然语言和 Python 代码给出;模型必须在不执行的情况下选出最终指标最好的配方。 |
| Ground truth | 只能由渲染并执行同一份代码得到:spec → 渲染 .py → 导入运行 → 指标。prompt 里展示的代码就是被执行的代码(渲染前后都会同步一次)。 |
| 基准规模 | 固定面板 500 题,每题 N=3 个候选;赢家必须在 10 个随机种子下全胜,保证答案不受初始化随机性影响。 |
| 主结果 | 最强 GPT-5.6 Sol 76.4%,前沿模型约 76%,随机 33.3%,10 位人类研究者最高 61.1%;跨模型代际差距约 40 个百分点;最弱模型仍高于随机。 |
| 类型不对称 | 领先模型 optimizer 类题 85–87% 正确,而 architecture 类题只有 58–61%。(GPT-5.6 Sol 58/87/85,Claude Opus 5 61/86/81,按 arch/opt/mixed) |
| 数据集翻转 | 52 对形状匹配、执行后赢家相反的配对:合并准确率 .46–.50(对照集 .81–.95),模型只在一小部分配对上改变选择(6–23%)。 |
| 选项数扩展 | k=2,3,5,10 上拟合 acc(k) ≈ a + (1−a)/k,R² ≥ .94,渐近线 a 落在 0.35–0.39;k=10 时 top-1 降到 .391,但正确答案常仍在 top-3 短名单内。 |
| ICL 无效 | zero-shot vs 10-shot:Claude Opus 5 70.0→70.0,GPT-5.6 Luna 64.0→60.0,DeepSeek V4 Flash 62.0→65.0,DeepSeek V4 Pro 62.0→60.0;人类答题曲线也无随时间上升的信号。 |
| TTS 无效 | 同一批 50 题跑遍全部推理档位:GLM-5.2 从关闭思考的 54.0 跳到 low 档 68.8,再涨 1.7× token 反降到 65.3;Claude Opus 5 在 medium 档峰值 80.0,到 max 降到 74.0;答案改变率很低。 |
| 结构化基线 | 87 维规格特征 + histogram GBM(按数据集分 5 折)是最强基线;可读深度 4 树 .618;解析决策树(按有效学习率路由)OOF top-1 .782;附录 CART .650±.065。GBM 的 107 个 OOF 错误中,四个领先模型的多数投票能答对 43 个(占整个面板的 8.6%)。 |
| 残差构成 | optimizer–学习率交互 32.6%、无单规则解释(常为 bigram-LM 架构选择)30.2%、width–depth 权衡 27.9%、多因素交互 4.7%、参数量级 2.3%、训练稳定性 2.3%。 |
| 知识积累 | 每 epoch 从命题库注入最多 20 条命题(epoch = 50 道新题),按 Laplace 平滑成功率 + 探索项排序,由 curator 合并重复项;每 epoch 存一个快照。GPT-4o 是唯一到 snapshot 8 仍在上升的模型,最终快照相对匹配的 kb0 提升 14.4 个百分点(exact McNemar p=3.18e-10);最强 solver 在 snapshot 4 达峰 76%,未突破初始上限。 |
以上数字全部取自 iclr2027_conference.tex 正文与表格,未做任何外推。
论文标题当前仍是占位符(\title{ArchitectureIQ}),作者块是 ICLR 模板样例,尚未替换。
每个格子给出完整的 claim 原文与一句解释,不截断;点击编号可跳到第 3 节查看该位点的完整引用理由。
| 编号 | 位置 | 优先级 | 要支撑的 claim(完整) | 建议引用 |
|---|---|---|---|---|
| 1.1 | §1 Introduction L84 | T1 | Top machine learning researchers possess an esoteric intuition: before running an experiment, they can often inspect a change in architecture or training hyperparameters and anticipate how it will affect convergence, stability, and final performance. 解释:把“专家直觉”落到已有文献:超参数/架构确实主导结果,且不同超参数的重要性有量化排序(学习率、优化器 > 多数架构细节)。这样写的好处是让 AIQ 测的能力有明确出处,而不是自造概念。 | vanrijn2018hpi bischl2021hpo bengio2012practical |
| 1.2 | §1 Introduction L86(稿内 #TODO) | T1 | As language models are increasingly used to automate research workflows, they require a similar capability. [#TODO 一些参考文献,介绍 auto-research 和 self-evolve] 解释:这是稿子里明确留的 TODO:先交代 auto-research(AI Scientist 系列、AlphaEvolve)与 self-evolve(DGM、self-evolving 综述)的主线,再引出“它们都要求模型在动手前判断哪个实验值得做”。引用顺序建议:先系统类(AI Scientist / AlphaEvolve / DGM),后综述(TMLR 综述 + 2026 RSI 综述)收口。 | lu2024aiscientist yamada2025aiscientistv2 novikov2025alphaevolve zhang2025dgm tao2025selfevolving rsisurvey2026 |
| 1.3 | §1 Introduction L88 / L97 | T2 | We introduce ArchitectureIQ, a fully controlled and verifiable probe for studying this capability. … a static 500-question benchmark supporting fair model comparison 解释:一句话交代与既有 AI R&D / AI4AI 评测的区别:它们让 agent 真去跑实验(端到端执行),AIQ 冻结执行、只考“预测”。同时用 leakage 文献强调“可验证”不是修辞。 | mlsbench2026 wijk2024rebench kapoor2022leakage |
| 1.4 | §1 Introduction L99 | T2 | a best of 61% among 10 human machine learning researchers 解释:人类专家基线的先例(GPQA、HLE 都以专家准确率作参照)。在方法上说明“我们不是第一个拿专家当参照的”,减少 reviewer 对 10 人小样本的意外感。 | rein2023gpqa phan2025hle |
| 1.5 | §1 Introduction L103 | T2 | In-context learning and test-time scaling, surprisingly, does not reliably close these gaps 解释:与“更多思考通常更好”的推理时计算文献形成对照,提前给 §4.2 的零结果立靶子。 | wei2022cot snell2024testtime |
| 2.1 | §2 Related Work(第 1 段) L109(空节) | T1 | 【建议段落主题】训练结果预测:从 NAS / HPO 的表格化基准,到代理模型与 zero-cost proxy 解释:这条谱系回答“不训练就预测性能”从哪来:NAS 方法(RL/可微/一次性)→ 表格化可复现基准(NAS-Bench-101/201、Tabular Bench、HPOBench、YAHPO)→ 代理模型与 zero-cost proxy(BANANAS、predictors 评估、ZenNAS/NASWOT)→ 学习曲线外推。AIQ 的定位句:可以把它读成“以 LLM 为先验的 zero-cost proxy”,区别是输入为完整配方 + 可执行代码,输出是排序而非分数。 | zoph2017nasrl pham2018enas liu2019darts cai2020ofa ying2019nasbench101 dong2020nasbench201 klein2019tabular eggensperger2021hpobench pfisterer2022yahpo white2021predictors white2019bananas abdelfattah2021zerocost mellor2021naswot unterthiner2020predicting domhan2015learningcurves mohr2022learningcurves |
| 2.2 | §2 Related Work(第 2 段) L109(空节) | T1 | 【建议段落主题】LLM agent 做 ML 工程 / AI 研究自动化:测的是 agency,不是知识 解释:这些基准让 agent 真正执行实验(Kaggle 工程、仓库级代码、复现论文、长时程优化),度量的是“会不会做”。AIQ 度量“不做实验能否预判”,两者正交;把这一点写清楚,才能主张 AIQ 是 RSI/AI4AI 评测里缺的那块“实验直觉”拼图。 | chan2024mlebench huang2023mlagentbench nathani2025mlgym wijk2024rebench paperbench2025 chen2025mlrbench liang2024mlbench mlsbench2026 autolab2026 jimenez2024swebench metr2025timehorizon |
| 2.3 | §2 Related Work(第 3 段) L109(空节) | T1 | 【建议段落主题】RSI / 自我改进评测:从“改进系统”到“先验判断” 解释:2026 年 RSI 评测密集出现(AI4AI-Bench、RSIBench-Data、PostTrainBench),它们都要求 agent 闭环改进训练算法/数据/后训练;AIQ 提供上游能力探针:如果模型连“哪个配方更好”都判不准,闭环搜索的收益上限就受限。这一段是审稿人最可能追问“你和 RSI bench 什么关系”的地方。 | chi2026ai4aibench rsibenchdata2026 posttrainbench2026 rsisurvey2026 zhang2025dgm novikov2025alphaevolve lu2024aiscientist |
| 2.4 | §2 Related Work(第 4 段) L109(空节) | T1 | 【建议段落主题】最接近的工作:不执行就预测结果 解释:必须主动引用并划界:TuneAhead 用探针特征预测微调成败;Can-We-Predict 在数据报告上做方案偏好;How-Predictable 拟合历史实验记录。它们预测的是“分数/成败”,AIQ 要求在同一数据集的三份完整配方中排序,GT 由执行产生并带反事实翻转审计。LLM+HPO 类工作(AgentHPO、LLM-BO、OPRO、AutoML-GPT)是“让 LLM 提方案”,与“让 LLM 判断方案”互补。 | tuneahead2026 predictbefore2026 wang2023predictable liu2024agenthpo liu2024llmbo yang2023opro zhang2023automlgpt |
| 2.5 | §2 Related Work(第 5 段) L109(空节) | T1 | 【建议段落主题】基准的构造效度与鲁棒性批评:分数可能来自先验或伪影 解释:这条批评线(选项位置敏感、提示扰动、benchmark lottery、泄漏、欠定、shortcut、分布偏移)正是 AIQ 反事实实验的方法论来源:把“扰动文本”升级为“扰动 ground truth 本身”。写完这段,§3.4 的翻转实验就有了理论依据而不是孤例。 | hendrycks2021mmlu srivastava2022bigbench liang2022helm rein2023gpqa zheng2023mcqrobust sclar2023promptsensitivity dehghani2021benchmarklottery kapoor2022leakage damour2020underspecification geirhos2020shortcut miller2020distributionshift kaushik2020counterfactual ribeiro2020checklist |
| 2.6 | §2 Related Work(第 6 段) L109(空节) | T2 | 【建议段落主题】推理时计算、上下文学习与经验复用 解释:为 §4(ICL/TTS 均无效)和 §5(知识积累)铺路:既有方法多在“任务求解轨迹”上积累经验或做上下文优化;AIQ 积累的是可被 GT 验证的“关于训练结果的命题”,且要回答“强模型的能力能否文本化传给弱模型”。 | wei2022cot wang2022selfconsistency snell2024testtime muennighoff2025s1 guo2025deepseekr1 chen2024overthinking brown2024monkeys brown2020gpt3 min2022rethinking chan2022datadistributional shinn2023reflexion zhao2023expel zhang2025ace evolver2025 yuan2026skillopt ai4aittesttime2026 |
| 3.1 | §3.1 Benchmark Construction L115 | T2 | Importantly, the information presented in a question, in principle, fully determines the training outcome. 解释:“规格完备”是评测设计原则(HELM 的 holistic 设计、CheckList 的行为测试);引用它们可以让这句从断言变成有依据的设计选择。 | liang2022helm ribeiro2020checklist |
| 3.2 | §3.1 Benchmark Construction L119 | T1 | Ground truth is obtained only by importing and executing that exact generated code; no parallel implementation estimates the outcome. 解释:这是 AIQ 的核心不变量(spec → code → run → GT)。引用泄漏与 benchmark lottery 文献,说明“执行锚定”是在回应可复现性与隐性偏差问题。 | kapoor2022leakage dehghani2021benchmarklottery |
| 3.3 | §3.2 Main Results L137 | T3 | Performance also changes substantially across model generations 解释:跨代比较容易被解读成“涌现”;引用 mirage 一文提醒读者区分“能力出现”与“度量口径变化”,措辞更稳。 | schaeffer2023mirage |
| 3.4 | §3.2 Main Results L149 | T2 | The same evaluation is carried out on human machine-learning researchers. They completed the same prediction task and achieved a maximum of 61.1% accuracy. 解释:专家基线的方法学先例;同时方便在附录交代被试招募/协议时引用同类做法。 | rein2023gpqa phan2025hle glazer2024frontiermath |
| 3.5 | §3.3 Architecture Knowledge as the Weak Point L163-L165 | T1 | The leading models answer 85–87% of optimizer comparisons correctly but only 58–61% of architecture comparisons. … Optimizer comparisons frequently expose recognizable short-horizon patterns. 解释:解释这种不对称:优化器差异大多可归约为低维、单调、可解析的量(有效步长、自适应缩放、临界 batch),而架构差异是多因素交互。引用优化器文献让“为什么优化器题更容易”有机制解释,而不只是经验观察。 | wilson2017adaptive kingma2014adam loshchilov2017adamw smith2018dontdecay mccandlish2018largbatch |
| 3.6 | §3.4 Training Configuration Dominates Dataset Evidence L169-L175 | T1 | Our main intervention changes the dataset while holding the candidate recipes fixed. … accuracy falls to .46–.50 … Models change their selected candidate on only 6–23% of pairs. 解释:反事实与分布偏移的方法学基础:反事实数据(CAD)证明“换掉决定性因素”能暴露模型的真实依赖;自然分布偏移会显著掉点;shortcut/underspecification 解释“模型可以忽略证据而仍然得分”。这段是全文最有争议的结论,引用越扎实越好。 | kaushik2020counterfactual miller2020distributionshift geirhos2020shortcut damour2020underspecification |
