Validate repeated learned RMSNorm functions with CPU evidence

2026-11-09 14:14:59 +08:00
parent 573cbedd98
commit 6d1acbc26a
108 changed files with 6526 additions and 33 deletions
@@ -3,3 +3,4 @@ results/** -text
 # Generated CSV uses RFC 4180 line endings; Matplotlib SVG paths contain spaces.
 results/**/*.csv -whitespace
 results/**/*.svg -whitespace
+results/**/*.log -whitespace
@@ -3,3 +3,8 @@ __pycache__/
 weights/
 *.partial
 *.log
+
+# Large reproducible functional activations remain on the experiment server.
+results/cpu-qwen35-2b/baseline-activations.npz
+results/cpu-qwen35-2b/neuron-reuse-activations.npz
+results/cpu-qwen35-2b/norm-repetition-input-profiles.npz
@@ -1,11 +1,15 @@
 # Model parameter similarity experiments

-Bounded, inference-free analysis of official unquantized model weights. The first experiment
-is **Qwen3.5-2B**. Model weights are downloaded and analyzed on the authorized remote host,
-then deleted after archived results pass validation for the completed static pilot.
+Research on repeated learned functions in **Qwen3.5-2B**: a completed static weight baseline
+and a training-free CPU functional pilot. The CPU study found similar existing RMSNorm
+functions at layer indices 12/13, with a uniform analytic error bound and heldout substitution checks.
+Cost is not a success condition. The completed static pilot was inference-free and its
+weights were deleted after archival; the functional pilot executes streaming FP32 text forwards.
 The current CPU functional checkpoint is retained until the user explicitly requests deletion.
 No model weights are committed here.

+- [CPU learned-function repetition findings](docs/CPU_FUNCTIONAL_FINDINGS.md)
+- [Current learned-function repetition criterion](docs/LEARNED_FUNCTION_REPETITION.md)
 - [CPU-first feasibility and validation](docs/CPU_FIRST_VALIDATION.md)
 - [Training-free functional reuse plan (proposed; not executed)](docs/TRAINING_FREE_FUNCTIONAL_PLAN.md)
 - [Primary-source literature and applicability](docs/FUNCTIONAL_REUSE_LITERATURE.md)
@@ -21,9 +25,10 @@ No model weights are committed here.
 - `pilot/run.py`: one-model supervisor with bounded download wait and analysis timeouts.
 - `tests/test_pilot.py`: synthetic numerical and 3D correctness tests.

-The primary result is **verified net byte savings versus direct relative weight error**.
-This does not demonstrate preserved task capability or faster inference. Different block
-scales are alternative partitions and their savings must not be summed.
+The static baseline reports **verified net byte savings versus direct relative weight error**.
+The functional study separately reports same-input error, an all-input RMSNorm function bound
+and tiny heldout NLL changes; these are not broad task-accuracy guarantees. Different static
+block scales are alternative partitions and their savings must not be summed.

 Dependencies are pinned in `requirements.txt`; install only prebuilt wheels on the remote host.
 Full resolved versions and run provenance accompany each result. See the SOP for commands.
