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  "errors": [],
  "monitoring": {
    "snapshot_refresh_seconds": 900,
    "automatic_agent_followup": false,
    "last_agent_check_at": 1790391476.1510358,
    "last_agent_check_label": "2026-09-25 22:57 EDT",
    "agent_check_age_minutes": 6753.159442818165
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  "incidents": [
    {
      "title": "自动快照与 agent 巡检分别记录",
      "detail": "页面每15分钟自动采集任务状态。Agent最近实际巡检：2026-09-25 22:57 EDT。当前没有已安排的后台agent巡检；页面刷新不会自动分析结果或安排新实验。已提交任务的自动续跑由现有队列执行。"
    },
    {
      "title": "A 配套实验：共享调度中断",
      "detail": "24个配置共41次短尝试受到共享调度服务中断影响，消耗197 GPU秒，记录均保留。现使用独立任务清单和新的完整训练预算；这不是算法结果。"
    },
    {
      "title": "32 组网格：完整训练超出最初时限",
      "detail": "全部通过模型、优化器和随机状态恢复检查；实测速度预测超过7200秒，因此在32步资格检查后停止。旧尝试共1533 GPU秒，已计入成本。完整配置与horizon不变，重新从相同base运行，单次预算调整为12000秒。"
    }
  ],
  "questions": [
    {
      "priority": "P0",
      "title": "CPT：能否学得更多，同时忘得更少？",
      "why": "T³ 包含训练时适应、测试与反馈的不同算法；检验它们能否超过调参的普通 CPT、replay 和固定教师蒸馏。",
      "next": "56条完整训练已核验，开发集192个T³点中0个通过完整普通前沿、0个同时有own-zero支持。48条曲线：完成48、运行0、失败0；共384固定checkpoint。复用已验证的512篇文档与base评分，单seed探索；无收益也完整报告。当前最小半径的目标学习最好，但仍须结合完整普通早停前沿；不会用更弱终点制造收益。 48条曲线、384点评分与完整普通前沿已核验。两个数据比例中0个固定主比较通过探索性门槛。区间为配对文档bootstrap，只有一个训练seed，不能视为独立确认。 135M32条轨迹及26个选定checkpoint评分完成。K8接近普通LR缩小十倍；K64、四遍数据、20% replay相对预选普通LR1e-5第1536步：目标域代价+0.000444，FineWeb改善0.001504 NLL。单seed、512篇已观察文档，尚未独立确认；接下来加密普通LR/早停checkpoint，排除稀疏对照造成的优势。训练20.677、评分0.290 GPU小时。Wave2a和360M的执行状态见下方各实验进度；仅使用B。 密集对照已完成：新增44点及原状态精确复现。最强匹配普通对照变为LR1e-5第1376步，K64目标NLL降低0.000430、FineWeb降低0.001217；仍是单seed开发集。各512篇新数学/FineWeb文档及9项评分已准备，尚未启动评分。加密成本含失败启动共0.641GPU小时。 独立文档评分已完成并通过固定主比较：数学基本不变、FineWeb降低0.001446。当前结论只覆盖一个训练seed的标准Lookahead风格对照，尚非预训练FOMAML算子的收益证据。 360M32条训练也已完成：同一K64/四遍数据/20%replay配置保留开发集信号，相对最强匹配普通对照，数学代价+0.000074、FineWeb降低0.001117；单seed，尚未独立评分。"
    },
    {
      "priority": "P0",
      "title": "Muon：为什么 T³ 的增益较小？",
      "why": "系数、临时适应方向和外层更新可能改变收益，跨项目差异不能直接归因于一个因素。",
      "next": "以 own-zero 为主对照；当前系数比较已接受，避免把不同 setting 的差距写成因果解释。"
    }
  ],
  "plans": [
    {
      "priority": "P0",
      "title": "TRAIN-query：测试信号能否改善轨迹提交？",
      "family": "query_signal_probe",
      "why": "已知固定插值有效，但尚未证明训练时测试提供额外帮助；先检查局部选择信号。",
      "design": "4个起点×2个临时LR×2组数据抽样；每段64步、7个插值幅度；两种仅用TRAIN-query的决策在不重叠审计batch上比较。",
      "decision": "配置已准备 16/16；运行 0，训练完成 16，失败／中断 0，待启动／确认 0。 局部reset-Adam探针，无新CPT有效性结论；完整训练需固定插值、普通CPT与额外query数据对照。"
    },
    {
      "priority": "P0",