| 3.7 | §3.5 Scaling with the Number of Choices L179-L200 | T2 | The models retain ranking signal as the search space grows, but their ability to identify the single winner degrades quickly. … At k=10, top-1 accuracy converges to .391, while the correct candidate often remains in the model's top-three shortlist. 解释:选项数与 MCQ 鲁棒性文献直接相关(选择偏置、位置偏置);top-3 短名单可用 best-of-n / 重复采样视角解释为“排序信号存在但被噪声淹没”。 | zheng2023mcqrobust brown2024monkeys |
| 4.1 | §4.1 Ineffective In-context Learning L216 | T1 | We tested zero-shot and 10-shot performance on a subsample of ArchitectureIQ. It turned out that few-shot prompting does not reliably improve performance, showing no signal of in-context learning. 解释:ICL 的经典结论与“示例到底提供了什么”的分析(Min et al.:示例主要提供格式与标签空间,而非输入-标签映射)。AIQ 上 10-shot 无效,正好与“ICL 依赖任务分布”的解释一致。 | brown2020gpt3 min2022rethinking chan2022datadistributional liu2022whatmakesgood |
| 4.2 | §4.2 Ineffective Test-time Scaling L246-L250 | T1 | Accuracy moves within the question-sampling noise … Longer traces often contain more detailed discussion, but they generally lead to the same final preference. 解释:TTS 在多数推理任务上有效(CoT、自洽、最优分配算力、s1、R1),AIQ 给出边界条件:当任务由先验主导时,额外 token 只是从同一分布重采样。overthinking 文献支持“更长≠更好”的观察,不是孤例。 | wei2022cot wang2022selfconsistency snell2024testtime muennighoff2025s1 guo2025deepseekr1 chen2024overthinking brown2024monkeys |
| 4.3 | §4.3 Rule-based and Structured Predictor Baselines L254 | T1 | Each question–candidate pair is represented by 87 features … The strongest baseline is a histogram gradient-boosting model evaluated with five folds grouped by dataset. 解释:“树模型在表格数据上是强基线”的既有结论(Grinsztajn 等),加上 GBM 原始文献;TabPFN 说明小样本表格任务上更现代的替代方案(可作为 baseline 备选)。这样 0.784 的基线强度不会被质疑成“故意做强/做弱”。 | grinsztajn2022tabular hollmann2022tabpfn friedman2001gbm |
| 4.3b | §4.3 Rule-based and Structured Predictor Baselines L264 | T2 | A readable depth-four tree reaches .618 using learning-rate rank, effective-step rank, and optimizer identity … An analytical decision tree … reaches OOF top-1 0.782 as a frozen rule set. 解释:有效学习率/临界 batch/学习率最重要——这些正是树模型前几个分裂点使用的量。引用后,“简单统计量就能到 .618”变成对既有理论的印证,而不是对基准的威胁。 | smith2018dontdecay mccandlish2018largbatch bengio2012practical vanrijn2018hpi |
| 4.4 | §4.4 Analytical Reasoning L304 | T2 | Some of these propositions are even semi-quantitative ones containing numerical range of where it holds, or calculables with symbolic expression. 解释:模型的自我解释未必忠实(Turpin、Lanham),所以 AIQ 用执行 GT 去验证命题而不是相信自述——这句话能顺手把 §5 的方法论正当性建立起来。 | turpin2023faithfulness lanham2023measuring wei2022cot |
| 5.1 | §5.1 Learning Verifiable Propositions Online L321(稿内 #TODO cite PUCT) | T1 | claims are ranked using a Laplace-smoothed success estimate plus an exploration bonus (#TODO cite PUCT): S = (s+1)/(s+f+2) + 0.3·min(1, sqrt(ln(1+N)/(1+s+f))) 解释:公式的两个来源:UCB1(探索项)与 PUCT(AlphaGo Zero / MCTS 综述)。建议正文只引 UCB1 + AlphaGo Zero,MCTS 综述放脚注即可。 | auer2002ucb silver2017alphagozero browne2012mcts |
| 5.1b | §5.1 Learning Verifiable Propositions Online L316-L320 | T1 | The solver may cite multiple existing propositions or propose new ones, assigning each with fractional credits adding up to 1. … Credit assigned to invoked propositions is added to their weighted success count. 解释:“分数信用分配 + 经验库更新”的方法谱系:credit assignment(RL)、verbal RL(Reflexion)、经验学习(ExpeL)、上下文进化(ACE、EvolveR)。AIQ 的差异在于命题由执行 GT 打分而非自我评价。 | mesnard2021credit shinn2023reflexion zhao2023expel zhang2025ace evolver2025 |
| 5.2 | §5.2 Evaluation Across KB Snapshots L329 / L387 | T1 | Claude Opus 5 peaks at 76% on snapshot 4, while GPT-4o is the only model still improving at snapshot 8. … The final snapshot improves over the matched empty-KB condition by 14.4 percentage points (exact McNemar p=3.18e-10). 解释:“强模型提取的文本知识能提升弱模型”正是 strong-to-weak 转移的近期工作(AI4AI at Test-Time、SkillOpt);检索式上下文(RAG、示例选择)是机制上的近亲。另:正文写了 McNemar 检验,需补统计方法引用。 | ai4aittesttime2026 yuan2026skillopt lewis2020rag liu2022whatmakesgood mcnemar1947 |
| 5.3 | §5.3 Compressibility Without a New Ceiling L392-L396 | T2 | At the same time, the KB does not reliably raise the strongest solver beyond its initial capability. The propositions were extracted from the reasoning of that solver. 解释:解释“为何不涨”:自我改进的锐化机制(Sharpening)说明自生成信号会放大已有先验而非引入新信息;SEAL/self-rewarding/ReST-EM 提供“自我改进有天花板”的对照。 | ye2024sharpening zweiger2025seal yuan2024selfrewarding singh2023restem |
| 6.1 | §6 Discussion / §7 Conclusion L399-L402 | T2 | (讨论节目前为空)建议:把 AIQ 放回 AI R&D / RSI 评测拼图,并明确它测的是“实验直觉”这一前置能力。 解释:讨论节需要向上抽象:AIQ 不是又一个 agent benchmark,而是给 RSI 研究提供“预测能力”这一维度的可验证读数。 | mlsbench2026 wijk2024rebench metr2025timehorizon rsisurvey2026 |
| 6.2 | §6 Discussion(limitations) L399-L402 | T2 | (建议在 limitations 中引用)合成数据家族、单一生成器分布、人类样本 n=10、跨生成器未测。 解释:局限段引用这些文献,把“我们知道自己可能被质疑什么”写实,比泛泛说“future work”更能通过审稿。 | kapoor2022leakage damour2020underspecification dehghani2021benchmarklottery |
| A.1 | 附录 A Benchmark Construction Details L416 | T3 | (构造细节)六族合成数据 + 10 seed 全胜闸门 + param ratio ≤ 2。 解释:表格化/多保真基准与行为测试的先例,用来支撑闸门与家族设计不是任意选择。 | klein2019tabular eggensperger2021hpobench ribeiro2020checklist |
为什么:把“专家直觉”落到已有文献:超参数/架构确实主导结果,且不同超参数的重要性有量化排序(学习率、优化器 > 多数架构细节)。这样写的好处是让 AIQ 测的能力有明确出处,而不是自造概念。
为什么:这是稿子里明确留的 TODO:先交代 auto-research(AI Scientist 系列、AlphaEvolve)与 self-evolve(DGM、self-evolving 综述)的主线,再引出“它们都要求模型在动手前判断哪个实验值得做”。引用顺序建议:先系统类(AI Scientist / AlphaEvolve / DGM),后综述(TMLR 综述 + 2026 RSI 综述)收口。
为什么:一句话交代与既有 AI R&D / AI4AI 评测的区别:它们让 agent 真去跑实验(端到端执行),AIQ 冻结执行、只考“预测”。同时用 leakage 文献强调“可验证”不是修辞。
为什么:人类专家基线的先例(GPQA、HLE 都以专家准确率作参照)。在方法上说明“我们不是第一个拿专家当参照的”,减少 reviewer 对 10 人小样本的意外感。
为什么:与“更多思考通常更好”的推理时计算文献形成对照,提前给 §4.2 的零结果立靶子。
为什么:这条谱系回答“不训练就预测性能”从哪来:NAS 方法(RL/可微/一次性)→ 表格化可复现基准(NAS-Bench-101/201、Tabular Bench、HPOBench、YAHPO)→ 代理模型与 zero-cost proxy(BANANAS、predictors 评估、ZenNAS/NASWOT)→ 学习曲线外推。AIQ 的定位句:可以把它读成“以 LLM 为先验的 zero-cost proxy”,区别是输入为完整配方 + 可执行代码,输出是排序而非分数。
为什么:这些基准让 agent 真正执行实验(Kaggle 工程、仓库级代码、复现论文、长时程优化),度量的是“会不会做”。AIQ 度量“不做实验能否预判”,两者正交;把这一点写清楚,才能主张 AIQ 是 RSI/AI4AI 评测里缺的那块“实验直觉”拼图。
为什么:2026 年 RSI 评测密集出现(AI4AI-Bench、RSIBench-Data、PostTrainBench),它们都要求 agent 闭环改进训练算法/数据/后训练;AIQ 提供上游能力探针:如果模型连“哪个配方更好”都判不准,闭环搜索的收益上限就受限。这一段是审稿人最可能追问“你和 RSI bench 什么关系”的地方。
为什么:必须主动引用并划界:TuneAhead 用探针特征预测微调成败;Can-We-Predict 在数据报告上做方案偏好;How-Predictable 拟合历史实验记录。它们预测的是“分数/成败”,AIQ 要求在同一数据集的三份完整配方中排序,GT 由执行产生并带反事实翻转审计。LLM+HPO 类工作(AgentHPO、LLM-BO、OPRO、AutoML-GPT)是“让 LLM 提方案”,与“让 LLM 判断方案”互补。
为什么:这条批评线(选项位置敏感、提示扰动、benchmark lottery、泄漏、欠定、shortcut、分布偏移)正是 AIQ 反事实实验的方法论来源:把“扰动文本”升级为“扰动 ground truth 本身”。写完这段,§3.4 的翻转实验就有了理论依据而不是孤例。
为什么:为 §4(ICL/TTS 均无效)和 §5(知识积累)铺路:既有方法多在“任务求解轨迹”上积累经验或做上下文优化;AIQ 积累的是可被 GT 验证的“关于训练结果的命题”,且要回答“强模型的能力能否文本化传给弱模型”。
为什么:“规格完备”是评测设计原则(HELM 的 holistic 设计、CheckList 的行为测试);引用它们可以让这句从断言变成有依据的设计选择。
为什么:这是 AIQ 的核心不变量(spec → code → run → GT)。引用泄漏与 benchmark lottery 文献,说明“执行锚定”是在回应可复现性与隐性偏差问题。
为什么:跨代比较容易被解读成“涌现”;引用 mirage 一文提醒读者区分“能力出现”与“度量口径变化”,措辞更稳。
为什么:专家基线的方法学先例;同时方便在附录交代被试招募/协议时引用同类做法。
为什么:解释这种不对称:优化器差异大多可归约为低维、单调、可解析的量(有效步长、自适应缩放、临界 batch),而架构差异是多因素交互。引用优化器文献让“为什么优化器题更容易”有机制解释,而不只是经验观察。
为什么:反事实与分布偏移的方法学基础:反事实数据(CAD)证明“换掉决定性因素”能暴露模型的真实依赖;自然分布偏移会显著掉点;shortcut/underspecification 解释“模型可以忽略证据而仍然得分”。这段是全文最有争议的结论,引用越扎实越好。
为什么:选项数与 MCQ 鲁棒性文献直接相关(选择偏置、位置偏置);top-3 短名单可用 best-of-n / 重复采样视角解释为“排序信号存在但被噪声淹没”。
为什么:ICL 的经典结论与“示例到底提供了什么”的分析(Min et al.:示例主要提供格式与标签空间,而非输入-标签映射)。AIQ 上 10-shot 无效,正好与“ICL 依赖任务分布”的解释一致。
为什么:TTS 在多数推理任务上有效(CoT、自洽、最优分配算力、s1、R1),AIQ 给出边界条件:当任务由先验主导时,额外 token 只是从同一分布重采样。overthinking 文献支持“更长≠更好”的观察,不是孤例。
为什么:“树模型在表格数据上是强基线”的既有结论(Grinsztajn 等),加上 GBM 原始文献;TabPFN 说明小样本表格任务上更现代的替代方案(可作为 baseline 备选)。这样 0.784 的基线强度不会被质疑成“故意做强/做弱”。
为什么:有效学习率/临界 batch/学习率最重要——这些正是树模型前几个分裂点使用的量。引用后,“简单统计量就能到 .618”变成对既有理论的印证,而不是对基准的威胁。
为什么:模型的自我解释未必忠实(Turpin、Lanham),所以 AIQ 用执行 GT 去验证命题而不是相信自述——这句话能顺手把 §5 的方法论正当性建立起来。
为什么:公式的两个来源:UCB1(探索项)与 PUCT(AlphaGo Zero / MCTS 综述)。建议正文只引 UCB1 + AlphaGo Zero,MCTS 综述放脚注即可。
为什么:“分数信用分配 + 经验库更新”的方法谱系:credit assignment(RL)、verbal RL(Reflexion)、经验学习(ExpeL)、上下文进化(ACE、EvolveR)。AIQ 的差异在于命题由执行 GT 打分而非自我评价。
为什么:“强模型提取的文本知识能提升弱模型”正是 strong-to-weak 转移的近期工作(AI4AI at Test-Time、SkillOpt);检索式上下文(RAG、示例选择)是机制上的近亲。另:正文写了 McNemar 检验,需补统计方法引用。
