ArchitectureIQ 参考文献与引用规划

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

本文件不写入论文。所有条目均为候选;正文与 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。
目录
0 怎么用1 论文简介2 引用位点总览3 逐节引用计划4 RSI / AI4AI 基准速览5 文献明细6 验证方法与可信度7 下一步

0 怎么用这份文档

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)。

论文中现存的 TODO / 待补位置(tex 全文扫描)

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 Learningcite 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)

1 论文简介(ArchitectureIQ 是什么)

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”。

论文结构(含待补状态)

章节行号内容 待补
§1 IntroductionL83–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 WorkL109空节整节待写
§3 ArchitectureIQ: A Generative and Verifiable ProbeL111–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–L306ICL 无效、TTS 无效、结构化基线、解析推理有 TODO(L241、L306)
§5 Online Knowledge AccumulationL308–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 DiscussionL399–L400空节待写
§7 ConclusionL402空节待写
Appendix A–DL415–L508构造细节、基线细节、完整结果、配对翻转评测—

关键设定与数字

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。

2 引用位点总览

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
T1Top 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)
T1As 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
T2We 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
T2a 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
T2In-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
T2Importantly, 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
T1Ground 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
T3Performance 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
T2The 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
T1The 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
T1Our 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
T2The 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
T1We 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
T1Accuracy 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
T1Each 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
T2A 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
T2Some 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)
T1claims 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
T1The 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
T1Claude 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
T2At 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

3 逐节引用计划

§1 Introduction

1.1 · T1 L84

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.

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。

vanrijn2018hpibischl2021hpobengio2012practical

1.2 · T1 L86(稿内 #TODO)

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]

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。

lu2024aiscientistyamada2025aiscientistv2novikov2025alphaevolvezhang2025dgmtao2025selfevolvingrsisurvey2026

1.3 · T2 L88 / L97

We introduce ArchitectureIQ, a fully controlled and verifiable probe for studying this capability. … a static 500-question benchmark supporting fair model comparison

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。

mlsbench2026wijk2024rebenchkapoor2022leakage

1.4 · T2 L99

a best of 61% among 10 human machine learning researchers

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。

rein2023gpqaphan2025hle

1.5 · T2 L103

In-context learning and test-time scaling, surprisingly, does not reliably close these gaps

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。

wei2022cotsnell2024testtime

§2 Related Work(第 1 段)

2.1 · T1 L109(空节)

【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

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。

zoph2017nasrlpham2018enasliu2019dartscai2020ofaying2019nasbench101dong2020nasbench201klein2019tabulareggensperger2021hpobenchpfisterer2022yahpowhite2021predictorswhite2019bananasabdelfattah2021zerocostmellor2021naswotunterthiner2020predictingdomhan2015learningcurvesmohr2022learningcurves

§2 Related Work(第 2 段)

2.2 · T1 L109(空节)

【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

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。

chan2024mlebenchhuang2023mlagentbenchnathani2025mlgymwijk2024rebenchpaperbench2025chen2025mlrbenchliang2024mlbenchmlsbench2026autolab2026jimenez2024swebenchmetr2025timehorizon

§2 Related Work(第 3 段)

2.3 · T1 L109(空节)

【建议段落主题】RSI / 自我改进评测:从“改进系统”到“先验判断”

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。

chi2026ai4aibenchrsibenchdata2026posttrainbench2026rsisurvey2026zhang2025dgmnovikov2025alphaevolvelu2024aiscientist

§2 Related Work(第 4 段)

2.4 · T1 L109(空节)

【建议段落主题】最接近的工作:不执行就预测结果

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。

tuneahead2026predictbefore2026wang2023predictableliu2024agenthpoliu2024llmboyang2023oprozhang2023automlgpt

§2 Related Work(第 5 段)

2.5 · T1 L109(空节)

【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

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。

hendrycks2021mmlusrivastava2022bigbenchliang2022helmrein2023gpqazheng2023mcqrobustsclar2023promptsensitivitydehghani2021benchmarklotterykapoor2022leakagedamour2020underspecificationgeirhos2020shortcutmiller2020distributionshiftkaushik2020counterfactualribeiro2020checklist

§2 Related Work(第 6 段)

2.6 · T2 L109(空节)

【建议段落主题】推理时计算、上下文学习与经验复用

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”。

wei2022cotwang2022selfconsistencysnell2024testtimemuennighoff2025s1guo2025deepseekr1chen2024overthinkingbrown2024monkeysbrown2020gpt3min2022rethinkingchan2022datadistributionalshinn2023reflexionzhao2023expelzhang2025aceevolver2025yuan2026skilloptai4aittesttime2026

§3.1 Benchmark Construction

3.1 · T2 L115

Importantly, the information presented in a question, in principle, fully determines the training outcome.

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。

liang2022helmribeiro2020checklist

3.2 · T1 L119

Ground truth is obtained only by importing and executing that exact generated code; no parallel implementation estimates the outcome.

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。

kapoor2022leakagedehghani2021benchmarklottery

§3.2 Main Results

3.3 · T3 L137

Performance also changes substantially across model generations

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。

schaeffer2023mirage

3.4 · T2 L149

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.

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。

rein2023gpqaphan2025hleglazer2024frontiermath

§3.3 Architecture Knowledge as the Weak Point

3.5 · T1 L163-L165

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.

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。

wilson2017adaptivekingma2014adamloshchilov2017adamwsmith2018dontdecaymccandlish2018largbatch

§3.4 Training Configuration Dominates Dataset Evidence

3.6 · T1 L169-L175

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.

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。

kaushik2020counterfactualmiller2020distributionshiftgeirhos2020shortcutdamour2020underspecification

§3.5 Scaling with the Number of Choices

3.7 · T2 L179-L200

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.

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”。

zheng2023mcqrobustbrown2024monkeys

§4.1 Ineffective In-context Learning

4.1 · T1 L216

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.

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。

brown2020gpt3min2022rethinkingchan2022datadistributionalliu2022whatmakesgood

§4.2 Ineffective Test-time Scaling

4.2 · T1 L246-L250

Accuracy moves within the question-sampling noise … Longer traces often contain more detailed discussion, but they generally lead to the same final preference.

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。

wei2022cotwang2022selfconsistencysnell2024testtimemuennighoff2025s1guo2025deepseekr1chen2024overthinkingbrown2024monkeys

§4.3 Rule-based and Structured Predictor Baselines

4.3 · T1 L254

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.

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”。

grinsztajn2022tabularhollmann2022tabpfnfriedman2001gbm

4.3b · T2 L264

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.

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。

smith2018dontdecaymccandlish2018largbatchbengio2012practicalvanrijn2018hpi

§4.4 Analytical Reasoning

4.4 · T2 L304

Some of these propositions are even semi-quantitative ones containing numerical range of where it holds, or calculables with symbolic expression.

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。

turpin2023faithfulnesslanham2023measuringwei2022cot

§5.1 Learning Verifiable Propositions Online

5.1 · T1 L321(稿内 #TODO cite PUCT)

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)))

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。

auer2002ucbsilver2017alphagozerobrowne2012mcts

5.1b · T1 L316-L320

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.

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。

mesnard2021creditshinn2023reflexionzhao2023expelzhang2025aceevolver2025

§5.2 Evaluation Across KB Snapshots

5.2 · T1 L329 / L387

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).

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。

ai4aittesttime2026yuan2026skilloptlewis2020ragliu2022whatmakesgoodmcnemar1947

§5.3 Compressibility Without a New Ceiling

5.3 · T2 L392-L396

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.

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。

ye2024sharpeningzweiger2025sealyuan2024selfrewardingsingh2023restem

§6 Discussion / §7 Conclusion

6.1 · T2 L399-L402

(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。

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。

mlsbench2026wijk2024rebenchmetr2025timehorizonrsisurvey2026

§6 Discussion(limitations)

6.2 · T2 L399-L402

(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。

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。

kapoor2022leakagedamour2020underspecificationdehghani2021benchmarklottery

附录 A Benchmark Construction Details

A.1 · T3 L416

(构造细节)六族合成数据 + 10 seed 全胜闸门 + param ratio ≤ 2。

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。

klein2019tabulareggensperger2021hpobenchribeiro2020checklist

4 RSI / AI4AI 基准速览(2024–2026)

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-Bench
arXiv:2605.08678
2026-05140 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-Bench
arXiv:2608.20318
2026-0810 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-Data
arXiv: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。
PostTrainBench
arXiv: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。
AutoLab
arXiv:2606.05080
2026-0636 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。
PaperBench
arXiv: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-Bench
arXiv:2411.15114
2024-11AI 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-bench
arXiv:2410.07095
2024-10Kaggle 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 / MLAgentBench
arXiv: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。
TuneAhead
arXiv: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-Time
arXiv: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 horizon
arXiv:2503.14499
2025-0350% 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。

5 文献明细

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。

C1 · NAS、HPO 与性能预测(“不训练就预测”)