      "title": "135M：更强普通对照下的三个配对seed",
      "family": "refined_paired_seeds",
      "why": "新文档验证通过后，检验收益是否依赖seed0训练顺序；复用候选避免重复计算。",
      "design": "三个新普通LR8.75e-6训练，与已有K64/alpha0.1/LR1e-4候选seed1/2/3严格配对。主比较2048对1664，次比较均2048；九个固定评分点。",
      "decision": "三组配对训练seed和9点评分全部核验；通过预定训练seed门槛。平均数学差-0.001192、FineWeb差-0.001339。区间以三个训练seed为单位；候选来自已知Lookahead，normalized T³ null保持不变。"
    },
    {
      "priority": "P0",
      "title": "Lookahead：更强普通对照下的新文档验证",
      "family": "independent_documents",
      "why": "排除对已使用文档反复筛选的影响，先查固定比较能否泛化。",
      "design": "135M/360M各4个固定点；每域512篇新文档，排除旧评估、开发窗口和TRAIN文档。8个评分任务，上限2 GPU小时。",
      "decision": "8个固定点在未用于此前筛选的文档上完成评分。 135M数学差-0.001212、FineWeb差-0.001226；达到预定的后续seed验证门槛。 360M数学差-0.001392、FineWeb差-0.000990；未达到预定的后续seed验证门槛。 区间只反映文档变化，仍是一个训练seed。Lookahead与normalized T³分开，后者wave2a结果不变。"
    },
    {
      "priority": "P0",
      "title": "普通CPT学习率细调：挑战两种模型上的Lookahead信号",
      "family": "ordinary_lr_refinement",
      "why": "排除普通基线LR调得过粗；固定候选，先加强对照再加seed。",
      "design": "135M/360M各3个新普通LR：7.5e-6、8.75e-6、1.25e-5；其余seed0数据、replay、优化器与2048步计划不变。每条45个科学评估点，共270点。",
      "decision": "六条新普通LR曲线、270个预定评分点均已完成核验。 135M固定Lookahead候选相对更强普通对照：数学NLL差-0.000887、FineWeb差-0.001159，通过预定诊断门槛。 360M固定Lookahead候选相对更强普通对照：数学NLL差-0.000667、FineWeb差-0.001033，通过预定诊断门槛。 单seed、已用文档；对照选择未校正。旧三seed结果只覆盖原先固定的对照；Lookahead与T³单列。"
    },
    {
      "priority": "P0",
      "title": "Wave 2a：完整外部评分与前沿读出",
      "family": "overfit_external",
      "why": "截止前优先回答预训练T³算子在CPT中是否改善学习与遗忘。",
      "design": "18条普通LR×WD曲线、24条非零半径曲线、6条own-zero；每条8个固定点，共384点评分。",
      "decision": "48条曲线、384点评分与完整普通前沿已核验。两个数据比例中0个固定主比较通过探索性门槛。区间为配对文档bootstrap，只有一个训练seed，不能视为独立确认。"
    },
    {
      "priority": "P1",
      "title": "Lookahead：三个配对训练seed验证",
      "family": "paired_confirmation",
      "why": "独立文档上的小幅保留收益，需要排除训练顺序的偶然性。",
      "design": "三个固定seed × K64/调参普通训练；预先固定15个评分点，沿用已冻结的512+512文档。",
      "decision": "6条配对训练和15个固定评分全部完成；通过预定三seed验证门槛。平均数学NLL差-0.000157，FineWeb差-0.001667。三个新训练顺序seed，seed0不计入；区间按seed计算。主比较2048对1376步，多49%更新。同2048步时数学代价+0.001540，不能声称计算效率提升。Lookahead-style interpolation单列，不能归因于normalized T³。"
    },
    {
      "priority": "P2",
      "title": "360M Lookahead：完整外部曲线",
      "family": "lookahead360_external",
      "why": "检验同一插值配方在更大模型上的开发集信号，保留完整普通LR与早停挑战。",
      "design": "32条已完成训练，各5个固定点评分，另一个共享base；复用通过的360M数值检查。",
      "decision": "32条曲线/161点评分全部核验；固定主比较通过探索性门槛。数学NLL差+0.000301、FineWeb差-0.001300。一个训练seed，已使用的512篇文档；仍需密集普通对照和独立文档，不能当作确认。Lookahead单列。"