@@ -67,3 +67,9 @@ CPU 上“找到输出接近的候选”能支持继续投入 GPU；
 最多选择一个候选，必须分别验证两个替换方向；完整模块失败不会被小模块通过所掩盖。
 归一化共享最多节省少量字节，不预设能显著减少计算。
 采集真实隐藏输入必须正确执行前置线性注意力；它不进入首轮复用候选，但其递推计算纳入官方参考对照。
+
+## 用户最新目标修订
+
+不再以成本/加速作为成功条件；主问题是现有学习部分的功能重复。
+新增一次固定四层的 MLP 神经元函数搜索、独立留出和双向替换，
+见 [功能重复判据](LEARNED_FUNCTION_REPETITION.md)。解析增益合并单独归类，不冒充学习部分重复。
@@ -0,0 +1,86 @@
+# CPU 先导结论：存在已训练归一化模块的相似功能重复
+
+本轮主目标按用户最新要求为功能重复的存在性，不以成本/加速为成功条件。
+全部计算在 2 核、约 1.7 GiB RAM 的 CPU 服务器完成，无训练、缩放拟合或旋转搜索。
+层编号均为从 0 开始的索引。
+
+## 一个具体正例
+
+固定官方 Qwen3.5-2B checkpoint 的两个独立已训练模块：
+
+- `model.language_model.layers.12.input_layernorm`
+- `model.language_model.layers.13.input_layernorm`
+
+二者均为 2048→2048 的 RMSNorm，eps=1e-6，使用训练后的有效增益 gamma=1+weight。
+增益标准差分别约 0.1901、0.1685，最小值约 0.5547、0.5703；不是零函数或恒定增益。
+
+| 证据 | 结果 |
+|---|---|
+| 全输入域数学界（相对于层12输出） | 输出相对 L2 误差≤10.1852% |
+| 反方向全输入域数学界（相对于层13输出） | 输出相对 L2 误差≤11.3402% |
+| 4 条留出文本、两个来源层输入的最坏双向误差 | 4.5285% |
+| 相同留出比较中的最低余弦 | 0.999487 |
+| 层12使用层13，留出平均 token NLL 增量 | +0.0000325651 nat/token |
+| 层13使用层12，留出平均 token NLL 增量 | −0.0003639730 nat/token |
+
+两方向逐文本 NLL 增量 P95 分别约 0.00098464、0.00070972，均通过原定小样本质量门槛。
+所有数值来自保存的逐文本、逐 token 原始数据。留出为 4 条不同的实验原创短文本，
+每条截取 32 tokens；校准另有 4 条。它们不是广泛任务能力评测。
+
+## 为什么不只是“权重看起来接近”
+
+对相同 eps 和维数，f_a(x)=diag(a)x/sqrt(mean(x²)+eps)。任意非零实数输入满足：
+
+||f_a(x)-f_b(x)|| / ||f_a(x)|| ≤ max_j |a_j-b_j|/|a_j|。
+
+共同归一化分母消去；坐标基向量可达到最大值，因而该界是精确的最坏情况。
+反向分母换成 |b_j|。本轮先穷举 24 层同角色 input/post-attention norm 的 552 对，
+仅按最小双向最坏界选定一对，未使用任何输入样本作选择。≤20% 界有 18 对，≤10% 无配对；
+本轮只验证最优的一对，不根据留出结果换选。
+
+这个确定性界直接给出两个已训练函数整体接近的证据。真实输入上的误差更小，
+与只关心目标输入分布的目标一致。浮点实测与实数数学界分别报告。
+重新采集输入时，未修改的完整前向逐 bit 复现了最初保存的基线输出。
+输入探测分别使用两个来源层，对相同输入同时应用两套增益，避免比较不同输入的输出。
+从已加增益的激活还原共同归一化输入时有额外 FP32 舍入，未将其称为 bitwise 等价。
+
+结论限定为两个已有 RMSNorm 模块实现相似映射；不把共同包含 RMS 操作本身当作证据。
+二者学习增益不同且非恒定，输出非零。完整 attention/MLP 主体的重复仍未得到正例。
+
+## 已完成的阴性搜索
+
+1. 原定 3 对完整 attention、2 对完整 MLP、6 对层内 Q/K norm 均未通过预设门槛。
+   attention 检索分布 JS 范围约 0.145～0.318，也未通过 0.01 的门槛；
+   未进行头排列、连续坐标变换或训练对齐，不能外推为不存在其他可复用表示。
+2. 在层 5、6、20、21 各 6144 个 MLP scalar 子函数中，排除低活跃和近常量单元后，
+   共精确筛查 7,450,139 个唯一同层配对。要求双向相对误差≤20%、余弦≥0.98、