Neural Architecture Search with Reinforcement LearningarXiv 已验证
Barret Zoph, Quoc V. Le · arXiv preprint arXiv:1611.01578 · zoph2017nasrl
NAS 的 RL 开山作,谱系起点
BibTeX
@article{zoph2017nasrl,
  author    = {Barret Zoph and Quoc V. Le},
  title     = {Neural Architecture Search with Reinforcement Learning},
  journal   = {arXiv preprint arXiv:1611.01578},
  year      = {2016},
  eprint    = {1611.01578},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/1611.01578},
}
Efficient Neural Architecture Search via Parameter SharingarXiv 已验证
Hieu Pham, Melody Y. Guan, Barret Zoph, et al. · arXiv preprint arXiv:1802.03268 · pham2018enas
权重共享,把 NAS 成本压下来
BibTeX
@article{pham2018enas,
  author    = {Hieu Pham and Melody Y. Guan and Barret Zoph and Quoc V. Le and Jeff Dean},
  title     = {Efficient Neural Architecture Search via Parameter Sharing},
  journal   = {arXiv preprint arXiv:1802.03268},
  year      = {2018},
  eprint    = {1802.03268},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/1802.03268},
}
DARTS: Differentiable Architecture SearcharXiv 已验证
Hanxiao Liu, Karen Simonyan, Yiming Yang · ICLR 2019 · liu2019darts
可微架构搜索,NAS 主流范式
BibTeX
@inproceedings{liu2019darts,
  author    = {Hanxiao Liu and Karen Simonyan and Yiming Yang},
  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},
}
Once-for-All: Train One Network and Specialize it for Efficient DeploymentarXiv 已验证
Han Cai, Chuang Gan, Tianzhe Wang, et al. · ICLR 2020 · cai2020ofa
一次性训练、按需派生,NAS 工程化代表
BibTeX
@inproceedings{cai2020ofa,
  author    = {Han Cai and Chuang Gan and Tianzhe Wang and Zhekai Zhang and Song Han},
  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},
}
NAS-Bench-101: Towards Reproducible Neural Architecture SearcharXiv 已验证
Chris Ying, Aaron Klein, Esteban Real, et al. · arXiv preprint arXiv:1902.09635 · ying2019nasbench101
表格化 NAS 基准(可复现)
BibTeX
@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},
}
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture SearcharXiv 已验证
Xuanyi Dong, Yi Yang · ICLR 2020 · dong2020nasbench201
表格化 NAS 基准的扩展版
BibTeX
@inproceedings{dong2020nasbench201,
  author    = {Xuanyi Dong and Yi Yang},
  title     = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
  booktitle = {ICLR 2020},
  year      = {2020},
  eprint    = {2001.00326},
  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…},
}
Tabular Benchmarks for Joint Architecture and Hyperparameter OptimizationarXiv 已验证
Aaron Klein, Frank Hutter · arXiv preprint arXiv:1905.04970 · klein2019tabular
联合架构+超参的表格基准(与 AIQ 最像的“查表”范式)
BibTeX
@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},
}
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOarXiv 已验证
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, et al. · arXiv preprint arXiv:2109.06716 · eggensperger2021hpobench
可复现多保真 HPO 基准集合
BibTeX
@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},
}
YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter OptimizationarXiv 已验证
Florian Pfisterer, Lennart Schneider, Julia Moosbauer, et al. · arXiv preprint arXiv:2109.03670 · pfisterer2022yahpo
多目标多保真 HPO 基准
BibTeX
@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},
}
How Powerful are Performance Predictors in Neural Architecture Search?arXiv 已验证
Colin White, Arber Zela, Binxin Ru, et al. · NeurIPS 2021 · white2021predictors
系统评估性能预测器到底有多强
BibTeX
@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},
}
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearcharXiv 已验证
Colin White, Willie Neiswanger, Yash Savani · AAAI Conference on Artificial Intelligence 2021 · white2019bananas
代理模型 + 贝叶斯优化的性能预测
BibTeX
@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},
}
Zero-Cost Proxies for Lightweight NASarXiv 已验证
Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, et al. · ICLR 2021 · abdelfattah2021zerocost
zero-cost proxy(不训练打分)
BibTeX
@inproceedings{abdelfattah2021zerocost,
  author    = {Mohamed S. Abdelfattah and Abhinav Mehrotra and Łukasz Dudziak and Nicholas D. Lane},
  title     = {Zero-Cost Proxies for Lightweight NAS},
  booktitle = {ICLR 2021},
  year      = {2021},
  eprint    = {2101.08134},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2101.08134},
}
Neural Architecture Search without TrainingarXiv 已验证
Joseph Mellor, Jack Turner, Amos Storkey, et al. · ICML 2021 · mellor2021naswot
NASWOT:无训练代理的经典
BibTeX
@inproceedings{mellor2021naswot,
  author    = {Joseph Mellor and Jack Turner and Amos Storkey and Elliot J. Crowley},
  title     = {Neural Architecture Search without Training},
  booktitle = {ICML 2021},
  year      = {2020},
  eprint    = {2006.04647},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2006.04647},
}
Predicting Neural Network Accuracy from WeightsarXiv 已验证
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, et al. · arXiv preprint arXiv:2002.11448 · unterthiner2020predicting
直接从权重预测精度
BibTeX
@article{unterthiner2020predicting,
  author    = {Thomas Unterthiner and Daniel Keysers and Sylvain Gelly and Olivier Bousquet and Ilya Tolstikhin},
  title     = {Predicting Neural Network Accuracy from Weights},
  journal   = {arXiv preprint arXiv:2002.11448},
  year      = {2020},
  eprint    = {2002.11448},
  archivePrefix = {arXiv},
  primaryClass = {stat.ML},
  url       = {https://arxiv.org/abs/2002.11448},
}
Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves人工核对
Tobias Domhan, Jost Tobias Springenberg, Frank Hutter · IJCAI, pp. 3462-3468 · domhan2015learningcurves
学习曲线外推:早期预测最终性能的先驱工作
BibTeX
@inproceedings{domhan2015learningcurves,
  author    = {Tobias Domhan and Jost Tobias Springenberg and Frank Hutter},
  title     = {Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves},
  booktitle = {IJCAI, pp. 3462-3468},
  year      = {2015},
  url       = {https://www.ijcai.org/Proceedings/15/Papers/525.pdf},
}
Learning Curves for Decision Making in Supervised Machine Learning: A SurveyarXiv 已验证
Felix Mohr, Jan N. van Rijn · Machine Learning, Volume 113, pages 8371-8425 (2024) · mohr2022learningcurves
学习曲线用于决策的综述
BibTeX
@article{mohr2022learningcurves,
  author    = {Felix Mohr and Jan N. van Rijn},
  title     = {Learning Curves for Decision Making in Supervised Machine Learning: A Survey},
  journal   = {Machine Learning, Volume 113, pages 8371-8425 (2024)},
  year      = {2022},
  eprint    = {2201.12150},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2201.12150},
}
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open ChallengesarXiv 已验证
Bernd Bischl, Martin Binder, Michel Lang, et al. · arXiv preprint arXiv:2107.05847 · bischl2021hpo
HPO 权威综述(方法与实践)
BibTeX
@article{bischl2021hpo,
  author    = {Bernd Bischl and Martin Binder and Michel Lang and Tobias Pielok and Jakob Richter and Stefan Coors and Janek Thomas and Theresa Ullmann and Marc Becker and Anne-Laure Boulesteix and Difan Deng and Marius Lindauer},
  title     = {Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges},
  journal   = {arXiv preprint arXiv:2107.05847},
  year      = {2021},
  eprint    = {2107.05847},
  archivePrefix = {arXiv},
  primaryClass = {stat.ML},
  url       = {https://arxiv.org/abs/2107.05847},
}
Hyperparameter Importance Across DatasetsarXiv 已验证
J. N. van Rijn, F. Hutter · arXiv preprint arXiv:1710.04725 · vanrijn2018hpi
超参数重要性排序(LR 最重要)
BibTeX
@article{vanrijn2018hpi,
  author    = {J. N. van Rijn and F. Hutter},
  title     = {Hyperparameter Importance Across Datasets},
  journal   = {arXiv preprint arXiv:1710.04725},
  year      = {2017},
  eprint    = {1710.04725},
  archivePrefix = {arXiv},
  primaryClass = {stat.ML},
  url       = {https://arxiv.org/abs/1710.04725},
}
Practical recommendations for gradient-based training of deep architecturesarXiv 已验证
Yoshua Bengio · arXiv preprint arXiv:1206.5533 · bengio2012practical
训练超参实用建议(LR 优先)
BibTeX
@article{bengio2012practical,
  author    = {Yoshua Bengio},
  title     = {Practical recommendations for gradient-based training of deep architectures},
  journal   = {arXiv preprint arXiv:1206.5533},
  year      = {2012},
  eprint    = {1206.5533},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/1206.5533},
}

C2 · ML 工程 / AI 研究 agent 与 AI R&D 评测

MLE-bench: Evaluating Machine Learning Agents on Machine Learning EngineeringarXiv 已验证
Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, et al. · arXiv preprint arXiv:2410.07095 · chan2024mlebench
Kaggle 级 ML 工程 agent 基准
BibTeX
@article{chan2024mlebench,
  author    = {Jun Shern Chan and Neil Chowdhury and Oliver Jaffe and James Aung and Dane Sherburn and Evan Mays and Giulio Starace and Kevin Liu and Leon Maksin and Tejal Patwardhan and Lilian Weng and Aleksander Mądry},
  title     = {MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering},
  journal   = {arXiv preprint arXiv:2410.07095},
  year      = {2024},
  eprint    = {2410.07095},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2410.07095},
}
MLAgentBench: Evaluating Language Agents on Machine Learning ExperimentationarXiv 已验证
Qian Huang, Jian Vora, Percy Liang, et al. · arXiv preprint arXiv:2310.03302 · huang2023mlagentbench
ML 实验 agent 基准
BibTeX
@article{huang2023mlagentbench,
  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},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2310.03302},
}
MLGym: A New Framework and Benchmark for Advancing AI Research AgentsarXiv 已验证
Deepak Nathani, Lovish Madaan, Nicholas Roberts, et al. · arXiv preprint arXiv:2502.14499 · nathani2025mlgym
AI 研究 agent 的 gym 框架与基准
BibTeX
@article{nathani2025mlgym,
  author    = {Deepak Nathani and Lovish Madaan and Nicholas Roberts and Nikolay Bashlykov and Ajay Menon and Vincent Moens and Amar Budhiraja and Despoina Magka and Vladislav Vorotilov and Gaurav Chaurasia and Dieuwke Hupkes and Ricardo Silveira Cabral and Tatiana Shavrina and Jakob Foerster and Yoram Bachrach and William Yang Wang and Roberta Raileanu},
  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},
}
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human expertsarXiv 已验证
Hjalmar Wijk, Tao Lin, Joel Becker, et al. · arXiv preprint arXiv:2411.15114 · wijk2024rebench
AI R&D 能力 vs 人类专家(含时间预算)
BibTeX
@article{wijk2024rebench,
  author    = {Hjalmar Wijk and Tao Lin and Joel Becker and Sami Jawhar and Neev Parikh and Thomas Broadley and Lawrence Chan and Michael Chen and Josh Clymer and Jai Dhyani and Elena Ericheva and Katharyn Garcia and Brian Goodrich and Nikola Jurkovic and Holden Karnofsky and Megan Kinniment and Aron Lajko and Seraphina Nix and Lucas Sato and William Saunders and Maksym Taran and Ben West and Elizabeth Barnes},
  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},
}
PaperBench: Evaluating AI's Ability to Replicate AI ResearcharXiv 已验证
Giulio Starace, Oliver Jaffe, Dane Sherburn, et al. · arXiv preprint arXiv:2504.01848 · paperbench2025
论文复现能力的 agent 基准
BibTeX
@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},
}
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning ResearcharXiv 已验证
Hui Chen, Miao Xiong, Yujie Lu, et al. · NeurIPS 2025 · chen2025mlrbench
开放式 ML 研究 agent 评测
BibTeX
@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},
}
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level CodearXiv 已验证
Xiangru Tang, Yuliang Liu, Zefan Cai, et al. · arXiv preprint arXiv:2311.09835 · liang2024mlbench
仓库级 ML 任务基准
BibTeX
@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},
}
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AIarXiv 已验证
Bohan Lyu, Yucheng Yang, Siqiao Huang, et al. · arXiv preprint arXiv:2605.08678 · mlsbench2026
“构建更好的 AI”系统级评测(12 域 140 任务)
BibTeX
@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},
}
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?arXiv 已验证
Zhangchen Xu, Junda Chen, Yue Huang, et al. · arXiv preprint arXiv:2606.05080 · autolab2026
长时程自动研究/工程闭环任务(36 个)
BibTeX
@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},
}
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?arXiv 已验证
Carlos E. Jimenez, John Yang, Alexander Wettig, et al. · ICLR 2024 · jimenez2024swebench
真实 GitHub issue 的 agent 基准
BibTeX
@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…},
}
Measuring AI Ability to Complete Long Software TasksarXiv 已验证
Thomas Kwa, Ben West, Joel Becker, et al. · NeurIPS 2025 · metr2025timehorizon
长任务能力时域测量(AI R&D 能力代理)
BibTeX
@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},
}
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific DiscoveryarXiv 已验证
Ziru Chen, Shijie Chen, Yuting Ning, et al. · ICLR 2025 · chen2024scienceagentbench
数据驱动科学发现的 agent 基准
BibTeX
@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},
}
DiscoveryBench: Towards Data-Driven Discovery with Large Language ModelsarXiv 已验证
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, et al. · arXiv preprint arXiv:2407.01725 · majumder2024discoverybench
假设驱动的数据科学发现基准
BibTeX
@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},
}