为什么:解释“为何不涨”:自我改进的锐化机制(Sharpening)说明自生成信号会放大已有先验而非引入新信息;SEAL/self-rewarding/ReST-EM 提供“自我改进有天花板”的对照。
为什么:讨论节需要向上抽象:AIQ 不是又一个 agent benchmark,而是给 RSI 研究提供“预测能力”这一维度的可验证读数。
为什么:局限段引用这些文献,把“我们知道自己可能被质疑什么”写实,比泛泛说“future work”更能通过审稿。
为什么:表格化/多保真基准与行为测试的先例,用来支撑闸门与家族设计不是任意选择。
这一节回答“这类工作别人做到哪了、AIQ 站在哪里”。全部条目的元数据已按 arXiv 核对;每条给出与 AIQ 的关系与建议引用位置。
| 基准 / 工作 | 时间 | 度量什么 | 与 AIQ 的关系 / 引用位置 |
|---|---|---|---|
MLS-BencharXiv:2605.08678 | 2026-05 | 140 个任务、12 个领域:agent 改进 ML 系统的某个组件,并证明改进在受控设置与更大规模下可泛化 | 与 AIQ 同属“让 AI 改进 AI”,但它要求真实执行实验;AIQ 冻结执行、只考预判。引用位置:§1 动机 + §2.2/§2.3。 |
AI4AI-BencharXiv:2608.20318 | 2026-08 | 10 个冻结研究仓库 / 10 个训练算法家族;agent 有 4 小时在单张 B300 上重写训练算法,代码随后从零重跑至多 12 小时并由隐藏评估器打分 | RSI 的直接评测。AIQ 度量的“架构/优化器判断力”正是它能否成功的前置条件——这是 §2.3 的核心论点。 |
RSIBench-DataarXiv:2607.25886 | 2026-07 | 以数据为中心的研究闭环评测(数据侧自我改进) | 与 AI4AI-Bench 互补覆盖数据轴;AIQ 覆盖配方(模型/优化器/损失/批量)轴。 |
PostTrainBencharXiv:2603.08640 | 2026-03 | 在受限算力(10 小时、单张 H100)下自动完成后训练 | 说明“算力受限的自动化研究”是当前共识问题;AIQ 的“预判能力”直接对应省预算。 |
AutoLabarXiv:2606.05080 | 2026-06 | 36 个专家构造的长时程任务(系统优化、模型开发、CUDA kernel 等);17 个前沿模型参与 | 其结论“成功主要取决于能否持续迭代并吸收实验反馈,而非初始方案质量”与 AIQ“先验主导”形成对照,值得在 §6 讨论。 |
PaperBencharXiv:2504.01848 | 2025-04 | 复现 AI 论文的 agent 基准(含 rubric 评分) | 执行型评测;与 AIQ 的“不执行预测”构成两种互补能力。 |
RE-BencharXiv:2411.15114 | 2024-11 | AI R&D 能力对照人类专家(含时间预算与人类基线) | 人类专家基线的方法学先例,支持 AIQ §3.2 的 61.1% 人类对照设计。 |
MLE-bencharXiv:2410.07095 | 2024-10 | Kaggle 级 ML 工程能力(模型训练、数据处理、提交) | 最常被拿来与 AIQ 并提的 ML agent 基准;必须显式划界(它考工程执行,不考判断)。 |
MLGym / MLAgentBencharXiv:2502.14499 | 2025-02 / 2023-10 | 面向 AI 研究 agent 的 gym 框架与 ML 实验基准 | 同属“agent 做 ML”一族,用于 §2.2 的谱系铺陈。 |
TuneAheadarXiv:2606.17660 | 2026-06 | 微调前预测性能:元特征(数据集描述 + 短探针动态特征)→ 性能估计;1300+ 次 Qwen2.5-7B 微调,held-out 370 次 RMSE 1.47 点 | 最接近的“预测而不执行”工作。差异:它是数值回归且需要一次短探针执行;AIQ 是零样本排序,输入只有规格与代码。必须引,见 §2.4。 |
Can We Predict Before Executing ML Agents?arXiv:2601.05930 | 2026-01 | 形式化 Data-centric Solution Preference,构建 18,438 对比较;LLM 在“已验证数据分析报告”条件下达 61.5% 准确率,并用于 Predict-then-Verify 循环(6× 加速) | 另一条最接近的线:它预测的是数据方案偏好且需要报告作输入;AIQ 的 GT 由执行产生并带反事实翻转审计。见 §2.4。 |
AI4AI at Test-TimearXiv:2608.12307 | 2026-08 | 强模型构造推理时 harness,把能力传给弱模型(strong-to-weak scaffolding) | 与 AIQ §5.2“强模型产出的命题能提升弱模型”直接对照,是知识传递章节的关键引用。 |
METR time horizonarXiv:2503.14499 | 2025-03 | 50% 任务完成时域指标;前沿模型时域约每 7 个月翻倍 | AI R&D 能力的宏观趋势参照,用于 §1/§6 定位 AIQ 的时间点。 |
每条含:标题、作者、venue/年份、arXiv/DOI 链接、验证状态、以及“它在本文里扮演什么角色”。点击 “BibTeX” 可展开条目。
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title = {Neural Architecture Search with Reinforcement Learning},
journal = {arXiv preprint arXiv:1611.01578},
year = {2016},
eprint = {1611.01578},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
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}@article{pham2018enas,
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title = {Efficient Neural Architecture Search via Parameter Sharing},
journal = {arXiv preprint arXiv:1802.03268},
year = {2018},
eprint = {1802.03268},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
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}@inproceedings{liu2019darts,
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title = {DARTS: Differentiable Architecture Search},
booktitle = {ICLR 2019},
year = {2018},
eprint = {1806.09055},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1806.09055},
note = {Code and pretrained models available at https://github.com/quark0/darts},
}@inproceedings{cai2020ofa,
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title = {Once-for-All: Train One Network and Specialize it for Efficient Deployment},
booktitle = {ICLR 2020},
year = {2019},
eprint = {1908.09791},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1908.09791},
}@article{ying2019nasbench101,
author = {Chris Ying and Aaron Klein and Esteban Real and Eric Christiansen and Kevin Murphy and Frank Hutter},
title = {NAS-Bench-101: Towards Reproducible Neural Architecture Search},
journal = {arXiv preprint arXiv:1902.09635},
year = {2019},
eprint = {1902.09635},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1902.09635},
}@inproceedings{dong2020nasbench201,
author = {Xuanyi Dong and Yi Yang},
title = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
booktitle = {ICLR 2020},
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archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2001.00326},
note = {As a spotlight paper; 16 pages; 10 figures; 7 tables; Code is available at https://gith…},
}@article{klein2019tabular,
author = {Aaron Klein and Frank Hutter},
title = {Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization},
journal = {arXiv preprint arXiv:1905.04970},
year = {2019},
eprint = {1905.04970},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1905.04970},
}@article{eggensperger2021hpobench,
author = {Katharina Eggensperger and Philipp Müller and Neeratyoy Mallik and Matthias Feurer and René Sass and Aaron Klein and Noor Awad and Marius Lindauer and Frank Hutter},
title = {HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO},
journal = {arXiv preprint arXiv:2109.06716},
year = {2021},
eprint = {2109.06716},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2109.06716},
}@article{pfisterer2022yahpo,
author = {Florian Pfisterer and Lennart Schneider and Julia Moosbauer and Martin Binder and Bernd Bischl},
title = {YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization},
journal = {arXiv preprint arXiv:2109.03670},
year = {2021},
eprint = {2109.03670},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2109.03670},
}@inproceedings{white2021predictors,
author = {Colin White and Arber Zela and Binxin Ru and Yang Liu and Frank Hutter},
title = {How Powerful are Performance Predictors in Neural Architecture Search?},
booktitle = {NeurIPS 2021},
year = {2021},
eprint = {2104.01177},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2104.01177},
}@inproceedings{white2019bananas,
author = {Colin White and Willie Neiswanger and Yash Savani},
title = {BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search},
booktitle = {AAAI Conference on Artificial Intelligence 2021},
year = {2019},
eprint = {1910.11858},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1910.11858},
}@inproceedings{abdelfattah2021zerocost,
author = {Mohamed S. Abdelfattah and Abhinav Mehrotra and Łukasz Dudziak and Nicholas D. Lane},
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primaryClass = {cs.LG},
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}@inproceedings{mellor2021naswot,
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title = {MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering},
journal = {arXiv preprint arXiv:2410.07095},
year = {2024},
eprint = {2410.07095},
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author = {Qian Huang and Jian Vora and Percy Liang and Jure Leskovec},
title = {MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation},
journal = {arXiv preprint arXiv:2310.03302},
year = {2023},
eprint = {2310.03302},
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title = {MLGym: A New Framework and Benchmark for Advancing AI Research Agents},
journal = {arXiv preprint arXiv:2502.14499},
year = {2025},
eprint = {2502.14499},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2502.14499},
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title = {RE-Bench: Evaluating frontier AI R\&D capabilities of language model agents against human experts},
journal = {arXiv preprint arXiv:2411.15114},
year = {2024},
eprint = {2411.15114},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2411.15114},
}@article{paperbench2025,
author = {Giulio Starace and Oliver Jaffe and Dane Sherburn and James Aung and Jun Shern Chan and Leon Maksin and Rachel Dias and Evan Mays and Benjamin Kinsella and Wyatt Thompson and Johannes Heidecke and Amelia Glaese and Tejal Patwardhan},
title = {PaperBench: Evaluating AI's Ability to Replicate AI Research},
journal = {arXiv preprint arXiv:2504.01848},
year = {2025},
eprint = {2504.01848},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2504.01848},
}@inproceedings{chen2025mlrbench,