+   Pearson≥0.95，无通过者。没有针对留出集不断更换候选或放宽门槛。
+3. 这些结果只覆盖声明的模块、输入和阈值，不否定其他粒度/角色的功能重复。
+
+## 单独归类的解析恒等式
+
+Q/K 未参与 RoPE 的 192 个维度，其增益只以乘积影响点积，可合并到任一侧。
+96 组真实输入检查最大分数差 3.8147e-6、最大概率差 8.0466e-7；固定第3层两个方向
+留出 NLL 变化接近 FP32 舍入。详见 [推导](QK_GAIN_FACTORIZATION.md)。
+这是重参数化等价，不作为“两个已有学习部分功能重复”的正例。先前性能测试原始数据
+保留用于溯源，成本不属于当前结论的成功条件。
+
+## 正确性、资源与保留规则
+
+- 固定模型 revision：15852e8c16360a2fea060d615a32b45270f8a8fc，原始权重 SHA256 校验通过。
+- 流式读取原始 BF16，逐块转为 FP32，不同时载入全模型。
+- 与固定 Transformers 官方定义比较的 22 项检查通过，最大差约 5.4e-7。
+- 保留早期参考适配器错误、临时分区空间不足及两次过于保守的换页停止记录。
+  只归还自身临时堆页，未改变系统 swap 设置；后续有界前向完成。
+- 计算预算跨重启累计，初始保守计入 180 秒先前检查预留；具体时长及峰值见归档日志。
+- 当前权重按用户最新指令持续保留，只在用户明确要求删除时清理。
+
+原始表、样本/token IDs、激活、NLL、证明界与图表见 `results/cpu-qwen35-2b/`。
+后续 GPU 阶段可增加长度、语言和任务来验证正例的稳健性；本轮不自动启动新模型或扩展搜索。
+
+## Git 交付与保留缓存
+
+全部图表源 CSV/JSON、逐 token NLL、证明界和日志已交付。三份较大的中间激活缓存
+保留在原服务器及本地完整归档，路径、大小、SHA256 见结果目录的 external-activation-cache.json。
+其完整原实验校验表为 checksums-full-server.json；Git交付包校验表为 checksums-functional.json。
+没有删除模型或缓存；这些缓存可由固定代码和样本重新生成，也可从服务器按哈希取回。
@@ -0,0 +1,58 @@
+# 当前目标：已有学习部分是否重复实现相似函数
+
+用户于 2026-11-09 明确：不以成本或加速作为成功条件，只关心相似功能重复是否存在。
+权重继续保留，只有用户明确要求删除才清理；不训练、不拟合旋转或缩放。
+
+## 什么可以作为正例
+
+两个不同的已训练部分，在同一真实输入上产生接近、且并非恒定/接近零的输出；
+独立留出输入仍满足预先固定的门槛。实际双向替换只作补充验证。
+不能仅因替换一小部分后整模型 NLL 几乎不变，就认定它们实现相同函数。
+
+Q/K 增益的解析合并证明同一计算的重参数化自由度，不等于两个现成学习部分功能重复。
+前一轮 11 对完整/归一化模块未通过门槛，也不能否定更小粒度的重复。
+不将两个模块都包含 SiLU、矩阵乘法等共同运算本身作为重复功能证据。
+
+## 新增一次有界搜索：MLP 学习神经元
+
+在已保存的第 5、6、20、21 层真实 MLP 入口输入上，研究
+
+f_(l,j)(h) = SiLU(w_gate_(l,j)^T h) × (w_up_(l,j)^T h)。
+
+每层 6,144 个神经元具有不同学习参数。比较同一层内两个 scalar 子函数，
+不是比较来自不同输入的两个观测值，也不是把整层或整 tensor 称作重复。
+下游 down-projection 的列保持各自原样；替换发生在两个投影和 SiLU 之后的激活值处。
+
+1. 4 条校准文本、4 条留出文本，每条 32 tokens，中文/英文各半，沿用先前冻结样本。
+2. 校准阶段只保留 RMS 至少为该层神经元中位数、std/RMS≥0.25、能量有效 token 数≥16
+   的非平凡单元。能量有效样本数定义为 (sum f²)² / sum f⁴，不是独立统计样本量。
+3. 对这些单元做分块穷举；实际配对数必须记录。不用随机 hidden 向量作主要证据。
+4. 预先固定门槛：双向直接相对 L2≤20%、余弦≥0.98、Pearson≥0.95；同时输出 5/10/20%
+   三档校准配对数量。没有拟合线性缩放、偏置或 R。
+5. 全部层中仅按校准的最小最坏方向误差选择 1 对，冻结选择；无通过者停止。
+6. 留出集要求同样的汇总函数门槛及活跃度条件，逐文本双向误差≤35%。不根据留出失败换候选。
+7. 只有函数验证通过，才做两个方向的独立网络替换；留出平均 NLL 增量≤0.01 nat/token，