C3 · RSI / 自我改进评测与系统

AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-ImprovementarXiv 已验证
Yizhe Chi, Wenyi Li, Deyao Hong, et al. · arXiv preprint arXiv:2608.20318 · chi2026ai4aibench
RSI:agent 设计训练算法(10 仓库)
BibTeX
@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},
}
RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-ImprovementarXiv 已验证
Fanqing Meng, Lingxiao Du, Qiguang Chen, et al. · arXiv preprint arXiv:2607.25886 · rsibenchdata2026
RSI:数据侧研究闭环评测
BibTeX
@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},
}
PostTrainBench: Can LLM Agents Automate LLM Post-Training?arXiv 已验证
Ben Rank, Hardik Bhatnagar, Ameya Prabhu, et al. · arXiv preprint arXiv:2603.08640 · posttrainbench2026
RSI:10 小时自动化后训练(1×H100)
BibTeX
@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},
}
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research LoopsarXiv 已验证
Mingguang Chen, Licheng Wang, Bo Qu · arXiv preprint arXiv:2607.07663 · rsisurvey2026
RSI 综述(1250 篇,按更新目标与闭环程度分类)
BibTeX
@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},
}
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super IntelligencearXiv 已验证
Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al. · arXiv preprint arXiv:2507.21046 · tao2025selfevolving
自进化 agent 综述:何时/何处进化(2507.21046)
BibTeX
@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},
}
Darwin Godel Machine: Open-Ended Evolution of Self-Improving AgentsarXiv 已验证
Jenny Zhang, Shengran Hu, Cong Lu, et al. · arXiv preprint arXiv:2505.22954 · zhang2025dgm
Darwin Gödel Machine:自改代码闭环
BibTeX
@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},
}
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-ImprovementarXiv 已验证
Xunjian Yin, Xinyi Wang, Liangming Pan, et al. · ACL 2025 · yin2024godelagent
Gödel Agent:自指式自我改进框架
BibTeX
@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},
}
Self-Taught Optimizer (STOP): Recursively Self-Improving Code GenerationarXiv 已验证
Eric Zelikman, Eliana Lorch, Lester Mackey, et al. · COLM 2024 · zelikman2023stop
STOP:让脚手架程序改进自己
BibTeX
@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},
}
AlphaEvolve: A coding agent for scientific and algorithmic discoveryarXiv 已验证
Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al. · arXiv preprint arXiv:2506.13131 · novikov2025alphaevolve
AlphaEvolve:LLM 进化代码库做发现
BibTeX
@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},
}
The AI Scientist: Towards Fully Automated Open-Ended Scientific DiscoveryarXiv 已验证
Chris Lu, Cong Lu, Robert Tjarko Lange, et al. · arXiv preprint arXiv:2408.06292 · lu2024aiscientist
AI Scientist:端到端自动科研闭环
BibTeX
@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},
}
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree SearcharXiv 已验证
Yutaro Yamada, Robert Tjarko Lange, Cong Lu, et al. · arXiv preprint arXiv:2504.08066 · yamada2025aiscientistv2
AI Scientist-v2:agentic tree search(Nature 2026)
BibTeX
@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},
}
Agent Laboratory: Using LLM Agents as Research AssistantsarXiv 已验证
Samuel Schmidgall, Yusheng Su, Ze Wang, et al. · arXiv preprint arXiv:2501.04227 · schmidgall2025agentlab
Agent Laboratory:LLM 研究助理流水线
BibTeX
@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},
}
Automated Design of Agentic SystemsarXiv 已验证
Shengran Hu, Cong Lu, Jeff Clune · arXiv preprint arXiv:2408.08435 · hu2024adas
ADAS:元 agent 自动设计 agent
BibTeX
@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},
}
AutoResearch: Insight In, Hallucination OutarXiv 已验证
Xiang Liu, Qumeng Sun, Bruno, et al. · arXiv preprint arXiv:2608.17906 · autoresearch2026
AutoResearch:先验入库、幻觉出库的自动化研究
BibTeX
@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},
}
MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm DiscoveryarXiv 已验证
Shangheng Du, Xiangchao Yan, Jinxin Shi, et al. · arXiv preprint arXiv:2606.06473 · mlevolve2026
MLEvolve:ML 算法发现的自进化框架
BibTeX
@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},
}
Towards Execution-Grounded Automated AI ResearcharXiv 已验证
Chenglei Si, Zitong Yang, Yejin Choi, et al. · arXiv preprint arXiv:2601.14525 · execgrounded2026
执行锚定的自动化 AI 研究
BibTeX
@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},
}

C4 · 不执行就预测结果(最接近的工作)

TuneAhead: Predicting Fine-tuning Performance Before Full Training BeginsarXiv 已验证
Yuxiang Luo, Haonan Long, Chen Wang, et al. · ICML 2026 · tuneahead2026
微调前预测成败(探针特征)
BibTeX
@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},
}
Can We Predict Before Executing Machine Learning Agents?arXiv 已验证
Jingsheng Zheng, Jintian Zhang, Yujie Luo, et al. · ACL 2026 · predictbefore2026
执行前预测方案优劣(数据报告上的偏好判断)
BibTeX
@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},
}
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bencharXiv 已验证
Qinyuan Ye, Harvey Yiyun Fu, Xiang Ren, et al. · EMNLP 2023 · wang2023predictable
用历史实验预测 LLM 能力(BIG-bench)
BibTeX
@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},
}
Large Language Model Agent for Hyper-Parameter OptimizationarXiv 已验证
Siyi Liu, Chen Gao, Yong Li · arXiv preprint arXiv:2402.01881 · liu2024agenthpo
LLM agent 做超参优化
BibTeX
@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},
}
Large Language Models to Enhance Bayesian OptimizationarXiv 已验证
Tennison Liu, Nicolás Astorga, Nabeel Seedat, et al. · ICLR 2024 · liu2024llmbo
LLM 增强贝叶斯优化
BibTeX
@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},
}
Large Language Models as OptimizersarXiv 已验证
Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al. · ICLR 2024 · yang2023opro
OPRO:LLM 作为优化器(提方案)
BibTeX
@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},
}
AutoML-GPT: Automatic Machine Learning with GPTarXiv 已验证
Shujian Zhang, Chengyue Gong, Lemeng Wu, et al. · arXiv preprint arXiv:2305.02499 · zhang2023automlgpt
AutoML-GPT:LLM 驱动 AutoML
BibTeX
@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},
}