author = {Hui Chen and Miao Xiong and Yujie Lu and Wei Han and Ailin Deng and Yufei He and Jiaying Wu and Yibo Li and Yue Liu and Bryan Hooi},
title = {MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research},
booktitle = {NeurIPS 2025},
year = {2025},
eprint = {2505.19955},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2505.19955},
}@article{liang2024mlbench,
author = {Xiangru Tang and Yuliang Liu and Zefan Cai and Yanjun Shao and Junjie Lu and Yichi Zhang and Zexuan Deng and Helan Hu and Kaikai An and Ruijun Huang and Shuzheng Si and Sheng Chen and Haozhe Zhao and Liang Chen and Yan Wang and Tianyu Liu and Zhiwei Jiang and Baobao Chang and Yin Fang and Yujia Qin and Wangchunshu Zhou and Yilun Zhao and Arman Cohan and Mark Gerstein},
title = {ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code},
journal = {arXiv preprint arXiv:2311.09835},
year = {2023},
eprint = {2311.09835},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2311.09835},
}@article{mlsbench2026,
author = {Bohan Lyu and Yucheng Yang and Siqiao Huang and Jiaru Zhang and Qixin Xu and Xinghan Li and Xinyang Han and Yicheng Zhang and Huaqing Zhang and Runhan Huang and Kaicheng Yang and Zitao Chen and Wentao Guo and Junlin Yang and Xinyue Ai and Wenhao Chai and Yadi Cao and Ziran Yang and Kun Wang and Dapeng Jiang and Huan-ang Gao and Shange Tang and Chengshuai Shi and Simon S. Du and Max Simchowitz and Jiantao Jiao and Dawn Song and Chi Jin},
title = {MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI},
journal = {arXiv preprint arXiv:2605.08678},
year = {2026},
eprint = {2605.08678},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2605.08678},
}@article{autolab2026,
author = {Zhangchen Xu and Junda Chen and Yue Huang and Dongfu Jiang and Jiefeng Chen and Hang Hua and Zijian Wu and Zheyuan Liu and Zexue He and Lichi Li and Shizhe Diao and Jiaxin Pei and Jinsung Yoon and Hao Zhang and Mengdi Wang and Radha Poovendran and Misha Sra and Alex Pentland and Zichen Chen},
title = {AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?},
journal = {arXiv preprint arXiv:2606.05080},
year = {2026},
eprint = {2606.05080},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2606.05080},
}@inproceedings{jimenez2024swebench,
author = {Carlos E. Jimenez and John Yang and Alexander Wettig and Shunyu Yao and Kexin Pei and Ofir Press and Karthik Narasimhan},
title = {SWE-bench: Can Language Models Resolve Real-World GitHub Issues?},
booktitle = {ICLR 2024},
year = {2023},
eprint = {2310.06770},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2310.06770},
note = {Code, and leaderboard are available at https://www.swebench.com , https://openreview.ne…},
}@inproceedings{metr2025timehorizon,
author = {Thomas Kwa and Ben West and Joel Becker and Amy Deng and Katharyn Garcia and Max Hasin and Sami Jawhar and Megan Kinniment and Nate Rush and Sydney Von Arx and Ryan Bloom and Thomas Broadley and Haoxing Du and Brian Goodrich and Nikola Jurkovic and Luke Harold Miles and Seraphina Nix and Tao Lin and Chris Painter and Neev Parikh and David Rein and Lucas Jun Koba Sato and Hjalmar Wijk and Daniel M. Ziegler and Elizabeth Barnes and Lawrence Chan},
title = {Measuring AI Ability to Complete Long Software Tasks},
booktitle = {NeurIPS 2025},
year = {2025},
eprint = {2503.14499},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2503.14499},
}@inproceedings{chen2024scienceagentbench,
author = {Ziru Chen and Shijie Chen and Yuting Ning and Qianheng Zhang and Boshi Wang and Botao Yu and Yifei Li and Zeyi Liao and Chen Wei and Zitong Lu and Vishal Dey and Mingyi Xue and Frazier N. Baker and Benjamin Burns and Daniel Adu-Ampratwum and Xuhui Huang and Xia Ning and Song Gao and Yu Su and Huan Sun},
title = {ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery},
booktitle = {ICLR 2025},
year = {2024},
eprint = {2410.05080},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2410.05080},
}@article{majumder2024discoverybench,
author = {Bodhisattwa Prasad Majumder and Harshit Surana and Dhruv Agarwal and Bhavana Dalvi Mishra and Abhijeetsingh Meena and Aryan Prakhar and Tirth Vora and Tushar Khot and Ashish Sabharwal and Peter Clark},
title = {DiscoveryBench: Towards Data-Driven Discovery with Large Language Models},
journal = {arXiv preprint arXiv:2407.01725},
year = {2024},
eprint = {2407.01725},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2407.01725},
}@article{chi2026ai4aibench,
author = {Yizhe Chi and Wenyi Li and Deyao Hong and Xiaoqiu Wang and Mingju Gao and Kaisen Yang and Bingxiang He and Youjie Zheng and Calvin Xiao and Qinhuai Na},
title = {AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement},
journal = {arXiv preprint arXiv:2608.20318},
year = {2026},
eprint = {2608.20318},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2608.20318},
}@article{rsibenchdata2026,
author = {Fanqing Meng and Lingxiao Du and Qiguang Chen and Ziqi Zhao and Haocheng Lu and Mengkang Hu and Michael Qizhe Shieh},
title = {RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement},
journal = {arXiv preprint arXiv:2607.25886},
year = {2026},
eprint = {2607.25886},
archivePrefix = {arXiv},
primaryClass = {cs.SE},
url = {https://arxiv.org/abs/2607.25886},
}@article{posttrainbench2026,
author = {Ben Rank and Hardik Bhatnagar and Ameya Prabhu and Shira Eisenberg and Karina Nguyen and Matthias Bethge and Maksym Andriushchenko},
title = {PostTrainBench: Can LLM Agents Automate LLM Post-Training?},
journal = {arXiv preprint arXiv:2603.08640},
year = {2026},
eprint = {2603.08640},
archivePrefix = {arXiv},
primaryClass = {cs.SE},
url = {https://arxiv.org/abs/2603.08640},
}@article{rsisurvey2026,
author = {Mingguang Chen and Licheng Wang and Bo Qu},
title = {Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops},
journal = {arXiv preprint arXiv:2607.07663},
year = {2026},
eprint = {2607.07663},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2607.07663},
}@article{tao2025selfevolving,
author = {Huan-ang Gao and Jiayi Geng and Wenyue Hua and Mengkang Hu and Xinzhe Juan and Hongzhang Liu and Shilong Liu and Jiahao Qiu and Xuan Qi and Yiran Wu and Hongru Wang and Han Xiao and Yuhang Zhou and Shaokun Zhang and Jiayi Zhang and Jinyu Xiang and Yixiong Fang and Qiwen Zhao and Dongrui Liu and Qihan Ren and Cheng Qian and Zhenhailong Wang and Minda Hu and Huazheng Wang and Qingyun Wu and Heng Ji and Mengdi Wang},
title = {A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence},
journal = {arXiv preprint arXiv:2507.21046},
year = {2025},
eprint = {2507.21046},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2507.21046},
}@article{zhang2025dgm,
author = {Jenny Zhang and Shengran Hu and Cong Lu and Robert Lange and Jeff Clune},
title = {Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents},
journal = {arXiv preprint arXiv:2505.22954},
year = {2025},
eprint = {2505.22954},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2505.22954},
}@inproceedings{yin2024godelagent,
author = {Xunjian Yin and Xinyi Wang and Liangming Pan and Li Lin and Xiaojun Wan and William Yang Wang},
title = {Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement},
booktitle = {ACL 2025},
year = {2024},
eprint = {2410.04444},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2410.04444},
note = {Main. The code can be found at https://github.com/Arvid-pku/Godel_Agent},
}@inproceedings{zelikman2023stop,
author = {Eric Zelikman and Eliana Lorch and Lester Mackey and Adam Tauman Kalai},
title = {Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation},
booktitle = {COLM 2024},
year = {2023},
eprint = {2310.02304},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2310.02304},
}@article{novikov2025alphaevolve,
author = {Alexander Novikov and Ngân Vũ and Marvin Eisenberger and Emilien Dupont and Po-Sen Huang and Adam Zsolt Wagner and Sergey Shirobokov and Borislav Kozlovskii and Francisco J. R. Ruiz and Abbas Mehrabian and M. Pawan Kumar and Abigail See and Swarat Chaudhuri and George Holland and Alex Davies and Sebastian Nowozin and Pushmeet Kohli and Matej Balog},
title = {AlphaEvolve: A coding agent for scientific and algorithmic discovery},
journal = {arXiv preprint arXiv:2506.13131},