+   逐文本增量 P95≤0.05。它不是主要的功能重复判据，防止因单元贡献很小而得到假正例。
+
+本阶段使用先前两小时累计预算的剩余额度，复用现有隐藏输入；不增加模型、样本规模或成本测试。
+神经元层面的经验正例不证明完整层可以互换、不证明全输入空间等价，也不能推断独立任务能力。
+留出只有 4 条文本，结论必须限定到此短文本先导分布；后续扩展文本才能检验稳健性。
+
+## 同角色学习 RMSNorm：确定性函数误差界
+
+静态试验已经发现部分语言层 post-attention norm 的参数接近。新增一次独立检验：
+比较 24 层各自的 input/post-attention RMSNorm（同角色内共 552 对），
+仅按训练后增益的全输入最坏误差界选 1 对，完全不依赖校准或留出输入。
+增益需非零且不是常量向量（std≥0.001），不把没有学习参数的共同归一化运算当作正例。
+
+相同 eps、相同形状下，f_a(x)=diag(a)x/sqrt(mean(x²)+eps)。对任意非零 x，
+
+||f_a(x)-f_b(x)|| / ||f_a(x)|| ≤ max_j |a_j-b_j|/|a_j|。
+
+反方向分母换为 |b_j|。该上界由相应坐标基向量达到，是实数算术下的精确最坏界。
+因此可先证明两个现成学习模块的函数整体接近，而不只比较原始参数 w；实际增益为 1+w。
+选择门槛：最坏双向界≤20%；同时记录≤5%、≤10%、≤20% 配对数量。只选最优界的一对。
+随后重新采集其实际输入，要求留出逐文本直接双向输出误差≤5%，再测两个方向网络替换。
+函数接近与替换后网络 NLL 分开报告；后者沿用已有 0.01/0.05 门槛，不决定函数存在性本身。
+选定后不根据留出结果换配对；不搜索视觉模块、不增加模型、不以成本为条件。
+归一化层正例不能推广为 attention 或 MLP 主体存在同样重复。
@@ -0,0 +1,46 @@
+# Q/K 未旋转维度的确定性增益合并（已数值验证，归为重参数化）
+
+这是从已核对架构直接推导的恒等式，不是看留出指标后扩大近邻搜索。
+原定 11 对候选保留原协议；本项是独立的解析对照，不训练、不求 R，也不选择层。
+预先固定完整网络干预层为第 3 层，分别验证两个移动方向；不根据数据换层。
+
+## 恒等式与前提
+
+以完成 RMS 单位化、尚未乘可学习增益的 q/k 为 u/v，Q/K 增益向量为 a/b。
+固定 checkpoint 的 norm 参数实际使用 a=1+w_Q，b=1+w_K。
+模型 head_dim=256，partial_rotary_factor=0.25：前 64 维旋转，后 192 维不旋转。
+逐头共享的增益向量与 GQA 结构已由官方代码确认。
+
+对于后 192 维，任意 query/key 位置 p/s：
+
+sum_j (a_j u_pj)(b_j v_sj) = sum_j u_pj ((a_j b_j) v_sj)
+                         = sum_j ((a_j b_j) u_pj) v_sj。
+
+前 64 维保留原增益和位置旋转。因而完整 QK 点积在实数算术中相等，softmax、V、
+输出门控和 O 不变时，完整 attention 分支也是同一个函数。这个结论不依赖某一输入分布。
+浮点乘法次序改变，不能承诺逐 bit 相等；需报告实际误差并验证留出 NLL。
+不能把这一规则用于旋转维度，或未经核对的逐头独立增益架构。
+
+## 验证与成本边界
+
+1. 在已有真实 q/k 输入上，检查 6 个 full-attention 层的原始/两个合并方向分数、概率及输出。
+2. 固定第 3 层进行两个方向的独立网络替换，不叠加，使用原留出文本和质量阈值。
+3. 记录预先缓存增益后、相同 CPU 环境下原版与合并版局部归一化耗时；不外推 GPU 性能。
+4. 双精度数学推导和 FP32 实测分开。没有消除 Q/K 投影或 RMS 方差计算。
+
+将 Q 的未旋转增益移入 K 时，每 token、每层少做 8×192=1,536 次逐元素增益乘法；
+反方向少做 2×192=384 次。它们只是归一化里的小部分操作，不是整个模型的百分比加速。
+NumPy 切片/拼接可能抵消这点收益，必须报告测得的局部时延。
+
+参数量从两个 256 维增益变为两个 64 维旋转增益及一个 192 维联合增益，即 512→320 个数。
+但原增益以 BF16 存储、联合乘积以 FP32 保留时，载荷仍是 1,024 字节，没有 checkpoint
+净压缩；元数据还可能增加。FP32 运行时缓存增益的理论载荷 2,048→1,280 字节，
+不等于已测得整个模型 RSS 降低。另行 BF16 量化乘积会增加误差，不属于本次恒等式验证。
+