C5 · 基准方法学与鲁棒性 / 构造效度批评

Measuring Massive Multitask Language UnderstandingarXiv 已验证
Dan Hendrycks, Collin Burns, Steven Basart, et al. · ICLR 2021 · hendrycks2021mmlu
大规模多任务语言理解基准(评测范式)
BibTeX
@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},
}
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv 已验证
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al. · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj · srivastava2022bigbench
程序化生成任务的协作基准
BibTeX
@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…},
}
Holistic Evaluation of Language ModelsarXiv 已验证
Percy Liang, Rishi Bommasani, Tony Lee, et al. · Published in Transactions on Machine Learning Research (TMLR), 2023 · liang2022helm
整体性评测方法论
BibTeX
@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…},
}
GPQA: A Graduate-Level Google-Proof Q&A BenchmarkarXiv 已验证
David Rein, Betty Li Hou, Asa Cooper Stickland, et al. · arXiv preprint arXiv:2311.12022 · rein2023gpqa
专家级问答基准(人类专家对照)
BibTeX
@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},
}
Humanity's Last ExamarXiv 已验证
Long Phan, Alice Gatti, Ziwen Han, et al. · arXiv preprint arXiv:2501.14249 · phan2025hle
HLE:专家级难题基准(人类上限)
BibTeX
@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. Zhang and Kostiantyn Dobarskyi and Roman Leventov and Ignat Soroko and Joshua Duersch and Vage Taamazyan and Andrew Ho and Wenjie Ma and William Held and Ruicheng Xian and Armel Randy Zebaze and Mohanad Mohamed and Julian Noah Leser and Michelle X Yuan and Laila Yacar and Johannes Lengler and Katarzyna Olszewska and Claudio Di Fratta and Edson Oliveira and Joseph W. Jackson and Andy Zou and Muthu Chidambaram and Timothy Manik and Hector Haffenden and Dashiell Stander and Ali Dasouqi and Alexander Shen and Bita Golshani and David Stap and Egor Kretov and Mikalai Uzhou and Alina Borisovna Zhidkovskaya and Nick Winter and Miguel Orbegozo Rodriguez and Robert Lauff and Dustin Wehr and Colin Tang and Zaki Hossain and Shaun Phillips and Fortuna Samuele and Fredrik Ekström and Angela Hammon and Oam Patel and Faraz Farhidi and George Medley and Forough Mohammadzadeh and Madellene Peñaflor and Haile Kassahun and Alena Friedrich and Rayner Hernandez Perez and Daniel Pyda and Taom Sakal and Omkar Dhamane and Ali Khajegili Mirabadi and Eric Hallman and Kenchi Okutsu and Mike Battaglia and Mohammad Maghsoudimehrabani and Alon Amit and Dave Hulbert and Roberto Pereira and Simon Weber and  Handoko and Anton Peristyy and Stephen Malina and Mustafa Mehkary and Rami Aly and Frank Reidegeld and Anna-Katharina Dick and Cary Friday and Mukhwinder Singh and Hassan Shapourian and Wanyoung Kim and Mariana Costa and Hubeyb Gurdogan and Harsh Kumar and Chiara Ceconello and Chao Zhuang and Haon Park and Micah Carroll and Andrew R. Tawfeek and Stefan Steinerberger and Daattavya Aggarwal and Michael Kirchhof and Linjie Dai and Evan Kim and Johan Ferret and Jainam Shah and Yuzhou Wang and Minghao Yan and Krzysztof Burdzy and Lixin Zhang and Antonio Franca and Diana T. Pham and Kang Yong Loh and Joshua Robinson and Abram Jackson and Paolo Giordano and Philipp Petersen and Adrian Cosma and Jesus Colino and Colin White and Jacob Votava and Vladimir Vinnikov and Ethan Delaney and Petr Spelda and Vit Stritecky and Syed M. Shahid and Jean-Christophe Mourrat and Lavr Vetoshkin and Koen Sponselee and Renas Bacho and Zheng-Xin Yong and Florencia de la Rosa and Nathan Cho and Xiuyu Li and Guillaume Malod and Orion Weller and Guglielmo Albani and Leon Lang and Julien Laurendeau and Dmitry Kazakov and Fatimah Adesanya and Julien Portier and Lawrence Hollom and Victor Souza and Yuchen Anna Zhou and Julien Degorre and Yiğit Yalın and Gbenga Daniel Obikoya and  Rai and Filippo Bigi and M. C. Boscá and Oleg Shumar and Kaniuar Bacho and Gabriel Recchia and Mara Popescu and Nikita Shulga and Ngefor Mildred Tanwie and Thomas C. H. Lux and Ben Rank and Colin Ni and Matthew Brooks and Alesia Yakimchyk and  Huanxu and  Liu and Stefano Cavalleri and Olle Häggström and Emil Verkama and Joshua Newbould and Hans Gundlach and Leonor Brito-Santana and Brian Amaro and Vivek Vajipey and Rynaa Grover and Ting Wang and Yosi Kratish and Wen-Ding Li and Sivakanth Gopi and Andrea Caciolai and Christian Schroeder de Witt and Pablo Hernández-Cámara and Emanuele Rodolà and Jules Robins and Dominic Williamson and Vincent Cheng and Brad Raynor and Hao Qi and Ben Segev and Jingxuan Fan and Sarah Martinson and Erik Y. Wang and Kaylie Hausknecht and Michael P. Brenner and Mao Mao and Christoph Demian and Peyman Kassani and Xinyu Zhang and David Avagian and Eshawn Jessica Scipio and Alon Ragoler and Justin Tan and Blake Sims and Rebeka Plecnik and Aaron Kirtland and Omer Faruk Bodur and D. P. Shinde and Yan Carlos Leyva Labrador and Zahra Adoul and Mohamed Zekry and Ali Karakoc and Tania C. B. Santos and Samir Shamseldeen and Loukmane Karim and Anna Liakhovitskaia and Nate Resman and Nicholas Farina and Juan Carlos Gonzalez and Gabe Maayan and Earth Anderson and Rodrigo De Oliveira Pena and Elizabeth Kelley and Hodjat Mariji and Rasoul Pouriamanesh and Wentao Wu and Ross Finocchio and Ismail Alarab and Joshua Cole and Danyelle Ferreira and Bryan Johnson and Mohammad Safdari and Liangti Dai and Siriphan Arthornthurasuk and Isaac C. McAlister and Alejandro José Moyano and Alexey Pronin and Jing Fan and Angel Ramirez-Trinidad and Yana Malysheva and Daphiny Pottmaier and Omid Taheri and Stanley Stepanic and Samuel Perry and Luke Askew and Raúl Adrián Huerta Rodríguez and Ali M. R. Minissi and Ricardo Lorena and Krishnamurthy Iyer and Arshad Anil Fasiludeen and Ronald Clark and Josh Ducey and Matheus Piza and Maja Somrak and Eric Vergo and Juehang Qin and Benjámin Borbás and Eric Chu and Jack Lindsey and Antoine Jallon and I. M. J. McInnis and Evan Chen and Avi Semler and Luk Gloor and Tej Shah and Marc Carauleanu and Pascal Lauer and Tran Đuc Huy and Hossein Shahrtash and Emilien Duc and Lukas Lewark and Assaf Brown and Samuel Albanie and Brian Weber and Warren S. Vaz and Pierre Clavier and Yiyang Fan and Gabriel Poesia Reis e Silva and  Long and  Lian and Marcus Abramovitch and Xi Jiang and Sandra Mendoza and Murat Islam and Juan Gonzalez and Vasilios Mavroudis and Justin Xu and Pawan Kumar and Laxman Prasad Goswami and Daniel Bugas and Nasser Heydari and Ferenc Jeanplong and Thorben Jansen and Antonella Pinto and Archimedes Apronti and Abdallah Galal and Ng Ze-An and Ankit Singh and Tong Jiang and Joan of Arc Xavier and Kanu Priya Agarwal and Mohammed Berkani and Gang Zhang and Zhehang Du and Benedito Alves de Oliveira Junior and Dmitry Malishev and Nicolas Remy and Taylor D. Hartman and Tim Tarver and Stephen Mensah and Gautier Abou Loume and Wiktor Morak and Farzad Habibi and Sarah Hoback and Will Cai and Javier Gimenez and Roselynn Grace Montecillo and Jakub Łucki and Russell Campbell and Asankhaya Sharma and Khalida Meer and Shreen Gul and Daniel Espinosa Gonzalez and Xavier Alapont and Alex Hoover and Gunjan Chhablani and Freddie Vargus and Arunim Agarwal and Yibo Jiang and Deepakkumar Patil and David Outevsky and Kevin Joseph Scaria and Rajat Maheshwari and Abdelkader Dendane and Priti Shukla and Ashley Cartwright and Sergei Bogdanov and Niels Mündler and Sören Möller and Luca Arnaboldi and Kunvar Thaman and Muhammad Rehan Siddiqi and Prajvi Saxena and Himanshu Gupta and Tony Fruhauff and Glen Sherman and Mátyás Vincze and Siranut Usawasutsakorn and Dylan Ler and Anil Radhakrishnan and Innocent Enyekwe and Sk Md Salauddin and Jiang Muzhen and Aleksandr Maksapetyan and Vivien Rossbach and Chris Harjadi and Mohsen Bahaloohoreh and Claire Sparrow and Jasdeep Sidhu and Sam Ali and Song Bian and John Lai and Eric Singer and Justine Leon Uro and Greg Bateman and Mohamed Sayed and Ahmed Menshawy and Darling Duclosel and Dario Bezzi and Yashaswini Jain and Ashley Aaron and Murat Tiryakioglu and Sheeshram Siddh and Keith Krenek and Imad Ali Shah and Jun Jin and Scott Creighton and Denis Peskoff and Zienab EL-Wasif and Ragavendran P and Michael Richmond and Joseph McGowan and Tejal Patwardhan and Hao-Yu Sun and Ting Sun and Nikola Zubić and Samuele Sala and Stephen Ebert and Jean Kaddour and Manuel Schottdorf and Dianzhuo Wang and Gerol Petruzella and Alex Meiburg and Tilen Medved and Ali ElSheikh and S Ashwin Hebbar and Lorenzo Vaquero and Xianjun Yang and Jason Poulos and Vilém Zouhar and Sergey Bogdanik and Mingfang Zhang and Jorge Sanz-Ros and David Anugraha and Yinwei Dai and Anh N. Nhu and Xue Wang and Ali Anil Demircali and Zhibai Jia and Yuyin Zhou and Juncheng Wu and Mike He and Nitin Chandok and Aarush Sinha and Gaoxiang Luo and Long Le and Mickaël Noyé and Michał Perełkiewicz and Ioannis Pantidis and Tianbo Qi and Soham Sachin Purohit and Letitia Parcalabescu and Thai-Hoa Nguyen and Genta Indra Winata and Edoardo M. Ponti and Hanchen Li and Kaustubh Dhole and Jongee Park and Dario Abbondanza and Yuanli Wang and Anupam Nayak and Diogo M. Caetano and Antonio A. W. L. Wong and Maria del Rio-Chanona and Dániel Kondor and Pieter Francois and Ed Chalstrey and Jakob Zsambok and Dan Hoyer and Jenny Reddish and Jakob Hauser and Francisco-Javier Rodrigo-Ginés and Suchandra Datta and Maxwell Shepherd and Thom Kamphuis and Qizheng Zhang and Hyunjun Kim and Ruiji Sun and Jianzhu Yao and Franck Dernoncourt and Satyapriya Krishna and Sina Rismanchian and Bonan Pu and Francesco Pinto and Yingheng Wang and Kumar Shridhar and Kalon J. Overholt and Glib Briia and Hieu Nguyen and  David and Soler Bartomeu and Tony CY Pang and Adam Wecker and Yifan Xiong and Fanfei Li and Lukas S. Huber and Joshua Jaeger and Romano De Maddalena and Xing Han Lù and Yuhui Zhang and Claas Beger and Patrick Tser Jern Kon and Sean Li and Vivek Sanker and Ming Yin and Yihao Liang and Xinlu Zhang and Ankit Agrawal and Li S. Yifei and Zechen Zhang and Mu Cai and Yasin Sonmez and Costin Cozianu and Changhao Li and Alex Slen and Shoubin Yu and Hyun Kyu Park and Gabriele Sarti and Marcin Briański and Alessandro Stolfo and Truong An Nguyen and Mike Zhang and Yotam Perlitz and Jose Hernandez-Orallo and Runjia Li and Amin Shabani and Felix Juefei-Xu and Shikhar Dhingra and Orr Zohar and My Chiffon Nguyen and Alexander Pondaven and Abdurrahim Yilmaz and Xuandong Zhao and Chuanyang Jin and Muyan Jiang and Stefan Todoran and Xinyao Han and Jules Kreuer and Brian Rabern and Anna Plassart and Martino Maggetti and Luther Yap and Robert Geirhos and Jonathon Kean and Dingsu Wang and Sina Mollaei and Chenkai Sun and Yifan Yin and Shiqi Wang and Rui Li and Yaowen Chang and Anjiang Wei and Alice Bizeul and Xiaohan Wang and Alexandre Oliveira Arrais and Kushin Mukherjee and Jorge Chamorro-Padial and Jiachen Liu and Xingyu Qu and Junyi Guan and Adam Bouyamourn and Shuyu Wu and Martyna Plomecka and Junda Chen and Mengze Tang and Jiaqi Deng and Shreyas Subramanian and Haocheng Xi and Haoxuan Chen and Weizhi Zhang and Yinuo Ren and Haoqin Tu and Sejong Kim and Yushun Chen and Sara Vera Marjanović and Junwoo Ha and Grzegorz Luczyna and Jeff J. Ma and Zewen Shen and Dawn Song and Cedegao E. Zhang and Zhun Wang and Gaël Gendron and Yunze Xiao and Leo Smucker and Erica Weng and Kwok Hao Lee and Zhe Ye and Stefano Ermon and Ignacio D. Lopez-Miguel and Theo Knights and Anthony Gitter and Namkyu Park and Boyi Wei and Hongzheng Chen and Kunal Pai and Ahmed Elkhanany and Han Lin and Philipp D. Siedler and Jichao Fang and Ritwik Mishra and Károly Zsolnai-Fehér and Xilin Jiang and Shadab Khan and Jun Yuan and Rishab Kumar