year = {2025},
eprint = {2506.13131},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2506.13131},
}@article{lu2024aiscientist,
author = {Chris Lu and Cong Lu and Robert Tjarko Lange and Jakob Foerster and Jeff Clune and David Ha},
title = {The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery},
journal = {arXiv preprint arXiv:2408.06292},
year = {2024},
eprint = {2408.06292},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2408.06292},
}@article{yamada2025aiscientistv2,
author = {Yutaro Yamada and Robert Tjarko Lange and Cong Lu and Shengran Hu and Chris Lu and Jakob Foerster and Jeff Clune and David Ha},
title = {The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search},
journal = {arXiv preprint arXiv:2504.08066},
year = {2025},
eprint = {2504.08066},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2504.08066},
}@article{schmidgall2025agentlab,
author = {Samuel Schmidgall and Yusheng Su and Ze Wang and Ximeng Sun and Jialian Wu and Xiaodong Yu and Jiang Liu and Michael Moor and Zicheng Liu and Emad Barsoum},
title = {Agent Laboratory: Using LLM Agents as Research Assistants},
journal = {arXiv preprint arXiv:2501.04227},
year = {2025},
eprint = {2501.04227},
archivePrefix = {arXiv},
primaryClass = {cs.HC},
url = {https://arxiv.org/abs/2501.04227},
}@article{hu2024adas,
author = {Shengran Hu and Cong Lu and Jeff Clune},
title = {Automated Design of Agentic Systems},
journal = {arXiv preprint arXiv:2408.08435},
year = {2024},
eprint = {2408.08435},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2408.08435},
}@article{autoresearch2026,
author = {Xiang Liu and Qumeng Sun and Bruno and Xiao Zhang and Jiahao Li and Haoyang Zhang},
title = {AutoResearch: Insight In, Hallucination Out},
journal = {arXiv preprint arXiv:2608.17906},
year = {2026},
eprint = {2608.17906},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2608.17906},
}@article{mlevolve2026,
author = {Shangheng Du and Xiangchao Yan and Jinxin Shi and Zongsheng Cao and Shiyang Feng and Zichen Liang and Boyuan Sun and Tianshuo Peng and Yifan Zhou and Xin Li and Jie Zhou and Liang He and Bo Zhang and Lei Bai},
title = {MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery},
journal = {arXiv preprint arXiv:2606.06473},
year = {2026},
eprint = {2606.06473},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2606.06473},
}@article{execgrounded2026,
author = {Chenglei Si and Zitong Yang and Yejin Choi and Emmanuel Candès and Diyi Yang and Tatsunori Hashimoto},
title = {Towards Execution-Grounded Automated AI Research},
journal = {arXiv preprint arXiv:2601.14525},
year = {2026},
eprint = {2601.14525},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2601.14525},
}@inproceedings{tuneahead2026,
author = {Yuxiang Luo and Haonan Long and Chen Wang and Qiqi Duan and Xiaotian Lin and Yanwei Xu and Yuyu Luo and Weikai Yang and Nan Tang},
title = {TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins},
booktitle = {ICML 2026},
year = {2026},
eprint = {2606.17660},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2606.17660},
note = {9 pages, 6 figures, accepted as poster:https://icml.cc/virtual/2026/poster/64847},
}@inproceedings{predictbefore2026,
author = {Jingsheng Zheng and Jintian Zhang and Yujie Luo and Yuren Mao and Yunjun Gao and Lun Du and Huajun Chen and Ningyu Zhang},
title = {Can We Predict Before Executing Machine Learning Agents?},
booktitle = {ACL 2026},
year = {2026},
eprint = {2601.05930},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2601.05930},
}@inproceedings{wang2023predictable,
author = {Qinyuan Ye and Harvey Yiyun Fu and Xiang Ren and Robin Jia},
title = {How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench},
booktitle = {EMNLP 2023},
year = {2023},
eprint = {2305.14947},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2305.14947},
note = {Findings. Camera-ready version. Code: https://github.com/INK-USC/predicting-big-bench},
}@article{liu2024agenthpo,
author = {Siyi Liu and Chen Gao and Yong Li},
title = {Large Language Model Agent for Hyper-Parameter Optimization},
journal = {arXiv preprint arXiv:2402.01881},
year = {2024},
eprint = {2402.01881},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2402.01881},
}@inproceedings{liu2024llmbo,
author = {Tennison Liu and Nicolás Astorga and Nabeel Seedat and Mihaela van der Schaar},
title = {Large Language Models to Enhance Bayesian Optimization},
booktitle = {ICLR 2024},
year = {2024},
eprint = {2402.03921},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2402.03921},
}@inproceedings{yang2023opro,
author = {Chengrun Yang and Xuezhi Wang and Yifeng Lu and Hanxiao Liu and Quoc V. Le and Denny Zhou and Xinyun Chen},
title = {Large Language Models as Optimizers},
booktitle = {ICLR 2024},
year = {2023},
eprint = {2309.03409},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2309.03409},
note = {42 pages, 26 figures, 15 tables. Code at https://github.com/google-deepmind/opro},
}@article{zhang2023automlgpt,
author = {Shujian Zhang and Chengyue Gong and Lemeng Wu and Xingchao Liu and Mingyuan Zhou},
title = {AutoML-GPT: Automatic Machine Learning with GPT},
journal = {arXiv preprint arXiv:2305.02499},
year = {2023},
eprint = {2305.02499},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2305.02499},
}@inproceedings{hendrycks2021mmlu,
author = {Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
title = {Measuring Massive Multitask Language Understanding},
booktitle = {ICLR 2021},
year = {2020},
eprint = {2009.03300},
archivePrefix = {arXiv},
primaryClass = {cs.CY},
url = {https://arxiv.org/abs/2009.03300},
note = {The test and code is available at https://github.com/hendrycks/test},
}@article{srivastava2022bigbench,
author = {Aarohi Srivastava and Abhinav Rastogi and Abhishek Rao and Abu Awal Md Shoeb and Abubakar Abid and Adam Fisch and Adam R. Brown and Adam Santoro and Aditya Gupta and Adrià Garriga-Alonso and Agnieszka Kluska and Aitor Lewkowycz and Akshat Agarwal and Alethea Power and Alex Ray and Alex Warstadt and Alexander W. Kocurek and Ali Safaya and Ali Tazarv and Alice Xiang and Alicia Parrish and Allen Nie and Aman Hussain and Amanda Askell and Amanda Dsouza and Ambrose Slone and Ameet Rahane and Anantharaman S. Iyer and Anders Andreassen and Andrea Madotto and Andrea Santilli and Andreas Stuhlmüller and Andrew Dai and Andrew La and Andrew Lampinen and Andy Zou and Angela Jiang and Angelica Chen and Anh Vuong and Animesh Gupta and Anna Gottardi and Antonio Norelli and Anu Venkatesh and Arash Gholamidavoodi and Arfa Tabassum and Arul Menezes and Arun Kirubarajan and Asher Mullokandov and Ashish Sabharwal and Austin Herrick and Avia Efrat and Aykut Erdem and Ayla Karakaş and B. Ryan Roberts and Bao Sheng Loe and Barret Zoph and Bartłomiej Bojanowski and Batuhan Özyurt and Behnam Hedayatnia and Behnam Neyshabur and Benjamin Inden and Benno Stein and Berk Ekmekci and Bill Yuchen Lin and Blake Howald and Bryan Orinion and Cameron Diao and Cameron Dour and Catherine Stinson and Cedrick Argueta and César Ferri Ramírez and Chandan Singh and Charles Rathkopf and Chenlin Meng and Chitta Baral and Chiyu Wu and Chris Callison-Burch and Chris Waites and Christian Voigt and Christopher D. Manning and Christopher Potts and Cindy Ramirez and Clara E. Rivera and Clemencia Siro and Colin Raffel and Courtney Ashcraft and Cristina Garbacea and Damien Sileo and Dan Garrette and Dan Hendrycks and Dan Kilman and Dan Roth and Daniel Freeman and Daniel Khashabi and Daniel Levy and Daniel Moseguí González and Danielle Perszyk and Danny Hernandez and Danqi Chen and Daphne Ippolito and Dar Gilboa and David Dohan and David Drakard and David Jurgens and Debajyoti Datta and Deep Ganguli and Denis Emelin and Denis Kleyko and Deniz Yuret and Derek Chen and Derek Tam and Dieuwke Hupkes and Diganta Misra and Dilyar Buzan and Dimitri Coelho Mollo and Diyi Yang and Dong-Ho Lee and Dylan Schrader and Ekaterina Shutova and Ekin Dogus Cubuk and Elad Segal and Eleanor Hagerman and Elizabeth Barnes and Elizabeth Donoway and Ellie Pavlick and Emanuele Rodola and Emma Lam and Eric Chu and Eric Tang and Erkut Erdem and Ernie Chang and