+这是同一注意力计算内部的重参数化与冗余消除，不是两个现成 attention 层能互换的证明。
+若得到正例，应将它作为小粒度确定性优化机会；较大模块互换仍由原候选试验单独回答。
+
+## 完成状态
+
+96 组真实输入分数/概率检查及固定第3层双向网络检查均通过。
+该结果独立归类；主目标的已训练函数重复正例见 [CPU 功能结论](CPU_FUNCTIONAL_FINDINGS.md)。
@@ -128,3 +128,13 @@ On 2026-11-09 the user explicitly changed retention: keep the CPU functional pil
 checkpoint until the user explicitly requests deletion. Completion, timeout, failure
 and archive validation do not authorize cleanup. The earlier pilot cleanup remains
 a historical action; do not apply its automatic cleanup step to the current run.
+
+## CPU functional follow-up outcome
+
+The executed CPU study found a learned RMSNorm function-repetition example at text
+layer indices 12/13. Use same-input function error and a uniform analytic bound as
+primary evidence, with separate heldout network substitution checks. Costs are not
+a success condition under the latest user instruction. See the
+[executed findings](CPU_FUNCTIONAL_FINDINGS.md) and
+[function-repetition criterion](LEARNED_FUNCTION_REPETITION.md).
+Keep whole-module, neuron, normalization and algebraic-reparameterization results distinct.
@@ -56,7 +56,8 @@ search extra candidates, mutate Git or delete weights. After reference checks, i
 8-token real text capacity check, then the bounded functional pilot. Reference, preflight
 and functional compute together are capped at two hours with a reporting reserve;
 download wait is recorded separately. Projection memory is checked, RSS soft stop is
-512 MiB, available-system-memory floor 256 MiB; sustained host swap IO also stops the run.
+512 MiB, available-system-memory floor 256 MiB; sustained host swap IO combined with low available memory or >128 MiB process swap also stops the run.
+A persistent ledger counts computation across restarts; early checks/preflight receive a conservative 180-second reserve.

 `preflight.py` is a capacity smoke check, not a semantic oracle. `run.py` records exact
 sample/token IDs, fixed configuration, source hashes, activations, per-text/per-head metrics,
@@ -66,3 +67,25 @@ saved data to produce PNG/PDF/SVG figures without reopening weights.