    },
    {
      "priority": "P0",
      "title": "360M：密集普通早停对照",
      "family": "dense360_ordinary",
      "why": "排除普通checkpoint过稀导致的表面前沿优势；候选保持固定。",
      "design": "三个普通LR区间，每32步补观测，共68点；从原状态续算，已有权重点必须精确一致；全部新增点做外部评分。",
      "decision": "68新点全部评分，6重合点逐文档一致；最强兼容普通对照下通过诊断门槛。数学NLL差+0.000456、FineWeb差-0.001137。单seed、已用文档，区间未校正对照选择；Lookahead单列，尚非确认。"
    },
    {
      "priority": "P0",
      "title": "Wave 2a：小数据过拟合与预训练T³算子",
      "family": "pretraining_overfit",
      "why": "检验普通CPT出现目标域过拟合后，预训练中有效的T³算子是否改善学习–遗忘前沿。",
      "design": "1/32与1/64数学数据；LR 3e-6/1e-5/3e-5 × WD .1/.5/1，保存完整早停对照。通过过拟合门槛后才运行四个非零半径。",
      "decision": "56条完整训练已核验，开发集192个T³点中0个通过完整普通前沿、0个同时有own-zero支持。48条曲线：完成48、运行0、失败0；共384固定checkpoint。复用已验证的512篇文档与base评分，单seed探索；无收益也完整报告。当前最小半径的目标学习最好，但仍须结合完整普通早停前沿；不会用更弱终点制造收益。 48条曲线、384点评分与完整普通前沿已核验。两个数据比例中0个固定主比较通过探索性门槛。区间为配对文档bootstrap，只有一个训练seed，不能视为独立确认。"
    },
    {
      "priority": "P0",
      "title": "K64候选：独立文档验证",
      "family": "fresh_validation",
      "why": "密集普通对照后仍有约0.0012的开发集保留收益，需要未用于选择的文档验证。",
      "design": "数学/FineWeb各512篇新文档；排除历史评估和训练重复文本。固定候选、两个密集普通点、原始早停/终点及own-zero，另复用已有共同文档候选评分。",
      "decision": "9项独立/共同文档评分全部完成，固定主比较通过。相对普通LR1e-5第1376步，数学NLL差值−0.000039（95%文档区间−0.000403至+0.000329），FineWeb差值−0.001446（−0.001559至−0.001338）。单训练seed；下一步3个配对seed，尚未完成训练seed验证。同曝光2048步普通对照的数学代价+0.001256，仍须明确报告；不能据此声称计算效率提升。"
    },
    {
      "priority": "P0",
      "title": "加密普通对照：先挑战现有小幅信号",
      "family": "dense_ordinary",
      "why": "现有K64优势依赖早停匹配，普通checkpoint过稀可能制造前沿空隙。",
      "design": "从保存的AdamW与RNG状态重算三个原始区间，每32步观察；已有checkpoint处必须逐项完全一致。",
      "decision": "配置已准备 3/3；运行 0，训练完成 3，失败／中断 0，待启动／确认 0。只在开发集挑最强兼容对照，再做固定文档评分；暂不加seed。"
    },
    {
      "priority": "P0",
      "title": "持续搜索：多步轨迹反馈 × 模型规模 × 数据复用，64组",
      "family": "trajectory_feedback",
      "why": "检验长适应轨迹是否改善学习–遗忘前沿，并排除普通步长缩小或replay的解释。",
      "design": "135M/360M × one-pass/quarter-four-pass；每个setting有8个调参普通对照和8个轨迹反馈配置。",
      "decision": "135M32条轨迹及26个选定checkpoint评分完成。K8接近普通LR缩小十倍；K64、四遍数据、20% replay相对预选普通LR1e-5第1536步：目标域代价+0.000444，FineWeb改善0.001504 NLL。单seed、512篇已观察文档，尚未独立确认；接下来加密普通LR/早停checkpoint，排除稀疏对照造成的优势。训练20.677、评分0.290 GPU小时。Wave2a和360M的执行状态见下方各实验进度；仅使用B。 密集对照已完成：新增44点及原状态精确复现。最强匹配普通对照变为LR1e-5第1376步，K64目标NLL降低0.000430、FineWeb降低0.001217；仍是单seed开发集。各512篇新数学/FineWeb文档及9项评分已准备，尚未启动评分。加密成本含失败启动共0.641GPU小时。 独立文档评分已完成并通过固定主比较：数学基本不变、FineWeb降低0.001446。当前结论只覆盖一个训练seed的标准Lookahead风格对照，尚非预训练FOMAML算子的收益证据。 360M32条训练也已完成：同一K64/四遍数据/20%replay配置保留开发集信号，相对最强匹配普通对照，数学代价+0.000074、FineWeb降低0.001117；单seed，尚未独立评分。"