Jain and Xi Lin and Mike Peterson and Zhe Wang and Aditya Malusare and Maosen Tang and Isha Gupta and Ivan Fosin and Timothy Kang and Barbara Dworakowska and Kazuki Matsumoto and Guangyao Zheng and Gerben Sewuster and Jorge Pretel Villanueva and Ivan Rannev and Igor Chernyavsky and Jiale Chen and Deepayan Banik and Ben Racz and Wenchao Dong and Jianxin Wang and Laila Bashmal and Duarte V. Gonçalves and Wei Hu and Kaushik Bar and Ondrej Bohdal and Atharv Singh Patlan and Shehzaad Dhuliawala and Caroline Geirhos and Julien Wist and Yuval Kansal and Bingsen Chen and Kutay Tire and Atak Talay Yücel and Brandon Christof and Veerupaksh Singla and Zijian Song and Sanxing Chen and Jiaxin Ge and Kaustubh Ponkshe and Isaac Park and Tianneng Shi and Martin Q. Ma and Joshua Mak and Sherwin Lai and Antoine Moulin and Zhuo Cheng and Zhanda Zhu and Ziyi Zhang and Vaidehi Patil and Ketan Jha and Qiutong Men and Jiaxuan Wu and Tianchi Zhang and Bruno Hebling Vieira and Alham Fikri Aji and Jae-Won Chung and Mohammed Mahfoud and Ha Thi Hoang and Marc Sperzel and Wei Hao and Kristof Meding and Sihan Xu and Vassilis Kostakos and Davide Manini and Yueying Liu and Christopher Toukmaji and Jay Paek and Eunmi Yu and Arif Engin Demircali and Zhiyi Sun and Ivan Dewerpe and Hongsen Qin and Roman Pflugfelder and James Bailey and Johnathan Morris and Ville Heilala and Sybille Rosset and Zishun Yu and Peter E. Chen and Woongyeong Yeo and Eeshaan Jain and Ryan Yang and Sreekar Chigurupati and Julia Chernyavsky and Sai Prajwal Reddy and Subhashini Venugopalan and Hunar Batra and Core Francisco Park and Hieu Tran and Guilherme Maximiano and Genghan Zhang and Yizhuo Liang and Hu Shiyu and Rongwu Xu and Rui Pan and Siddharth Suresh and Ziqi Liu and Samaksh Gulati and Songyang Zhang and Peter Turchin and Christopher W. Bartlett and Christopher R. Scotese and Phuong M. Cao and Ben Wu and Jacek Karwowski and Davide Scaramuzza and Aakaash Nattanmai and Gordon McKellips and Anish Cheraku and Asim Suhail and Ethan Luo and Marvin Deng and Jason Luo and Ashley Zhang and Kavin Jindel and Jay Paek and Kasper Halevy and Allen Baranov and Michael Liu and Advaith Avadhanam and David Zhang and Vincent Cheng and Brad Ma and Evan Fu and Liam Do and Joshua Lass and Hubert Yang and Surya Sunkari and Vishruth Bharath and Violet Ai and James Leung and Rishit Agrawal and Alan Zhou and Kevin Chen and Tejas Kalpathi and Ziqi Xu and Gavin Wang and Tyler Xiao and Erik Maung and Sam Lee and Ryan Yang and Roy Yue and Ben Zhao and Julia Yoon and Sunny Sun and Aryan Singh and Ethan Luo and Clark Peng and Tyler Osbey and Taozhi Wang and Daryl Echeazu and Hubert Yang and Timothy Wu and Spandan Patel and Vidhi Kulkarni and Vijaykaarti Sundarapandiyan and Ashley Zhang and Andrew Le and Zafir Nasim and Srikar Yalam and Ritesh Kasamsetty and Soham Samal and Hubert Yang and David Sun and Nihar Shah and Abhijeet Saha and Alex Zhang and Leon Nguyen and Laasya Nagumalli and Kaixin Wang and Alan Zhou and Aidan Wu and Jason Luo and Anwith Telluri and Steven Dillmann and Zhengxiang Wang and Junyu Luo and Hugo Lunn and Artem Gazizov and Haitz Sáez de Ocáriz Borde and Ivan Trus and Morgan Hervault and Zheyu Zhang and Bo Chen and Yuchen Wu and Christopher J. Cordier and Gün Kaynar and Cansin Ayvaz and Polina Avdiunina and Johannes Brust and Xingjian Diao and K. D. Meaney and Yifan Gu and Chenyu Wang and Chenzhuo Dong and William Wright and Simon Brave and Owen Root and Jiayuan Liu and Chow Chun Lok and Tianqin Li and Shiyi Du and Dailan He and Lufeiya Liu and Sina Jamalzadegan and Anil Ramakrishna and Xuanqing Xu and Xin Qing and Xin Luo and Wenkai Li and Shi Bo and Filipp Gusev and Maximos Skandalis and Desheng Ma and Chunhui Zhang and Haoran Qiu and Allen G Hart and Rickard Brüel Gabrielsson and Ido Akov and Artem Lukoianov and Summer Yue and Alexandr Wang and Dan Hendrycks},
  title     = {Humanity's Last Exam},
  journal   = {arXiv preprint arXiv:2501.14249},
  year      = {2025},
  eprint    = {2501.14249},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2501.14249},
}
FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AIarXiv 已验证
Elliot Glazer, Ege Erdil, Tamay Besiroglu, et al. · arXiv preprint arXiv:2411.04872 · glazer2024frontiermath
FrontierMath:专家级数学基准
BibTeX
@article{glazer2024frontiermath,
  author    = {Elliot Glazer and Ege Erdil and Tamay Besiroglu and Diego Chicharro and Evan Chen and Alex Gunning and Caroline Falkman Olsson and Jean-Stanislas Denain and Anson Ho and Emily de Oliveira Santos and Olli Järviniemi and Matthew Barnett and Robert Sandler and Matej Vrzala and Jaime Sevilla and Qiuyu Ren and Elizabeth Pratt and Lionel Levine and Grant Barkley and Natalie Stewart and Bogdan Grechuk and Tetiana Grechuk and Shreepranav Varma Enugandla and Mark Wildon},
  title     = {FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI},
  journal   = {arXiv preprint arXiv:2411.04872},
  year      = {2024},
  eprint    = {2411.04872},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url       = {https://arxiv.org/abs/2411.04872},
}
Large Language Models Are Not Robust Multiple Choice SelectorsarXiv 已验证
Chujie Zheng, Hao Zhou, Fandong Meng, et al. · ICLR 2024 · zheng2023mcqrobust
LLM 对多选题选项不鲁棒
BibTeX
@inproceedings{zheng2023mcqrobust,
  author    = {Chujie Zheng and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
  title     = {Large Language Models Are Not Robust Multiple Choice Selectors},
  booktitle = {ICLR 2024},
  year      = {2023},
  eprint    = {2309.03882},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2309.03882},
}
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingarXiv 已验证
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, et al. · ICLR 2024 · sclar2023promptsensitivity
提示格式扰动导致的性能波动
BibTeX
@inproceedings{sclar2023promptsensitivity,
  author    = {Melanie Sclar and Yejin Choi and Yulia Tsvetkov and Alane Suhr},
  title     = {Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting},
  booktitle = {ICLR 2024},
  year      = {2023},
  eprint    = {2310.11324},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2310.11324},
}
The Benchmark LotteryarXiv 已验证
Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko, et al. · arXiv preprint arXiv:2107.07002 · dehghani2021benchmarklottery
基准选择本身决定排名(lottery)
BibTeX
@article{dehghani2021benchmarklottery,
  author    = {Mostafa Dehghani and Yi Tay and Alexey A. Gritsenko and Zhe Zhao and Neil Houlsby and Fernando Diaz and Donald Metzler and Oriol Vinyals},
  title     = {The Benchmark Lottery},
  journal   = {arXiv preprint arXiv:2107.07002},
  year      = {2021},
  eprint    = {2107.07002},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2107.07002},
}
Leakage and the Reproducibility Crisis in ML-based SciencearXiv 已验证
Sayash Kapoor, Arvind Narayanan · arXiv preprint arXiv:2207.07048 · kapoor2022leakage
ML 科学中的数据泄漏与可复现危机
BibTeX
@article{kapoor2022leakage,
  author    = {Sayash Kapoor and Arvind Narayanan},
  title     = {Leakage and the Reproducibility Crisis in ML-based Science},
  journal   = {arXiv preprint arXiv:2207.07048},
  year      = {2022},
  eprint    = {2207.07048},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2207.07048},
}
Underspecification Presents Challenges for Credibility in Modern Machine LearningarXiv 已验证
Alexander D'Amour, Katherine Heller, Dan Moldovan, et al. · arXiv preprint arXiv:2011.03395 · damour2020underspecification
欠定问题:同一规格多种解法
BibTeX
@article{damour2020underspecification,
  author    = {Alexander D'Amour and Katherine Heller and Dan Moldovan and Ben Adlam and Babak Alipanahi and Alex Beutel and Christina Chen and Jonathan Deaton and Jacob Eisenstein and Matthew D. Hoffman and Farhad Hormozdiari and Neil Houlsby and Shaobo Hou and Ghassen Jerfel and Alan Karthikesalingam and Mario Lucic and Yian Ma and Cory McLean and Diana Mincu and Akinori Mitani and Andrea Montanari and Zachary Nado and Vivek Natarajan and Christopher Nielson and Thomas F. Osborne and Rajiv Raman and Kim Ramasamy and Rory Sayres and Jessica Schrouff and Martin Seneviratne and Shannon Sequeira and Harini Suresh and Victor Veitch and Max Vladymyrov and Xuezhi Wang and Kellie Webster and Steve Yadlowsky and Taedong Yun and Xiaohua Zhai and D. Sculley},
  title     = {Underspecification Presents Challenges for Credibility in Modern Machine Learning},
  journal   = {arXiv preprint arXiv:2011.03395},
  year      = {2020},
  eprint    = {2011.03395},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2011.03395},
}
Shortcut Learning in Deep Neural NetworksarXiv 已验证
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, et al. · arXiv preprint arXiv:2004.07780 · geirhos2020shortcut
shortcut learning:模型走捷径
BibTeX
@article{geirhos2020shortcut,
  author    = {Robert Geirhos and Jörn-Henrik Jacobsen and Claudio Michaelis and Richard Zemel and Wieland Brendel and Matthias Bethge and Felix A. Wichmann},
  title     = {Shortcut Learning in Deep Neural Networks},
  journal   = {arXiv preprint arXiv:2004.07780},
  year      = {2020},
  eprint    = {2004.07780},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
  url       = {https://arxiv.org/abs/2004.07780},
}
The Effect of Natural Distribution Shift on Question Answering ModelsarXiv 已验证
John Miller, Karl Krauth, Benjamin Recht, et al. · arXiv preprint arXiv:2004.14444 · miller2020distributionshift
自然分布偏移下的性能下降
BibTeX
@article{miller2020distributionshift,
  author    = {John Miller and Karl Krauth and Benjamin Recht and Ludwig Schmidt},
  title     = {The Effect of Natural Distribution Shift on Question Answering Models},
  journal   = {arXiv preprint arXiv:2004.14444},
  year      = {2020},
  eprint    = {2004.14444},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2004.14444},
}
Learning the Difference that Makes a Difference with Counterfactually-Augmented DataarXiv 已验证
Divyansh Kaushik, Eduard Hovy, Zachary C. Lipton · ICLR 2020 · kaushik2020counterfactual
反事实增广数据(换掉决定性因素)
BibTeX
@inproceedings{kaushik2020counterfactual,
  author    = {Divyansh Kaushik and Eduard Hovy and Zachary C. Lipton},
  title     = {Learning the Difference that Makes a Difference with Counterfactually-Augmented Data},
  booktitle = {ICLR 2020},
  year      = {2019},
  eprint    = {1909.12434},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/1909.12434},
}
Beyond Accuracy: Behavioral Testing of NLP models with CheckListarXiv 已验证
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, et al. · Association for Computational Linguistics (ACL), 2020 · ribeiro2020checklist
行为测试:按能力维度构造用例
BibTeX
@inproceedings{ribeiro2020checklist,
  author    = {Marco Tulio Ribeiro and Tongshuang Wu and Carlos Guestrin and Sameer Singh},
  title     = {Beyond Accuracy: Behavioral Testing of NLP models with CheckList},
  booktitle = {Association for Computational Linguistics (ACL), 2020},
  year      = {2020},
  eprint    = {2005.04118},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2005.04118},
}
Are Emergent Abilities of Large Language Models a Mirage?arXiv 已验证
Rylan Schaeffer, Brando Miranda, Sanmi Koyejo · arXiv preprint arXiv:2304.15004 · schaeffer2023mirage
“涌现能力”可能是度量假象
BibTeX
@article{schaeffer2023mirage,
  author    = {Rylan Schaeffer and Brando Miranda and Sanmi Koyejo},
  title     = {Are Emergent Abilities of Large Language Models a Mirage?},
  journal   = {arXiv preprint arXiv:2304.15004},
  year      = {2023},
  eprint    = {2304.15004},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url       = {https://arxiv.org/abs/2304.15004},
}