Ethan A. Chi and Ethan Dyer and Ethan Jerzak and Ethan Kim and Eunice Engefu Manyasi and Evgenii Zheltonozhskii and Fanyue Xia and Fatemeh Siar and Fernando Martínez-Plumed and Francesca Happé and Francois Chollet and Frieda Rong and Gaurav Mishra and Genta Indra Winata and Gerard de Melo and Germán Kruszewski and Giambattista Parascandolo and Giorgio Mariani and Gloria Wang and Gonzalo Jaimovitch-López and Gregor Betz and Guy Gur-Ari and Hana Galijasevic and Hannah Kim and Hannah Rashkin and Hannaneh Hajishirzi and Harsh Mehta and Hayden Bogar and Henry Shevlin and Hinrich Schütze and Hiromu Yakura and Hongming Zhang and Hugh Mee Wong and Ian Ng and Isaac Noble and Jaap Jumelet and Jack Geissinger and Jackson Kernion and Jacob Hilton and Jaehoon Lee and Jaime Fernández Fisac and James B. Simon and James Koppel and James Zheng and James Zou and Jan Kocoń and Jana Thompson and Janelle Wingfield and Jared Kaplan and Jarema Radom and Jascha Sohl-Dickstein and Jason Phang and Jason Wei and Jason Yosinski and Jekaterina Novikova and Jelle Bosscher and Jennifer Marsh and Jeremy Kim and Jeroen Taal and Jesse Engel and Jesujoba Alabi and Jiacheng Xu and Jiaming Song and Jillian Tang and Joan Waweru and John Burden and John Miller and John U. Balis and Jonathan Batchelder and Jonathan Berant and Jörg Frohberg and Jos Rozen and Jose Hernandez-Orallo and Joseph Boudeman and Joseph Guerr and Joseph Jones and Joshua B. Tenenbaum and Joshua S. Rule and Joyce Chua and Kamil Kanclerz and Karen Livescu and Karl Krauth and Karthik Gopalakrishnan and Katerina Ignatyeva and Katja Markert and Kaustubh D. Dhole and Kevin Gimpel and Kevin Omondi and Kory Mathewson and Kristen Chiafullo and Ksenia Shkaruta and Kumar Shridhar and Kyle McDonell and Kyle Richardson and Laria Reynolds and Leo Gao and Li Zhang and Liam Dugan and Lianhui Qin and Lidia Contreras-Ochando and Louis-Philippe Morency and Luca Moschella and Lucas Lam and Lucy Noble and Ludwig Schmidt and Luheng He and Luis Oliveros Colón and Luke Metz and Lütfi Kerem Şenel and Maarten Bosma and Maarten Sap and Maartje ter Hoeve and Maheen Farooqi and Manaal Faruqui and Mantas Mazeika and Marco Baturan and Marco Marelli and Marco Maru and Maria Jose Ramírez Quintana and Marie Tolkiehn and Mario Giulianelli and Martha Lewis and Martin Potthast and Matthew L. Leavitt and Matthias Hagen and Mátyás Schubert and Medina Orduna Baitemirova and Melody Arnaud and Melvin McElrath and Michael A. Yee and Michael Cohen and Michael Gu and Michael Ivanitskiy and Michael Starritt and Michael Strube and Michał Swędrowski and Michele Bevilacqua and Michihiro Yasunaga and Mihir Kale and Mike Cain and Mimee Xu and Mirac Suzgun and Mitch Walker and Mo Tiwari and Mohit Bansal and Moin Aminnaseri and Mor Geva and Mozhdeh Gheini and Mukund Varma T and Nanyun Peng and Nathan A. Chi and Nayeon Lee and Neta Gur-Ari Krakover and Nicholas Cameron and Nicholas Roberts and Nick Doiron and Nicole Martinez and Nikita Nangia and Niklas Deckers and Niklas Muennighoff and Nitish Shirish Keskar and Niveditha S. Iyer and Noah Constant and Noah Fiedel and Nuan Wen and Oliver Zhang and Omar Agha and Omar Elbaghdadi and Omer Levy and Owain Evans and Pablo Antonio Moreno Casares and Parth Doshi and Pascale Fung and Paul Pu Liang and Paul Vicol and Pegah Alipoormolabashi and Peiyuan Liao and Percy Liang and Peter Chang and Peter Eckersley and Phu Mon Htut and Pinyu Hwang and Piotr Miłkowski and Piyush Patil and Pouya Pezeshkpour and Priti Oli and Qiaozhu Mei and Qing Lyu and Qinlang Chen and Rabin Banjade and Rachel Etta Rudolph and Raefer Gabriel and Rahel Habacker and Ramon Risco and Raphaël Millière and Rhythm Garg and Richard Barnes and Rif A. Saurous and Riku Arakawa and Robbe Raymaekers and Robert Frank and Rohan Sikand and Roman Novak and Roman Sitelew and Ronan LeBras and Rosanne Liu and Rowan Jacobs and Rui Zhang and Ruslan Salakhutdinov and Ryan Chi and Ryan Lee and Ryan Stovall and Ryan Teehan and Rylan Yang and Sahib Singh and Saif M. Mohammad and Sajant Anand and Sam Dillavou and Sam Shleifer and Sam Wiseman and Samuel Gruetter and Samuel R. Bowman and Samuel S. Schoenholz and Sanghyun Han and Sanjeev Kwatra and Sarah A. Rous and Sarik Ghazarian and Sayan Ghosh and Sean Casey and Sebastian Bischoff and Sebastian Gehrmann and Sebastian Schuster and Sepideh Sadeghi and Shadi Hamdan and Sharon Zhou and Shashank Srivastava and Sherry Shi and Shikhar Singh and Shima Asaadi and Shixiang Shane Gu and Shubh Pachchigar and Shubham Toshniwal and Shyam Upadhyay and Shyamolima and Debnath and Siamak Shakeri and Simon Thormeyer and Simone Melzi and Siva Reddy and Sneha Priscilla Makini and Soo-Hwan Lee and Spencer Torene and Sriharsha Hatwar and Stanislas Dehaene and Stefan Divic and Stefano Ermon and Stella Biderman and Stephanie Lin and Stephen Prasad and Steven T. Piantadosi and Stuart M. Shieber and Summer Misherghi and Svetlana Kiritchenko and Swaroop Mishra and Tal Linzen and Tal Schuster and Tao Li and Tao Yu and Tariq Ali and Tatsu Hashimoto and Te-Lin Wu and Théo Desbordes and Theodore Rothschild and Thomas Phan and Tianle Wang and Tiberius Nkinyili and Timo Schick and Timofei Kornev and Titus Tunduny and Tobias Gerstenberg and Trenton Chang and Trishala Neeraj and Tushar Khot and Tyler Shultz and Uri Shaham and Vedant Misra and Vera Demberg and Victoria Nyamai and Vikas Raunak and Vinay Ramasesh and Vinay Uday Prabhu and Vishakh Padmakumar and Vivek Srikumar and William Fedus and William Saunders and William Zhang and Wout Vossen and Xiang Ren and Xiaoyu Tong and Xinran Zhao and Xinyi Wu and Xudong Shen and Yadollah Yaghoobzadeh and Yair Lakretz and Yangqiu Song and Yasaman Bahri and Yejin Choi and Yichi Yang and Yiding Hao and Yifu Chen and Yonatan Belinkov and Yu Hou and Yufang Hou and Yuntao Bai and Zachary Seid and Zhuoye Zhao and Zijian Wang and Zijie J. Wang and Zirui Wang and Ziyi Wu},
title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
journal = {Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj},
year = {2022},
eprint = {2206.04615},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2206.04615},
note = {27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-b…},
}@article{liang2022helm,
author = {Percy Liang and Rishi Bommasani and Tony Lee and Dimitris Tsipras and Dilara Soylu and Michihiro Yasunaga and Yian Zhang and Deepak Narayanan and Yuhuai Wu and Ananya Kumar and Benjamin Newman and Binhang Yuan and Bobby Yan and Ce Zhang and Christian Cosgrove and Christopher D. Manning and Christopher Ré and Diana Acosta-Navas and Drew A. Hudson and Eric Zelikman and Esin Durmus and Faisal Ladhak and Frieda Rong and Hongyu Ren and Huaxiu Yao and Jue Wang and Keshav Santhanam and Laurel Orr and Lucia Zheng and Mert Yuksekgonul and Mirac Suzgun and Nathan Kim and Neel Guha and Niladri Chatterji and Omar Khattab and Peter Henderson and Qian Huang and Ryan Chi and Sang Michael Xie and Shibani Santurkar and Surya Ganguli and Tatsunori Hashimoto and Thomas Icard and Tianyi Zhang and Vishrav Chaudhary and William Wang and Xuechen Li and Yifan Mai and Yuhui Zhang and Yuta Koreeda},
title = {Holistic Evaluation of Language Models},
journal = {Published in Transactions on Machine Learning Research (TMLR), 2023},
year = {2022},
eprint = {2211.09110},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2211.09110},
note = {Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institu…},
}@article{rein2023gpqa,
author = {David Rein and Betty Li Hou and Asa Cooper Stickland and Jackson Petty and Richard Yuanzhe Pang and Julien Dirani and Julian Michael and Samuel R. Bowman},
title = {GPQA: A Graduate-Level Google-Proof Q\&A Benchmark},
journal = {arXiv preprint arXiv:2311.12022},
year = {2023},
eprint = {2311.12022},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2311.12022},
}@article{phan2025hle,