    },
    {
      "priority": "P0",
      "title": "新目标：测试内容 × 元梯度方式，32 组",
      "family": "bilevel",
      "why": "已有负结果只覆盖部分 FOMAML 实现；区分缺少旧知识测试与忽略适应导数两种解释。",
      "design": "Smol135M 数学四分之一数据：一阶／完整元梯度 × 新域／新旧域 query × 两个适应半径 × 两个 outer LR；另有16组调参的普通训练与 replay 对照。",
      "decision": "完整32组及全部固定点已核验：精确反馈优于一阶反馈，但全部16个适应终点均被普通训练超过；关闭当前半径/导数网格。"
    },
    {
      "priority": "P0",
      "title": "适应后教师的预测能否带来收益？16 组",
      "family": "distillation",
      "why": "测试蒸馏反馈是否不同于适应后梯度，并排除普通模型锚定的收益。",
      "design": "四分之一数据／一次遍历 × 两个 outer LR × 适应后教师／固定初始教师 × 两个 KL 系数；只用新域训练数据，配普通训练零控制。",
      "decision": "完整16组已核验：8/8适应教师比固定教师学得更多、也忘得更多。beta10保留收益达到0.01量级但有学习代价，未通过完整普通前沿筛选；不加seed。"
    },
    {
      "priority": "P0",
      "title": "一次遍历 S+Q 曝光对照，4 组",
      "family": "support_baseline",
      "why": "适应后教师额外看过 support 数据，必须排除普通训练的数据曝光收益。",
      "design": "一次遍历：普通 AdamW 同时使用 S+Q，四个 outer LR；保存与教师实验相同的四个固定checkpoint。",
      "decision": "4组和全部固定checkpoint已核验，纳入一次遍历完整普通训练前沿。"
    },
    {
      "priority": "P0",
      "title": "完整训练中放大修正是否有用？6 组",
      "family": "fieldgain",
      "why": "局部TRAIN上的学习代价不能回答最终泛化，也缺少中段优化器状态。",
      "design": "数学：原一次遍历／四分之一数据四遍 × gains0/1/10；outer AdamW3e-5；固定四个曲线点并挑战全部已测普通AdamW点。",
      "decision": "完整结果已核验：标准反馈增量小，10倍反馈崩溃；没有候选通过预定筛选，关闭该放大分支。"
    },
    {
      "priority": "P0",
      "title": "更强数据约束：约1/16数据，10 组",
      "family": "scarcity",
      "why": "测试少量新域数据反复学习时，适应能否比调参普通训练保留更多旧知识。",
      "design": "数学419批数据：16遍加4步；普通AdamW四档LR，两档LR各配gains0/1/10。全部共享同一query顺序，评估40个固定checkpoint。",
      "decision": "完整结果已核验：没有候选超过调参普通训练；两条10倍反馈崩溃，全部不利结果保留；不自动扩网格。"
    },
    {
      "priority": "P0",
      "title": "固定内层步长，放大适应梯度修正",
      "family": null,
      "why": "区分修正强度不足，与裁剪或方向造成的学习／遗忘取舍。",
      "design": "64个已接受状态、每状态8个TRAIN三元组；固定inner3e-5，比较修正系数0、1、3、10、30、100，全部送入原生AdamW。",
      "decision": "准备 64/64 个状态，实际运行 0，执行完成 64，失败／中断 0。完整结果已独立核验：512组三元组、3072次原生更新；耗时2.749 GPU小时。gain10平均旧域损失差−5.08e-7、目标域代价+1.717e-5；尚无两项均改善的可分辨组均值。仅局部TRAIN诊断，不能替代完整验证曲线。停止大范围倍数网格；准备有界数学训练验证。"
    },
    {
      "priority": "P0",
      "title": "CPT setting × 超参数 · 96 组",
      "family": "settinghparam",
      "why": "扩大数据重复程度和内外层学习率；检验历史收益是否依赖数值后端，并挑战调过参的普通训练。",
      "design": "代码／数学 × one-pass／固定小数据四轮；64组内外层LR网格含own-zero，16组普通AdamW学习率，16组历史后端锚点。",
      "decision": "96组历史终点已核验；新增早期普通checkpoint后，代码和数学6个SGD候选均被普通AdamW半程超过。原自身zero收益保留，终点折中不再构成优势证据。32组对照已精确恢复并完成评分。原失败计入成本；保留全部56个适应对比和8个后端交互。不等计算与调参预算，单seed探索。"