C6 · 推理时计算、ICL 与推理忠实性

Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsarXiv 已验证
Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. · arXiv preprint arXiv:2201.11903 · wei2022cot
CoT 提示(推理时计算的起点)
BibTeX
@article{wei2022cot,
  author    = {Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
  title     = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
  journal   = {arXiv preprint arXiv:2201.11903},
  year      = {2022},
  eprint    = {2201.11903},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2201.11903},
}
Self-Consistency Improves Chain of Thought Reasoning in Language ModelsarXiv 已验证
Xuezhi Wang, Jason Wei, Dale Schuurmans, et al. · ICLR 2023 · wang2022selfconsistency
自洽采样
BibTeX
@inproceedings{wang2022selfconsistency,
  author    = {Xuezhi Wang and Jason Wei and Dale Schuurmans and Quoc Le and Ed Chi and Sharan Narang and Aakanksha Chowdhery and Denny Zhou},
  title     = {Self-Consistency Improves Chain of Thought Reasoning in Language Models},
  booktitle = {ICLR 2023},
  year      = {2022},
  eprint    = {2203.11171},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2203.11171},
}
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model ParametersarXiv 已验证
Charlie Snell, Jaehoon Lee, Kelvin Xu, et al. · arXiv preprint arXiv:2408.03314 · snell2024testtime
测试时算力的最优分配
BibTeX
@article{snell2024testtime,
  author    = {Charlie Snell and Jaehoon Lee and Kelvin Xu and Aviral Kumar},
  title     = {Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters},
  journal   = {arXiv preprint arXiv:2408.03314},
  year      = {2024},
  eprint    = {2408.03314},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2408.03314},
}
s1: Simple test-time scalingarXiv 已验证
Niklas Muennighoff, Zitong Yang, Weijia Shi, et al. · arXiv preprint arXiv:2501.19393 · muennighoff2025s1
s1:简单测试时扩展
BibTeX
@article{muennighoff2025s1,
  author    = {Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto},
  title     = {s1: Simple test-time scaling},
  journal   = {arXiv preprint arXiv:2501.19393},
  year      = {2025},
  eprint    = {2501.19393},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2501.19393},
}
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningarXiv 已验证
DeepSeek-AI, Daya Guo, Dejian Yang, et al. · Nature volume 645, pages 633-638 (2025) · guo2025deepseekr1
R1:RL 训练出的推理模型
BibTeX
@article{guo2025deepseekr1,
  author    = { DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Peiyi Wang and Qihao Zhu and Runxin Xu and Ruoyu Zhang and Shirong Ma and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang},
  title     = {DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
  journal   = {Nature volume 645, pages 633-638 (2025)},
  year      = {2025},
  eprint    = {2501.12948},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2501.12948},
}
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMsarXiv 已验证
Xingyu Chen, Jiahao Xu, Tian Liang, et al. · arXiv preprint arXiv:2412.21187 · chen2024overthinking
过度思考:更长推理反而更差
BibTeX
@article{chen2024overthinking,
  author    = {Xingyu Chen and Jiahao Xu and Tian Liang and Zhiwei He and Jianhui Pang and Dian Yu and Linfeng Song and Qiuzhi Liu and Mengfei Zhou and Zhuosheng Zhang and Rui Wang and Zhaopeng Tu and Haitao Mi and Dong Yu},
  title     = {Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs},
  journal   = {arXiv preprint arXiv:2412.21187},
  year      = {2024},
  eprint    = {2412.21187},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2412.21187},
}
Large Language Monkeys: Scaling Inference Compute with Repeated SamplingarXiv 已验证
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al. · arXiv preprint arXiv:2407.21787 · brown2024monkeys
重复采样扩展推理算力(best-of-n)
BibTeX
@article{brown2024monkeys,
  author    = {Bradley Brown and Jordan Juravsky and Ryan Ehrlich and Ronald Clark and Quoc V. Le and Christopher Ré and Azalia Mirhoseini},
  title     = {Large Language Monkeys: Scaling Inference Compute with Repeated Sampling},
  journal   = {arXiv preprint arXiv:2407.21787},
  year      = {2024},
  eprint    = {2407.21787},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2407.21787},
}
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought PromptingarXiv 已验证
Miles Turpin, Julian Michael, Ethan Perez, et al. · NeurIPS 2023 · turpin2023faithfulness
CoT 解释不忠实
BibTeX
@inproceedings{turpin2023faithfulness,
  author    = {Miles Turpin and Julian Michael and Ethan Perez and Samuel R. Bowman},
  title     = {Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting},
  booktitle = {NeurIPS 2023},
  year      = {2023},
  eprint    = {2305.04388},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2305.04388},
}
Measuring Faithfulness in Chain-of-Thought ReasoningarXiv 已验证
Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al. · arXiv preprint arXiv:2307.13702 · lanham2023measuring
量化 CoT 忠实度
BibTeX
@article{lanham2023measuring,
  author    = {Tamera Lanham and Anna Chen and Ansh Radhakrishnan and Benoit Steiner and Carson Denison and Danny Hernandez and Dustin Li and Esin Durmus and Evan Hubinger and Jackson Kernion and Kamilė Lukošiūtė and Karina Nguyen and Newton Cheng and Nicholas Joseph and Nicholas Schiefer and Oliver Rausch and Robin Larson and Sam McCandlish and Sandipan Kundu and Saurav Kadavath and Shannon Yang and Thomas Henighan and Timothy Maxwell and Timothy Telleen-Lawton and Tristan Hume and Zac Hatfield-Dodds and Jared Kaplan and Jan Brauner and Samuel R. Bowman and Ethan Perez},
  title     = {Measuring Faithfulness in Chain-of-Thought Reasoning},
  journal   = {arXiv preprint arXiv:2307.13702},
  year      = {2023},
  eprint    = {2307.13702},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url       = {https://arxiv.org/abs/2307.13702},
}
Language Models are Few-Shot LearnersarXiv 已验证
Tom B. Brown, Benjamin Mann, Nick Ryder, et al. · arXiv preprint arXiv:2005.14165 · brown2020gpt3
GPT-3:few-shot 上下文学习
BibTeX
@article{brown2020gpt3,
  author    = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
  title     = {Language Models are Few-Shot Learners},
  journal   = {arXiv preprint arXiv:2005.14165},
  year      = {2020},
  eprint    = {2005.14165},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2005.14165},
}
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?arXiv 已验证
Sewon Min, Xinxi Lyu, Ari Holtzman, et al. · EMNLP 2022 · min2022rethinking
示例的作用主要是格式与标签空间
BibTeX
@inproceedings{min2022rethinking,
  author    = {Sewon Min and Xinxi Lyu and Ari Holtzman and Mikel Artetxe and Mike Lewis and Hannaneh Hajishirzi and Luke Zettlemoyer},
  title     = {Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?},
  booktitle = {EMNLP 2022},
  year      = {2022},
  eprint    = {2202.12837},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2202.12837},
  note      = {17 pages; 12 figures. Code available at https://github.com/Alrope123/rethinking-demonst…},
}
Data Distributional Properties Drive Emergent In-Context Learning in TransformersarXiv 已验证
Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, et al. · NeurIPS 2022 · chan2022datadistributional
ICL 依赖训练数据分布
BibTeX
@inproceedings{chan2022datadistributional,
  author    = {Stephanie C. Y. Chan and Adam Santoro and Andrew K. Lampinen and Jane X. Wang and Aaditya Singh and Pierre H. Richemond and Jay McClelland and Felix Hill},
  title     = {Data Distributional Properties Drive Emergent In-Context Learning in Transformers},
  booktitle = {NeurIPS 2022},
  year      = {2022},
  eprint    = {2205.05055},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2205.05055},
  note      = {Code is available at: https://github.com/deepmind/emergent_in_context_learning},
}
What Makes Good In-Context Examples for GPT-$3$?arXiv 已验证
Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al. · arXiv preprint arXiv:2101.06804 · liu2022whatmakesgood
什么样的示例才是好示例
BibTeX
@article{liu2022whatmakesgood,
  author    = {Jiachang Liu and Dinghan Shen and Yizhe Zhang and Bill Dolan and Lawrence Carin and Weizhu Chen},
  title     = {What Makes Good In-Context Examples for GPT-$3$?},
  journal   = {arXiv preprint arXiv:2101.06804},
  year      = {2021},
  eprint    = {2101.06804},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2101.06804},
}