author = {Long Phan and Alice Gatti and Ziwen Han and Nathaniel Li and Josephina Hu and Hugh Zhang and Chen Bo Calvin Zhang and Mohamed Shaaban and John Ling and Sean Shi and Michael Choi and Anish Agrawal and Arnav Chopra and Adam Khoja and Ryan Kim and Richard Ren and Jason Hausenloy and Oliver Zhang and Mantas Mazeika and Dmitry Dodonov and Tung Nguyen and Jaeho Lee and Daron Anderson and Mikhail Doroshenko and Alun Cennyth Stokes and Mobeen Mahmood and Oleksandr Pokutnyi and Oleg Iskra and Jessica P. Wang and John-Clark Levin and Mstyslav Kazakov and Fiona Feng and Steven Y. Feng and Haoran Zhao and Michael Yu and Varun Gangal and Chelsea Zou and Zihan Wang and Serguei Popov and Robert Gerbicz and Geoff Galgon and Johannes Schmitt and Will Yeadon and Yongki Lee and Scott Sauers and Alvaro Sanchez and Fabian Giska and Marc Roth and Søren Riis and Saiteja Utpala and Noah Burns and Gashaw M. Goshu and Mohinder Maheshbhai Naiya and Chidozie Agu and Zachary Giboney and Antrell Cheatom and Francesco Fournier-Facio and Sarah-Jane Crowson and Lennart Finke and Zerui Cheng and Jennifer Zampese and Ryan G. Hoerr and Mark Nandor and Hyunwoo Park and Tim Gehrunger and Jiaqi Cai and Ben McCarty and Alexis C Garretson and Edwin Taylor and Damien Sileo and Qiuyu Ren and Usman Qazi and Lianghui Li and Jungbae Nam and John B. Wydallis and Pavel Arkhipov and Jack Wei Lun Shi and Aras Bacho and Chris G. Willcocks and Hangrui Cao and Sumeet Motwani and Emily de Oliveira Santos and Johannes Veith and Edward Vendrow and Doru Cojoc and Kengo Zenitani and Joshua Robinson and Longke Tang and Yuqi Li and Joshua Vendrow and Natanael Wildner Fraga and Vladyslav Kuchkin and Andrey Pupasov Maksimov and Pierre Marion and Denis Efremov and Jayson Lynch and Kaiqu Liang and Aleksandar Mikov and Andrew Gritsevskiy and Julien Guillod and Gözdenur Demir and Dakotah Martinez and Ben Pageler and Kevin Zhou and Saeed Soori and Ori Press and Henry Tang and Paolo Rissone and Sean R. Green and Lina Brüssel and Moon Twayana and Aymeric Dieuleveut and Joseph Marvin Imperial and Ameya Prabhu and Jinzhou Yang and Nick Crispino and Arun Rao and Dimitri Zvonkine and Gabriel Loiseau and Mikhail Kalinin and Marco Lukas and Ciprian Manolescu and Nate Stambaugh and Subrata Mishra and Tad Hogg and Carlo Bosio and Brian P Coppola and Julian Salazar and Jaehyeok Jin and Rafael Sayous and Stefan Ivanov and Philippe Schwaller and Shaipranesh Senthilkuma and Andres M Bran and Andres Algaba and Kelsey Van den Houte and Lynn Van Der Sypt and Brecht Verbeken and David Noever and Alexei Kopylov and Benjamin Myklebust and Bikun Li and Lisa Schut and Evgenii Zheltonozhskii and Qiaochu Yuan and Derek Lim and Richard Stanley and Tong Yang and John Maar and Julian Wykowski and Martí Oller and Anmol Sahu and Cesare Giulio Ardito and Yuzheng Hu and Ariel Ghislain Kemogne Kamdoum and Alvin Jin and Tobias Garcia Vilchis and Yuexuan Zu and Martin Lackner and James Koppel and Gongbo Sun and Daniil S. Antonenko and Steffi Chern and Bingchen Zhao and Pierrot Arsene and Joseph M Cavanagh and Daofeng Li and Jiawei Shen and Donato Crisostomi and Wenjin Zhang and Ali Dehghan and Sergey Ivanov and David Perrella and Nurdin Kaparov and Allen Zang and Ilia Sucholutsky and Arina Kharlamova and Daniil Orel and Vladislav Poritski and Shalev Ben-David and Zachary Berger and Parker Whitfill and Michael Foster and Daniel Munro and Linh Ho and Shankar Sivarajan and Dan Bar Hava and Aleksey Kuchkin and David Holmes and Alexandra Rodriguez-Romero and Frank Sommerhage and Anji Zhang and Richard Moat and Keith Schneider and Zakayo Kazibwe and Don Clarke and Dae Hyun Kim and Felipe Meneguitti Dias and Sara Fish and Veit Elser and Tobias Kreiman and Victor Efren Guadarrama Vilchis and Immo Klose and Ujjwala Anantheswaran and Adam Zweiger and Kaivalya Rawal and Jeffery Li and Jeremy Nguyen and Nicolas Daans and Haline Heidinger and Maksim Radionov and Václav Rozhoň and Vincent Ginis and Christian Stump and Niv Cohen and Rafał Poświata and Josef Tkadlec and Alan Goldfarb and Chenguang Wang and Piotr Padlewski and Stanislaw Barzowski and Kyle Montgomery and Ryan Stendall and Jamie Tucker-Foltz and Jack Stade and T. Ryan Rogers and Tom Goertzen and Declan Grabb and Abhishek Shukla and Alan Givré and John Arnold Ambay and Archan Sen and Muhammad Fayez Aziz and Mark H Inlow and Hao He and Ling Zhang and Younesse Kaddar and Ivar Ängquist and Yanxu Chen and Harrison K Wang and Kalyan Ramakrishnan and Elliott Thornley and Antonio Terpin and Hailey Schoelkopf and Eric Zheng and Avishy Carmi and Ethan D. L. Brown and Kelin Zhu and Max Bartolo and Richard Wheeler and Martin Stehberger and Peter Bradshaw and JP Heimonen and Kaustubh Sridhar and Ido Akov and Jennifer Sandlin and Yury Makarychev and Joanna Tam and Hieu Hoang and David M. Cunningham and Vladimir Goryachev and Demosthenes Patramanis and Michael Krause and Andrew Redenti and David Aldous and Jesyin Lai and Shannon Coleman and Jiangnan Xu and Sangwon Lee and Ilias Magoulas and Sandy Zhao and Ning Tang and Michael K. Cohen and Orr Paradise and Jan Hendrik Kirchner and Maksym Ovchynnikov and Jason O. Matos and Adithya Shenoy and Michael Wang and Yuzhou Nie and Anna Sztyber-Betley and Paolo Faraboschi and Robin Riblet and Jonathan Crozier and Shiv Halasyamani and Shreyas Verma and Prashant Joshi and Eli Meril and Ziqiao Ma and Jérémy Andréoletti and Raghav Singhal and Jacob Platnick and Volodymyr Nevirkovets and Luke Basler and Alexander Ivanov and Seri Khoury and Nils Gustafsson and Marco Piccardo and Hamid Mostaghimi and Qijia Chen and Virendra Singh and Tran Quoc Khánh and Paul Rosu and Hannah Szlyk and Zachary Brown and Himanshu Narayan and Aline Menezes and Jonathan Roberts and William Alley and Kunyang Sun and Arkil Patel and Max Lamparth and Anka Reuel and Linwei Xin and Hanmeng Xu and Jacob Loader and Freddie Martin and Zixuan Wang and Andrea Achilleos and Thomas Preu and Tomek Korbak and Ida Bosio and Fereshteh Kazemi and Ziye Chen and Biró Bálint and Eve J. Y. Lo and Jiaqi Wang and Maria Inês S. Nunes and Jeremiah Milbauer and M Saiful Bari and Zihao Wang and Behzad Ansarinejad and Yewen Sun and Stephane Durand and Hossam Elgnainy and Guillaume Douville and Daniel Tordera and George Balabanian and Hew Wolff and Lynna Kvistad and Hsiaoyun Milliron and Ahmad Sakor and Murat Eron and Andrew Favre D. O. and Shailesh Shah and Xiaoxiang Zhou and Firuz Kamalov and Sherwin Abdoli and Tim Santens and Shaul Barkan and Allison Tee and Robin Zhang and Alessandro Tomasiello and G. Bruno De Luca and Shi-Zhuo Looi and Vinh-Kha Le and Noam Kolt and Jiayi Pan and Emma Rodman and Jacob Drori and Carl J Fossum and Niklas Muennighoff and Milind Jagota and Ronak Pradeep and Honglu Fan and Jonathan Eicher and Michael Chen and Kushal Thaman and William Merrill and Moritz Firsching and Carter Harris and Stefan Ciobâcă and Jason Gross and Rohan Pandey and Ilya Gusev and Adam Jones and Shashank Agnihotri and Pavel Zhelnov and Mohammadreza Mofayezi and Alexander Piperski and David K. 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archivePrefix = {arXiv},
primaryClass = {cs.CL},
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title = {Reflexion: Language Agents with Verbal Reinforcement Learning},
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title = {ExpeL: LLM Agents Are Experiential Learners},
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year = {2023},
eprint = {2308.10144},
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title = {Voyager: An Open-Ended Embodied Agent with Large Language Models},
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year = {2023},
eprint = {2305.16291},
archivePrefix = {arXiv},
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url = {https://arxiv.org/abs/2305.16291},
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booktitle = {ICLR 2026},
year = {2025},
eprint = {2510.04618},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2510.04618},
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title = {EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle},
booktitle = {ICML 2026},
year = {2025},
eprint = {2510.16079},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2510.16079},
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author = {Wujiang Xu and Zujie Liang and Kai Mei and Hang Gao and Juntao Tan and Yongfeng Zhang},