    },
    {
      "priority": "P0",
      "title": "将适应接到外层 AdamW · 两种数据场景共 64 组",
      "family": "adamtransfer",
      "why": "检验已观察到的适应收益能否迁移到常规外层优化配方，而非只对特定SGD基线有效。",
      "design": "各32组：代码／数学 × 4个外层AdamW LR × 4个内层reset-Adam LR（含own-zero）。A一次遍历；B固定四分之一数据训四遍。",
      "decision": "64/64训练与全部评分已核验。48个适应效应均小于4e-5；13个两项点估计改善、5个两项边际文档区间有利，但无配置达到预定0.001筛选阈值。16个zero与ordinary文档得分相同。新训练125.783 GPU小时；112次评分1.528 GPU小时。关闭本轮自动加seed分支。"
    },
    {
      "priority": "P0",
      "title": "普通训练早期 checkpoint 能否解释代码域收益？",
      "family": null,
      "why": "仅比较训练终点不足以证明相同学习效果下遗忘更少；较早普通 checkpoint 可能已经更好。",
      "design": "复用16条已接受轨迹：代码／数学 × 四个SGD适应率与四个普通AdamW学习率。补评各自1/4、1/2、3/4进度，共48点，结合既有base和终点。",
      "decision": "全部112新评分加24复用终点已独立核验，640个配对bootstrap效应独立复现。普通AdamW1e-5半程checkpoint在两项指标上超过全部6个SGD候选终点；完整16条五点曲线公开。单seed、已观察holdout，不把连线当作额外模型。"
    },
    {
      "priority": "P1",
      "title": "AdamW 下的适应差异在哪一步变小？",
      "family": null,
      "why": "区分梯度差异原本很小、动量历史稀释、以及自适应变换的影响；避免把完整配方差异归因于单一因素。",
      "design": "64个真实模型和优化器状态：域×数据场景×4个外层LR×2个训练历史×早期/终点；每状态8组固定TRAIN文本，比较raw、动量和AdamW变换及局部损失。",
      "decision": "已准备 64/64 个状态；实际运行 0，执行完成 64，失败／中断 0。完整结果已独立核验。无新训练轨迹；保留全部状态与不利结果，不把局部诊断当作持久遗忘的因果证明，也不外推Muon。"
    },
    {
      "priority": "P1",
      "title": "更新方向还是更新长度？32个 checkpoint 的局部探针",
      "family": null,
      "why": "检验已观察到的CPT收益是否只是有效步长变小。",
      "design": "32个已保存checkpoint，各8组训练support/query/retention文本；比较零内层更新、适应后的更新及匹配零内层更新范数的方向。",
      "decision": "结果与图已独立核验。固定小数据SGD0.15的数学域，匹配范数后仍保留87–99%的平均局部收益；代码域没有相同解释。局部训练loss不是持久遗忘或泛化的因果证明。"
    },
    {
      "priority": "P0",
      "title": "历史配方阶梯 · 16 组 + 第二 seed 8 个锚点",
      "family": "recipebridge",
      "why": "定位历史配方的自身适应收益在哪个改动后改变；不把跨实验差距直接归因于算法。",
      "design": "H0历史参考 → 数值后端 → 内层LR日程 → 重复support → 固定support → clip → outerLR → warmup；每格配own-zero。",
      "decision": "按用户要求合并到B：A已交给Kaiyue，第二seed从保留断点续跑；原训练步数、顺序与累计预算不变。4组GPU数值与重放检查全部通过；保留两条原预算不足的短测失败，严格后端新上限6小时。第二seed预先固定H0/H1/H4/H7各配own-zero，检查后端、内层和外层三个变化区间是否稳定；细分阶梯仍单seed。固定预训练模型，seed主要改变训练数据顺序。实际启动状态见下方运行表；第一阶段尚未覆盖batch、query位置、数据重复策略及outerAdamW。"
    },
    {
      "priority": "P0",
      "title": "Normalized 0.1× · 4 组",
      "family": "smallradius",
      "why": "局部 probe 显示减小半径能减少临时旧域损害；检验这是否改善最终训练。",
      "design": "代码／数学 × 两个 LR；沿用同一 trainer 与已核验的 zero、1×、ordinary 对照，只将 normalized 半径降至 0.1×。",
      "decision": "C50 已核验：4/4 比 1× 两项区间均改善；相对 own-zero 只有2/4两项区间改善，最大终点遗忘改善0.00008857 NLL。预定学习匹配仅2/4通过；math较大LR的匹配遗忘反而差0.002040。单seed、已观察holdout；不扩seed或更小半径，转向历史配方阶梯。"