 No generation throughput is measured in this short-text pilot. Any parameter-tying storage
 benefit must be accounted for separately, including metadata. Neither same-input similarity
 nor favorable tiny heldout NLL demonstrates broad mutual interchangeability or GPU speedup.
+
+## Current primary result and completed extensions
+
+The latest user goal is learned-function repetition, independent of costs. Existing
+input RMSNorm modules at layer indices 12/13 have a proven worst two-direction real-arithmetic
+error bound of 11.3402%, worst observed heldout relative error 4.5285%, minimum cosine 0.999487,
+and small bidirectional heldout NLL changes. See [findings](../docs/CPU_FUNCTIONAL_FINDINGS.md).
+
+- `neuron_reuse.py`: fixed four-layer scalar MLP function search, nontrivial activity
+  gates, calibration-only selection and independent heldout/substitution logic; no pair passed.
+- `norm_repetition.py`: same-role learned RMSNorm bound across 552 pairs, one deterministic
+  selection independent of any input samples, real probes and two full-network substitutions.
+- `gain_factorization.py`: architecture-derived identity on non-RoPE coordinates; a separate
+  reparameterization result, not a learned-function repetition example.
+- `benchmark_factorization.py`: historical one-attempt CPU allocation optimization and
+  bitwise equivalence check. Performance is no longer a success condition.
+- `storage.py`: explicit component layout accounting; the original candidate run selected
+  no pair. This does not affect the primary function-repetition conclusion.
+
+Do not expand the final positive claim to full attention or MLP bodies. Retain the
+fixed checkpoint until the user explicitly requests deletion. Full tests and audit are
+run remotely; no local build or model execution is required to inspect the archived results.
@@ -0,0 +1,86 @@
+"""Audit saved functional evidence and archive only outputs, never checkpoint weights."""
+import argparse
+import csv
+import hashlib
+import json
+from pathlib import Path
+import platform
+import subprocess
+import time
+import numpy as np
+from .model import StreamingLM
+from .norm_repetition import uniform_bounds
+from .run import write_json
+
+EXCLUDE={'tokenizer.json','reference.py','checksums-functional.json','audit-validation.json','archive-files.json'}
+
+
+def archive_files(root):
+    files=[p for p in root.iterdir() if p.is_file() and p.name not in EXCLUDE and not p.name.endswith(('.partial','.safetensors'))]
+    files += [p for p in (root/'figures').glob('*') if p.is_file()]
+    return sorted(files)
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);a=p.parse_args();root=a.root
+    manifest=json.loads((root/'manifest.json').read_text())
+    assert manifest['revision']=='15852e8c16360a2fea060d615a32b45270f8a8fc'
+    for f in manifest['files']:
+        if f['name'].endswith('.safetensors'):
+            path=root/'weights'/f['name']
+            assert path.is_file() and path.stat().st_size==f['bytes']
+            assert f['sha256']==f['upstream_sha256']
+    retention=json.loads((root/'retention-policy.json').read_text());assert retention['automatic_cleanup'] is False
+    reference=json.loads((root/'reference-check.json').read_text())
+    assert reference['passed'] and len(reference['tests'])==22
+    assert reference['implementation_sha256']==hashlib.sha256(Path('functional/model.py').read_bytes()).hexdigest()
+    tests=(root/'final-tests.txt').read_text();assert 'Ran 12 tests' in tests and '\nOK\n' in tests
+    result=json.loads((root/'norm-repetition-summary.json').read_text());assert result['state']=='completed'
+    assert result['learned_function_repetition_positive'] and result['bidirectional_network_quality_pass']
+    cfg=json.loads((root/'model_config.json').read_text());lm=StreamingLM(root/'weights',cfg,seconds=60)