C7 · 经验复用、记忆与知识积累

Reflexion: Language Agents with Verbal Reinforcement LearningarXiv 已验证
Noah Shinn, Federico Cassano, Edward Berman, et al. · arXiv preprint arXiv:2303.11366 · shinn2023reflexion
语言化反思记忆(verbal RL)
BibTeX
@article{shinn2023reflexion,
  author    = {Noah Shinn and Federico Cassano and Edward Berman and Ashwin Gopinath and Karthik Narasimhan and Shunyu Yao},
  title     = {Reflexion: Language Agents with Verbal Reinforcement Learning},
  journal   = {arXiv preprint arXiv:2303.11366},
  year      = {2023},
  eprint    = {2303.11366},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url       = {https://arxiv.org/abs/2303.11366},
}
ExpeL: LLM Agents Are Experiential LearnersarXiv 已验证
Andrew Zhao, Daniel Huang, Quentin Xu, et al. · arXiv preprint arXiv:2308.10144 · zhao2023expel
从轨迹提取可复用经验
BibTeX
@article{zhao2023expel,
  author    = {Andrew Zhao and Daniel Huang and Quentin Xu and Matthieu Lin and Yong-Jin Liu and Gao Huang},
  title     = {ExpeL: LLM Agents Are Experiential Learners},
  journal   = {arXiv preprint arXiv:2308.10144},
  year      = {2023},
  eprint    = {2308.10144},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2308.10144},
}
Voyager: An Open-Ended Embodied Agent with Large Language ModelsarXiv 已验证
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, et al. · arXiv preprint arXiv:2305.16291 · wang2023voyager
技能库式终身学习
BibTeX
@article{wang2023voyager,
  author    = {Guanzhi Wang and Yuqi Xie and Yunfan Jiang and Ajay Mandlekar and Chaowei Xiao and Yuke Zhu and Linxi Fan and Anima Anandkumar},
  title     = {Voyager: An Open-Ended Embodied Agent with Large Language Models},
  journal   = {arXiv preprint arXiv:2305.16291},
  year      = {2023},
  eprint    = {2305.16291},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url       = {https://arxiv.org/abs/2305.16291},
}
Agentic Context Engineering: Evolving Contexts for Self-Improving Language ModelsarXiv 已验证
Qizheng Zhang, Changran Hu, Shubhangi Upasani, et al. · ICLR 2026 · zhang2025ace
上下文即playbook,持续进化
BibTeX
@inproceedings{zhang2025ace,
  author    = {Qizheng Zhang and Changran Hu and Shubhangi Upasani and Boyuan Ma and Fenglu Hong and Vamsidhar Kamanuru and Jay Rainton and Chen Wu and Mengmeng Ji and Hanchen Li and Urmish Thakker and James Zou and Kunle Olukotun},
  title     = {Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models},
  booktitle = {ICLR 2026},
  year      = {2025},
  eprint    = {2510.04618},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2510.04618},
}
EvolveR: Self-Evolving LLM Agents through an Experience-Driven LifecyclearXiv 已验证
Rong Wu, Xiaoman Wang, Jianbiao Mei, et al. · ICML 2026 · evolver2025
经验驱动生命周期(ICML 2026)
BibTeX
@inproceedings{evolver2025,
  author    = {Rong Wu and Xiaoman Wang and Jianbiao Mei and Pinlong Cai and Daocheng Fu and Cheng Yang and Licheng Wen and Xuemeng Yang and Yufan Shen and Yuxin Wang and Botian Shi},
  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},
}
A-MEM: Agentic Memory for LLM AgentsarXiv 已验证
Wujiang Xu, Zujie Liang, Kai Mei, et al. · NeurIPS 2025 · wang2025amem
agentic memory(自组织记忆网络)
BibTeX
@inproceedings{wang2025amem,
  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},
  year      = {2025},
  eprint    = {2502.12110},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2502.12110},
}
MemoryBank: Enhancing Large Language Models with Long-Term MemoryarXiv 已验证
Wanjun Zhong, Lianghong Guo, Qiqi Gao, et al. · arXiv preprint arXiv:2305.10250 · zhong2023memorybank
长期记忆与遗忘机制
BibTeX
@article{zhong2023memorybank,
  author    = {Wanjun Zhong and Lianghong Guo and Qiqi Gao and He Ye and Yanlin Wang},
  title     = {MemoryBank: Enhancing Large Language Models with Long-Term Memory},
  journal   = {arXiv preprint arXiv:2305.10250},
  year      = {2023},
  eprint    = {2305.10250},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2305.10250},
}
SkillOpt: Executive Strategy for Self-Evolving Agent SkillsarXiv 已验证
Yifan Yang, Ziyang Gong, Weiquan Huang, et al. · arXiv preprint arXiv:2605.23904 · yuan2026skillopt
把技能文档当外部状态来优化
BibTeX
@article{yuan2026skillopt,
  author    = {Yifan Yang and Ziyang Gong and Weiquan Huang and Qihao Yang and Ziwei Zhou and Zisu Huang and Yan Li and Xuemei Gao and Qi Dai and Bei Liu and Kai Qiu and Yuqing Yang and Dongdong Chen and Xue Yang and Chong Luo},
  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},
}
AI4AI at Test-Time: Strong-to-Weak Capability Transfer via HarnessesarXiv 已验证
Cheng Qian, Wenting Zhao, Liangwei Yang, et al. · arXiv preprint arXiv:2608.12307 · ai4aittesttime2026
强模型搭 harness 把能力传给弱模型
BibTeX
@article{ai4aittesttime2026,
  author    = {Cheng Qian and Wenting Zhao and Liangwei Yang and Heng Wang and Jielin Qiu and Heng Ji and Silvio Savarese and Huan Wang and Shelby Heinecke},
  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},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2608.12307},
}
TextGrad: Automatic "Differentiation" via TextarXiv 已验证
Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al. · arXiv preprint arXiv:2406.07496 · yuksekgonul2024textgrad
文本梯度:优化提示/代码
BibTeX
@article{yuksekgonul2024textgrad,
  author    = {Mert Yuksekgonul and Federico Bianchi and Joseph Boen and Sheng Liu and Zhi Huang and Carlos Guestrin and James Zou},
  title     = {TextGrad: Automatic "Differentiation" via Text},
  journal   = {arXiv preprint arXiv:2406.07496},
  year      = {2024},
  eprint    = {2406.07496},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2406.07496},
}
DSPy: Compiling Declarative Language Model Calls into Self-Improving PipelinesarXiv 已验证
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al. · arXiv preprint arXiv:2310.03714 · khattab2023dspy
声明式流水线编译优化
BibTeX
@article{khattab2023dspy,
  author    = {Omar Khattab and Arnav Singhvi and Paridhi Maheshwari and Zhiyuan Zhang and Keshav Santhanam and Sri Vardhamanan and Saiful Haq and Ashutosh Sharma and Thomas T. Joshi and Hanna Moazam and Heather Miller and Matei Zaharia and Christopher Potts},
  title     = {DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines},
  journal   = {arXiv preprint arXiv:2310.03714},
  year      = {2023},
  eprint    = {2310.03714},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2310.03714},
}
Promptbreeder: Self-Referential Self-Improvement Via Prompt EvolutionarXiv 已验证
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al. · arXiv preprint arXiv:2309.16797 · fernando2023promptbreeder
提示自进化(含变异提示)
BibTeX
@article{fernando2023promptbreeder,
  author    = {Chrisantha Fernando and Dylan Banarse and Henryk Michalewski and Simon Osindero and Tim Rocktäschel},
  title     = {Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution},
  journal   = {arXiv preprint arXiv:2309.16797},
  year      = {2023},
  eprint    = {2309.16797},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2309.16797},
}
Self-Rewarding Language ModelsarXiv 已验证
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, et al. · ICML 2024 · yuan2024selfrewarding
自我奖励式迭代对齐
BibTeX
@inproceedings{yuan2024selfrewarding,
  author    = {Weizhe Yuan and Richard Yuanzhe Pang and Kyunghyun Cho and Xian Li and Sainbayar Sukhbaatar and Jing Xu and Jason Weston},
  title     = {Self-Rewarding Language Models},
  booktitle = {ICML 2024},
  year      = {2024},
  eprint    = {2401.10020},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2401.10020},
}
STaR: Bootstrapping Reasoning With ReasoningarXiv 已验证
Eric Zelikman, Yuhuai Wu, Jesse Mu, et al. · arXiv preprint arXiv:2203.14465 · zelikman2022star
STaR:自举推理
BibTeX
@article{zelikman2022star,
  author    = {Eric Zelikman and Yuhuai Wu and Jesse Mu and Noah D. Goodman},
  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},
}
Quiet-STaR: Language Models Can Teach Themselves to Think Before SpeakingarXiv 已验证
Eric Zelikman, Georges Harik, Yijia Shao, et al. · arXiv preprint arXiv:2403.09629 · zelikman2024quietstar
Quiet-STaR:隐式推理训练
BibTeX
@article{zelikman2024quietstar,
  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},
}
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language ModelsarXiv 已验证
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, et al. · arXiv preprint arXiv:2310.04406 · chen2024lats
树搜索 + 反思的 agent
BibTeX
@article{chen2024lats,
  author    = {Andy Zhou and Kai Yan and Michal Shlapentokh-Rothman and Haohan Wang and Yu-Xiong Wang},
  title     = {Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models},
  journal   = {arXiv preprint arXiv:2310.04406},
  year      = {2023},
  eprint    = {2310.04406},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url       = {https://arxiv.org/abs/2310.04406},
}
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-JudgearXiv 已验证
Tianhao Wu, Weizhe Yuan, Olga Golovneva, et al. · arXiv preprint arXiv:2407.19594 · wu2024metarewarding
元评审式自我改进
BibTeX
@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},
}
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language ModelsarXiv 已验证
Avi Singh, John D. Co-Reyes, Rishabh Agarwal, et al. · arXiv preprint arXiv:2312.06585 · singh2023restem
自训练超越人类数据(ReST-EM)
BibTeX
@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},
}
Large Language Models Can Self-ImprovearXiv 已验证
Jiaxin Huang, Shixiang Shane Gu, Le Hou, et al. · arXiv preprint arXiv:2210.11610 · huang2022selfimprove
自生成伪标签自改进
BibTeX
@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},
}
Self-Instruct: Aligning Language Models with Self-Generated InstructionsarXiv 已验证
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, et al. · ACL 2023 · wang2022selfinstruct
Self-Instruct:自举指令数据
BibTeX
@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},
}
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI FeedbackarXiv 已验证
Harrison Lee, Samrat Phatale, Hassan Mansoor, et al. · Proceedings of the 41st International Conference on Machine Learning, PMLR 235:26874-26901, 2024 · lee2023rlaif
AI 反馈替代人类反馈
BibTeX
@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},
}
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language ModelsarXiv 已验证
Zixiang Chen, Yihe Deng, Huizhuo Yuan, et al. · ICML 2024 · chen2024spin
自博弈微调
BibTeX
@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},
}
Self-Adapting Language ModelsarXiv 已验证
Adam Zweiger, Jyothish Pari, Han Guo, et al. · arXiv preprint arXiv:2506.10943 · zweiger2025seal
SEAL:模型生成自己的更新
BibTeX
@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},
}
Self-Improvement in Language Models: The Sharpening MechanismarXiv 已验证
Audrey Huang, Adam Block, Dylan J. Foster, et al. · arXiv preprint arXiv:2412.01951 · ye2024sharpening
自我改进的锐化机制(理论)
BibTeX
@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},
}
Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksarXiv 已验证
Patrick Lewis, Ethan Perez, Aleksandra Piktus, et al. · NeurIPS 2020 · lewis2020rag
检索增强生成(外部知识注入)
BibTeX
@inproceedings{lewis2020rag,
  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},
  title     = {Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks},
  booktitle = {NeurIPS 2020},
  year      = {2020},
  eprint    = {2005.11401},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2005.11401},
}