    },
    {
      "priority": "P0",
      "title": "同配比 replay · 16 组",
      "family": "matchedreplay",
      "why": "检验在相同保留数据下，T³ 是否还有独立于普通训练和零适应的收益。",
      "design": "代码／数学 × 两个 LR × ordinary mixed / split-zero / reset-Adam 1× / 10×；每步 4 条新域 + 4 条 FineWeb 训练序列。",
      "decision": "C49 已核验：8/8 个 own-zero 终点比较有微小保留收益，最大 0.00005620 NLL，远小于 0.01。4/8 个目标域区间略差；没有两项区间都改善的配置。全部通过预定 ±0.005 学习等价带，但这不等于学得更多。单 seed、已观察 holdout；暂不扩 seed。相对旧实验目标域曝光减半，跨批差距不能归因于 replay。"
    },
    {
      "priority": "P0",
      "title": "局部方向与旧领域损害 · 10 条件",
      "family": null,
      "why": "区分局部方向冲突、有限步长过冲与 FP32 实际位移；临时损失变化不能代替永久遗忘。",
      "design": "复用 base／中段／终点状态；两种方向、5 个半径；固定 support/query/FineWeb TRAIN 梯度及实际损失变化。",
      "decision": "C48 已独立核验：10/10 完成。normalized 1× 的 support loss 80/80 下降、FineWeb 训练 loss 80/80 上升；其中 41 个线性预测有利。有限步长效应值得排查；临时扰动不等于永久遗忘。"
    },
    {
      "priority": "P0",
      "title": "1.7B 多轮 CPT · 6 组",
      "family": "repeated17",
      "why": "检验 one-pass 的不利现象在固定数据多轮训练中是否保持。",
      "design": "数学，四轮，2 个 LR × ordinary / split-zero / normalized T³；13,416 updates。",
      "decision": "六组完整外部结果已核验：T³ 相对 own-zero 在两个 LR、全部八个固定点均学得更少、忘得更多。终点数学／FineWeb ΔNLL：1e-6 +0.022602／+0.033387；3e-6 +0.015351／+0.018636。四个终点文档区间均高于零；单 seed，已观察过的 holdout。B 已释放租约。固定终点是主比较；development-best 与 epoch-best 分开报告。"
    },
    {
      "priority": "P1",
      "title": "优化器 × 外层目标 × 适应 · 32 组",
      "family": "factorial",
      "why": "旧 CPT 的大部分遗忘改善在去掉适应后仍存在。需要识别 T³ 自身的增量。",
      "design": "135M，代码／数学，AdamW／SGD，各 2 个 LR；query-only／retained-mean；Adam 两步适应／配对 zero。",
      "decision": "完整外部结果已核验；适应增量小且依赖域。保留此前冻结的0个续跑候选，下一步区分方向与位移幅度。"
    },
    {
      "priority": "P1",
      "title": "普通训练 + 一步适应 · 24 组",
      "family": "companions",
      "why": "用调过 LR 的普通训练挑战结果，并隔离一步与两步适应的差别。",
      "design": "8 组 full-batch ordinary；16 组 Adam 一步适应。模型、数据顺序、外层参数与 32 组主网格一致。",
      "decision": "一步／两步比较用于 generalization；计算量不同，不据此宣称 compute efficiency。"
    },
    {
      "priority": "P1",
      "title": "135M 评分精度检查 · 已验收",
      "family": null,
      "why": "排除评分批大小或填充造成的小幅假收益，为完整学习—遗忘比较做准备。",
      "design": "相同的 8 段 development 文本，比较单独评分和分批评分；使用 1 张卡。",
      "decision": "通过后才能做完整外部评分；不一致时保留失败，先解决测量问题。"
    },
    {
      "priority": "P1",
      "title": "56 组学习—遗忘评估 · 56 组完整外部结果已核验",
      "family": null,
      "why": "检验临时适应能否带来独立收益，并比较目标域学习相当时的遗忘。",
      "design": "所有配置的 8 个固定 checkpoint，加上各自 development-best；配对 zero、ordinary 和一步／两步对照。",