+    c=result['selected'];ga=1+lm.small(f'layers.{c["a"]}.{c["role"]}.weight');gb=1+lm.small(f'layers.{c["b"]}.{c["role"]}.weight')
+    bounds=uniform_bounds(ga,gb)
+    np.testing.assert_allclose(bounds,[c['a_to_b_uniform_bound'],c['b_to_a_uniform_bound']],atol=1e-14,rtol=1e-14)
+    with (root/'norm-repetition-uniform-bounds.csv').open() as f:assert len(list(csv.DictReader(f)))==552
+    baseline=np.load(root/'baseline-heldout-token-nll.npy');assert baseline.shape==(4,31)
+    for rec in result['interventions']:
+        nll=np.load(root/f'norm-repetition-{rec["target"]}-uses-{rec["donor"]}-token-nll.npy')
+        delta=nll.mean(1)-baseline.mean(1)
+        np.testing.assert_allclose(delta,rec['sequence_nll_deltas'],atol=1e-13,rtol=1e-13)
+        assert abs(delta.mean()-rec['mean_nll_delta'])<1e-13
+    with (root/'norm-repetition-function-metrics.csv').open() as f:rows=list(csv.DictReader(f))
+    held=[r for r in rows if r['split']=='heldout'];assert len(held)==8
+    worst=max(max(float(r['relative_l2']),float(r['reverse_relative_l2'])) for r in held)
+    assert abs(worst-result['worst_heldout_relative_l2'])<1e-14
+    neuron=json.loads((root/'neuron-reuse-search-summary.json').read_text());assert sum(x['eligible_unique_pairs_checked'] for x in neuron)==7450139
+    ledger=json.loads((root/'compute-budget-ledger.json').read_text());assert ledger['spent_seconds']+600<7200
+    environment={'python':platform.python_version(),'platform':platform.platform(),'numpy':np.__version__,
+                 'packages':subprocess.run([str(Path(__import__('sys').executable)),'-m','pip','freeze'],capture_output=True,text=True,check=True).stdout.splitlines(),
+                 'weights_retained':True,'no_automatic_cleanup':True,'ledger_seconds_including_initial_reserve':ledger['spent_seconds'],
+                 'final_tests_report_audit_additional_budget_upper_reserve_seconds':600,
+                 'historical_error_logs':'Early reference adapter errors and preflight pressure stops; latest summaries/tests are authoritative.'}
+    write_json(root/'functional-environment.json',environment)
+    source_paths=[*Path('functional').glob('*.py'),Path('functional/reference.json'),Path('pilot/tensors.py'),Path('tests/test_functional.py')]
+    write_json(root/'final-source-hashes.json',{str(p):hashlib.sha256(p.read_bytes()).hexdigest() for p in sorted(source_paths)})
+    files=archive_files(root)
+    # Every numeric CSV value must be finite. Empty diagnostic fields stay unknown.
+    for path in files:
+        if path.suffix=='.csv':
+            with path.open() as f:
+                for row in csv.DictReader(f):
+                    for value in row.values():
+                        if value in (None,''):continue
+                        try:number=float(value)
+                        except ValueError:continue
+                        assert np.isfinite(number),(path,value)
+    checksums={str(p.relative_to(root)):hashlib.sha256(p.read_bytes()).hexdigest() for p in files}
+    write_json(root/'checksums-functional.json',checksums)
+    selected=[*checksums,'checksums-functional.json','audit-validation.json','archive-files.json']
+    write_json(root/'archive-files.json',selected)
+    write_json(root/'audit-validation.json',{'passed':True,'validated_unix':time.time(),'checksummed_files':len(checksums),
+               'proof_recomputed_from_retained_weights':True,'heldout_nll_recomputed_from_saved_arrays':True,
+               'weights_present_and_retained':True,'archive_excludes_weights_and_tokenizer':True})
+    print(json.dumps({'passed':True,'checksummed_files':len(checksums),'weights_retained':True}))
+
+
+if __name__=='__main__':main()
@@ -0,0 +1,83 @@
+"""One allocation-reduction attempt, with a similarly optimized baseline.