title = {A-MEM: Agentic Memory for LLM Agents},
booktitle = {NeurIPS 2025},
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archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2502.12110},
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title = {MemoryBank: Enhancing Large Language Models with Long-Term Memory},
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year = {2023},
eprint = {2305.10250},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2305.10250},
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title = {SkillOpt: Executive Strategy for Self-Evolving Agent Skills},
journal = {arXiv preprint arXiv:2605.23904},
year = {2026},
eprint = {2605.23904},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2605.23904},
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title = {AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses},
journal = {arXiv preprint arXiv:2608.12307},
year = {2026},
eprint = {2608.12307},
archivePrefix = {arXiv},
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year = {2023},
eprint = {2310.03714},
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title = {STaR: Bootstrapping Reasoning With Reasoning},
journal = {arXiv preprint arXiv:2203.14465},
year = {2022},
eprint = {2203.14465},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2203.14465},
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author = {Eric Zelikman and Georges Harik and Yijia Shao and Varuna Jayasiri and Nick Haber and Noah D. Goodman},
title = {Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking},
journal = {arXiv preprint arXiv:2403.09629},
year = {2024},
eprint = {2403.09629},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2403.09629},
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year = {2023},
eprint = {2310.04406},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2310.04406},
}@article{wu2024metarewarding,
author = {Tianhao Wu and Weizhe Yuan and Olga Golovneva and Jing Xu and Yuandong Tian and Jiantao Jiao and Jason Weston and Sainbayar Sukhbaatar},
title = {Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge},
journal = {arXiv preprint arXiv:2407.19594},
year = {2024},
eprint = {2407.19594},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2407.19594},
}@article{singh2023restem,
author = {Avi Singh and John D. Co-Reyes and Rishabh Agarwal and Ankesh Anand and Piyush Patil and Xavier Garcia and Peter J. Liu and James Harrison and Jaehoon Lee and Kelvin Xu and Aaron Parisi and Abhishek Kumar and Alex Alemi and Alex Rizkowsky and Azade Nova and Ben Adlam and Bernd Bohnet and Gamaleldin Elsayed and Hanie Sedghi and Igor Mordatch and Isabelle Simpson and Izzeddin Gur and Jasper Snoek and Jeffrey Pennington and Jiri Hron and Kathleen Kenealy and Kevin Swersky and Kshiteej Mahajan and Laura Culp and Lechao Xiao and Maxwell L. Bileschi and Noah Constant and Roman Novak and Rosanne Liu and Tris Warkentin and Yundi Qian and Yamini Bansal and Ethan Dyer and Behnam Neyshabur and Jascha Sohl-Dickstein and Noah Fiedel},
title = {Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models},
journal = {arXiv preprint arXiv:2312.06585},
year = {2023},
eprint = {2312.06585},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2312.06585},
}@article{huang2022selfimprove,
author = {Jiaxin Huang and Shixiang Shane Gu and Le Hou and Yuexin Wu and Xuezhi Wang and Hongkun Yu and Jiawei Han},
title = {Large Language Models Can Self-Improve},
journal = {arXiv preprint arXiv:2210.11610},
year = {2022},
eprint = {2210.11610},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2210.11610},
}@inproceedings{wang2022selfinstruct,
author = {Yizhong Wang and Yeganeh Kordi and Swaroop Mishra and Alisa Liu and Noah A. Smith and Daniel Khashabi and Hannaneh Hajishirzi},
title = {Self-Instruct: Aligning Language Models with Self-Generated Instructions},
booktitle = {ACL 2023},
year = {2022},
eprint = {2212.10560},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2212.10560},
}@inproceedings{lee2023rlaif,
author = {Harrison Lee and Samrat Phatale and Hassan Mansoor and Thomas Mesnard and Johan Ferret and Kellie Lu and Colton Bishop and Ethan Hall and Victor Carbune and Abhinav Rastogi and Sushant Prakash},
title = {RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback},
booktitle = {Proceedings of the 41st International Conference on Machine Learning, PMLR 235:26874-26901, 2024},
year = {2023},
eprint = {2309.00267},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2309.00267},
}@inproceedings{chen2024spin,
author = {Zixiang Chen and Yihe Deng and Huizhuo Yuan and Kaixuan Ji and Quanquan Gu},
title = {Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models},
booktitle = {ICML 2024},
year = {2024},
eprint = {2401.01335},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2401.01335},
}@article{zweiger2025seal,
author = {Adam Zweiger and Jyothish Pari and Han Guo and Ekin Akyürek and Yoon Kim and Pulkit Agrawal},
title = {Self-Adapting Language Models},
journal = {arXiv preprint arXiv:2506.10943},
year = {2025},
eprint = {2506.10943},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2506.10943},
}@article{ye2024sharpening,
author = {Audrey Huang and Adam Block and Dylan J. Foster and Dhruv Rohatgi and Cyril Zhang and Max Simchowitz and Jordan T. Ash and Akshay Krishnamurthy},
title = {Self-Improvement in Language Models: The Sharpening Mechanism},
journal = {arXiv preprint arXiv:2412.01951},
year = {2024},
eprint = {2412.01951},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2412.01951},
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author = {Patrick Lewis and Ethan Perez and Aleksandra Piktus and Fabio Petroni and Vladimir Karpukhin and Naman Goyal and Heinrich Küttler and Mike Lewis and Wen-tau Yih and Tim Rocktäschel and Sebastian Riedel and Douwe Kiela},
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booktitle = {NeurIPS 2020},
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eprint = {1412.6980},
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author = {Ilya Loshchilov and Frank Hutter},
title = {Decoupled Weight Decay Regularization},
booktitle = {ICLR 2019},
year = {2017},
eprint = {1711.05101},
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author = {Ashia C. Wilson and Rebecca Roelofs and Mitchell Stern and Nathan Srebro and Benjamin Recht},
title = {The Marginal Value of Adaptive Gradient Methods in Machine Learning},
journal = {arXiv preprint arXiv:1705.08292},
year = {2017},
eprint = {1705.08292},
archivePrefix = {arXiv},
primaryClass = {stat.ML},
url = {https://arxiv.org/abs/1705.08292},
}@inproceedings{smith2018dontdecay,
author = {Samuel L. Smith and Pieter-Jan Kindermans and Chris Ying and Quoc V. Le},
title = {Don't Decay the Learning Rate, Increase the Batch Size},
booktitle = {ICLR 2018},
year = {2017},
eprint = {1711.00489},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1711.00489},
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author = {Sam McCandlish and Jared Kaplan and Dario Amodei and OpenAI Dota Team},
title = {An Empirical Model of Large-Batch Training},
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author = {Jared Kaplan and Sam McCandlish and Tom Henighan and Tom B. Brown and Benjamin Chess and Rewon Child and Scott Gray and Alec Radford and Jeffrey Wu and Dario Amodei},
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}方法:所有 arXiv 条目由 refs_work/verify_refs.py 通过 arXiv Atom API 按标题精确检索
(ti:"...",取最相似命中并计算标题相似度),抓取时间 2026-09-21。相似度 ≥ 0.85 记为已验证;
低相似度标 待人工复核;API 无命中的条目单独人工处理。
局限:(1) arXiv 元数据以预印本为准,venue 信息来自 journal_ref/comment 字段,
正式发表信息(尤其 2026 年的会议)需要人工二次确认;(2) 非 arXiv 条目(UCB1、McNemar、GBM)为人工核对;
(3) 本文件只保证“论文存在且元数据一致”,不保证“引用位置最优”——第 2 节的位点判断是写作建议,需要作者拍板。
复现:cd data/evals/paper_exp/paper_draft/refs_work && python3 verify_refs.py
(幂等、可断点续跑,结果写入 arxiv_verified.json;再运行 python3 build_doc.py 重新生成本文档与
references_verified.bib)。
refs_work/references_verified.bib,但先核对 2026 年会议的 venue 字段。\cite 插到第 2 节计划标注的位置,替换 iclr2027_conference.bib 或新增
references.bib(注意该仓库 AGENTS.md 的 push 预览规则)。tools/validate_quiz_bake.py 无关;论文侧只需 latexmk 编译通过 + 引用无 undefined。