      "decision": "先冻结 development 候选再读外部评分。报告配对文档区间；这批已看过的外部数据属于探索性证据，不作为新的独立确认。"
    },
    {
      "priority": "P0",
      "title": "等位移方向 × 幅度 · 32 组",
      "family": "directiondose",
      "why": "当前一步／两步 Adam 适应同时改变位移大小和计算量，不能据此判断 normalized gradient 是否特殊。",
      "design": "135M 代码／数学 × AdamW 两个 LR × normalized-gradient／等位移重缩放 reset-Adam × 0／1／10／100 倍位移；24 正半径＋8 own-zero，复用四条同设置 ordinary。",
      "decision": "C47 已验收：32 组训练与全部外部评分完成。normalized-gradient 的 12 个终点对比均学得更少、忘得更多，且随半径增大恶化。代码 reset-Adam 四个低剂量对比通过同等学习下的微小保留收益，最大仅 0.000191 NLL，未达到 0.01。后续用局部方向 probe（含 FineWeb 训练梯度）和同配比 replay 对照分开检验机制与训练收益。同幅度响应相近支持幅度解释；只改变损失不算成功。保留全部失败，0.01 遗忘改善须同时满足冻结的目标学习匹配规则。"
    },
    {
      "priority": "P2",
      "title": "依据完整结果决定后续",
      "family": null,
      "why": "先排除混淆，再扩大实验；避免为了找正结果无限调参。",
      "design": "候选方向：适应半径、同配比 replay、one-pass、更大 base model；每条先冻结对照与预算。",
      "decision": "有清晰增量后再做 paired multiseed。无增量则收窄论文主张。"
    }
  ],
  "accepted": {
    "muon": [
      {
        "label": "Infinite-MG · Q",
        "gain": 0.002189874649
      },
      {
        "label": "Infinite-MG · SlowRun 系数 S",
        "gain": 0.004793405533
      },
      {
        "label": "SlowRun · 自身 setting",
        "gain": 0.006537788793
      }
    ],
    "muon_note": "收益 = split-zero NLL − T³ NLL（越高越好）。Q/S 是 300M 同设置系数比较；SlowRun 是不同的 1.44B 设置。单 seed；S 未达到预先设定的 0.005 筛选阈值。",
    "cpt": [
      {
        "lr": 1e-06,
        "target": 0.022613,
        "target_ci": [
          0.020693,
          0.024811
        ],
        "forgetting": 0.036206,
        "forgetting_ci": [
          0.033831,
          0.0387
        ]
      },
      {
        "lr": 3e-06,
        "target": 0.007045,
        "target_ci": [
          0.005514,
          0.008559
        ],
        "forgetting": 0.009282,
        "forgetting_ci": [
          0.008817,
          0.009771
        ]
      }
    ],
    "cpt_note": "已接受的 1.7B 数学 one-pass：T³ − own-zero，正值表示更差。两个学习率、全部 8 个固定点均不利。区间是配对文档 bootstrap，不代表训练 seed 的不确定性。",
    "evidence": [
      "C42 · Muon coefficient comparison",
      "C43 · common-recipe one-pass CPT",
      "C44 · Muon secondary evaluation",
      "C46 · repeated-data 1.7B CPT: adverse own-zero effects",
      "C47 · direction and dose CPT: all outcomes retained",
      "C49 · matched replay CPT: small retention increments",
      "C50 · normalized0.1 CPT: small conditional increments",
      "CPT paradigm · all52 trainings and240 points; no practical candidate"
    ]
  }
}