C8 · 训练动力学事实(基线与残差分析)

Adam: A Method for Stochastic OptimizationarXiv 已验证
Diederik P. Kingma, Jimmy Ba · arXiv preprint arXiv:1412.6980 · kingma2014adam
Adam 优化器
BibTeX
@article{kingma2014adam,
  author    = {Diederik P. Kingma and Jimmy Ba},
  title     = {Adam: A Method for Stochastic Optimization},
  journal   = {arXiv preprint arXiv:1412.6980},
  year      = {2014},
  eprint    = {1412.6980},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/1412.6980},
}
Decoupled Weight Decay RegularizationarXiv 已验证
Ilya Loshchilov, Frank Hutter · ICLR 2019 · loshchilov2017adamw
AdamW:解耦权重衰减
BibTeX
@inproceedings{loshchilov2017adamw,
  author    = {Ilya Loshchilov and Frank Hutter},
  title     = {Decoupled Weight Decay Regularization},
  booktitle = {ICLR 2019},
  year      = {2017},
  eprint    = {1711.05101},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/1711.05101},
}
The Marginal Value of Adaptive Gradient Methods in Machine LearningarXiv 已验证
Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, et al. · arXiv preprint arXiv:1705.08292 · wilson2017adaptive
自适应方法的边际价值(泛化争议)
BibTeX
@article{wilson2017adaptive,
  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},
}
Don't Decay the Learning Rate, Increase the Batch SizearXiv 已验证
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, et al. · ICLR 2018 · smith2018dontdecay
LR 与 batch size 的等价性
BibTeX
@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},
}
An Empirical Model of Large-Batch TrainingarXiv 已验证
Sam McCandlish, Jared Kaplan, Dario Amodei, et al. · arXiv preprint arXiv:1812.06162 · mccandlish2018largbatch
临界 batch 与有效步长模型
BibTeX
@article{mccandlish2018largbatch,
  author    = {Sam McCandlish and Jared Kaplan and Dario Amodei and OpenAI Dota Team},
  title     = {An Empirical Model of Large-Batch Training},
  journal   = {arXiv preprint arXiv:1812.06162},
  year      = {2018},
  eprint    = {1812.06162},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/1812.06162},
}
Scaling Laws for Neural Language ModelsarXiv 已验证
Jared Kaplan, Sam McCandlish, Tom Henighan, et al. · arXiv preprint arXiv:2001.08361 · kaplan2020scalinglaws
神经标度律
BibTeX
@article{kaplan2020scalinglaws,
  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},
  title     = {Scaling Laws for Neural Language Models},
  journal   = {arXiv preprint arXiv:2001.08361},
  year      = {2020},
  eprint    = {2001.08361},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2001.08361},
}
Training Compute-Optimal Large Language ModelsarXiv 已验证
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al. · arXiv preprint arXiv:2203.15556 · hoffmann2022chinchilla
算力最优训练(Chinchilla)
BibTeX
@article{hoffmann2022chinchilla,
  author    = {Jordan Hoffmann and Sebastian Borgeaud and Arthur Mensch and Elena Buchatskaya and Trevor Cai and Eliza Rutherford and Diego de Las Casas and Lisa Anne Hendricks and Johannes Welbl and Aidan Clark and Tom Hennigan and Eric Noland and Katie Millican and George van den Driessche and Bogdan Damoc and Aurelia Guy and Simon Osindero and Karen Simonyan and Erich Elsen and Jack W. Rae and Oriol Vinyals and Laurent Sifre},
  title     = {Training Compute-Optimal Large Language Models},
  journal   = {arXiv preprint arXiv:2203.15556},
  year      = {2022},
  eprint    = {2203.15556},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url       = {https://arxiv.org/abs/2203.15556},
}
Why do tree-based models still outperform deep learning on tabular data?arXiv 已验证
Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux · arXiv preprint arXiv:2207.08815 · grinsztajn2022tabular
树模型为何仍统治表格数据
BibTeX
@article{grinsztajn2022tabular,
  author    = {Léo Grinsztajn and Edouard Oyallon and Gaël Varoquaux},
  title     = {Why do tree-based models still outperform deep learning on tabular data?},
  journal   = {arXiv preprint arXiv:2207.08815},
  year      = {2022},
  eprint    = {2207.08815},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2207.08815},
}
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondarXiv 已验证
Noah Hollmann, Samuel Müller, Katharina Eggensperger, et al. · arXiv preprint arXiv:2207.01848 · hollmann2022tabpfn
TabPFN:小样本表格分类
BibTeX
@article{hollmann2022tabpfn,
  author    = {Noah Hollmann and Samuel Müller and Katharina Eggensperger and Frank Hutter},
  title     = {TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second},
  journal   = {arXiv preprint arXiv:2207.01848},
  year      = {2022},
  eprint    = {2207.01848},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url       = {https://arxiv.org/abs/2207.01848},
}

C9 · 算法与统计方法引用

Finite-time Analysis of the Multiarmed Bandit Problem人工核对
Peter Auer, Nicolò Cesa-Bianchi, Paul Fischer · Machine Learning, 47(2-3):235-256 · auer2002ucb
UCB1:探索-利用的经典界(§5.1 公式来源)
BibTeX
@article{auer2002ucb,
  author    = {Peter Auer and Nicolò Cesa-Bianchi and Paul Fischer},
  title     = {Finite-time Analysis of the Multiarmed Bandit Problem},
  journal   = {Machine Learning, 47(2-3):235-256},
  year      = {2002},
  url       = {https://doi.org/10.1023/A:1013689704352},
}
Mastering the Game of Go without Human Knowledge人工核对
David Silver, Julian Schrittwieser, Karen Simonyan, et al. · Nature, 550(7676):354-359 · silver2017alphagozero
AlphaGo Zero:PUCT 与树搜索(§5.1 公式来源)
BibTeX
@article{silver2017alphagozero,
  author    = {David Silver and Julian Schrittwieser and Karen Simonyan and Ioannis Antonoglou and Aja Huang and Arthur Guez and Thomas Hubert and Lucas Baker and Matthew Lai and Adrian Bolton and Yutian Chen and Timothy Lillicrap and Fan Hui and Laurent Sifre and George van den Driessche and Thore Graepel and Demis Hassabis},
  title     = {Mastering the Game of Go without Human Knowledge},
  journal   = {Nature, 550(7676):354-359},
  year      = {2017},
  url       = {https://doi.org/10.1038/nature24270},
}
A Survey of Monte Carlo Tree Search Methods人工核对
Cameron B. Browne, Edward Powley, Daniel Whitehouse, et al. · IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1-43 · browne2012mcts
MCTS 综述(PUCT 背景,脚注备选)
BibTeX
@article{browne2012mcts,
  author    = {Cameron B. Browne and Edward Powley and Daniel Whitehouse and Simon M. Lucas and Peter I. Cowling and Philipp Rohlfshagen and Stephen Tavener and Diego Perez and Spyridon Samothrakis and Simon Colton},
  title     = {A Survey of Monte Carlo Tree Search Methods},
  journal   = {IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1-43},
  year      = {2012},
  url       = {https://doi.org/10.1109/TCIAIG.2012.2186810},
}
Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages人工核对
Quinn McNemar · Psychometrika, 12(2):153-157 · mcnemar1947
McNemar 检验(§5.2 显著性检验引用)
BibTeX
@article{mcnemar1947,
  author    = {Quinn McNemar},
  title     = {Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages},
  journal   = {Psychometrika, 12(2):153-157},
  year      = {1947},
  url       = {https://doi.org/10.1007/BF02295996},
}
Greedy Function Approximation: A Gradient Boosting Machine人工核对
Jerome H. Friedman · The Annals of Statistics, 29(5):1189-1232 · friedman2001gbm
梯度提升机原始文献(§4.3 基线方法)
BibTeX
@article{friedman2001gbm,
  author    = {Jerome H. Friedman},
  title     = {Greedy Function Approximation: A Gradient Boosting Machine},
  journal   = {The Annals of Statistics, 29(5):1189-1232},
  year      = {2001},
  url       = {https://doi.org/10.1214/aos/1013203451},
}

6 验证方法与可信度

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)。

7 下一步(经确认后再执行)

  1. 筛条目:从 T1 开始逐条确认;T3 备选池可整段删除而不影响论证。
  2. 补中文稿相关工作段落:论文 §2 建议按 2.1–2.6 六段写(编号见第 2/3 节),先写 2.4(最接近的工作)以避免被质疑“没引直接竞争工作”。
  3. 生成 references.bib:直接用 refs_work/references_verified.bib,但先核对 2026 年会议的 venue 字段。
  4. 写入 overleaf-aiq:把 \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)。
  5. 复核:跑一遍 tools/validate_quiz_bake.py 无关;论文侧只需 latexmk 编译通过 + 引用无 undefined。