+
+Does not change weights or run the network again. Checks bitwise equality to the
+network-tested factorization on every captured real Q/K input before timing.
+"""
+import argparse
+import json
+from pathlib import Path
+import resource
+import time
+import numpy as np
+from .model import StreamingLM
+from .gain_factorization import consolidate,normalized,LAYERS
+from .run import save_csv,write_json
+
+
+def baseline_inplace(q,k,a,b):
+    q=normalized(q);q*=a
+    k=normalized(k);k*=b
+    return q,k
+
+
+def factored_inplace(q,k,a,b,r,combined,move='q_to_k'):
+    q,k=normalized(q),normalized(k)
+    if move=='q_to_k':
+        q[...,:r]*=a[:r];k*=combined
+    else:
+        q*=combined;k[...,:r]*=b[:r]
+    return q,k
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',required=True,type=Path);a=p.parse_args();root=a.root
+    ledger_path=root/'compute-budget-ledger.json';ledger=json.loads(ledger_path.read_text());remaining=int(7200-ledger['spent_seconds']-20)
+    if remaining<60:raise TimeoutError('Cumulative budget insufficient')
+    start=time.monotonic();result={'state':'running'}
+    try:
+        cfg=json.loads((root/'model_config.json').read_text());lm=StreamingLM(root/'weights',cfg,seconds=min(remaining,300))
+        r=64;data=np.load(root/'baseline-activations.npz');checks=[]
+        for layer in LAYERS:
+            lm.guard();pfx=f'layers.{layer}.self_attn.'
+            ag,bg=1+lm.small(pfx+'q_norm.weight'),1+lm.small(pfx+'k_norm.weight')
+            q,k=data[f'qraw_{layer}'],data[f'kraw_{layer}']
+            for move in ('q_to_k','k_to_q'):
+                combined=np.concatenate([(bg if move=='q_to_k' else ag)[:r],ag[r:]*bg[r:]])
+                expected=consolidate(q,k,ag,bg,r,move)
+                actual=factored_inplace(q,k,ag,bg,r,combined,move)
+                for x,y in zip(expected,actual):np.testing.assert_array_equal(x,y)
+                checks.append({'layer':layer,'direction':move,'query_elements':int(q.size),'key_elements':int(k.size),'bitwise_equal':True})
+        q,k=data['qraw_3'][:1],data['kraw_3'][:1];pfx='layers.3.self_attn.'
+        ag,bg=1+lm.small(pfx+'q_norm.weight'),1+lm.small(pfx+'k_norm.weight')
+        combined=np.concatenate([bg[:r],ag[r:]*bg[r:]])
+        funcs={'baseline_legacy':lambda:(normalized(q)*ag,normalized(k)*bg),
+               'baseline_inplace':lambda:baseline_inplace(q,k,ag,bg),
+               'factored_inplace':lambda:factored_inplace(q,k,ag,bg,r,combined)}
+        for _ in range(3):
+            for fn in funcs.values():fn()
+        timing=[];names=list(funcs)
+        for repeat in range(30):
+            order=names[repeat%3:]+names[:repeat%3]
+            for name in order:
+                lm.guard();t=time.perf_counter()
+                for _ in range(50):funcs[name]()
+                timing.append({'repeat':repeat,'implementation':name,'seconds_per_call':(time.perf_counter()-t)/50,'calls_per_block':50})
+        save_csv(root/'gain-factorization-optimized-timing.csv',timing)
+        values={name:np.array([row['seconds_per_call'] for row in timing if row['implementation']==name]) for name in names}
+        ratio=values['baseline_inplace']/values['factored_inplace']
+        medians={name:float(np.median(v)) for name,v in values.items()}
+        result.update(state='completed',bitwise_checks=checks,medians_seconds=medians,
+                      paired_baseline_over_factored_median=float(np.median(ratio)),
+                      paired_ratio_p10=float(np.quantile(ratio,.1)),paired_ratio_p90=float(np.quantile(ratio,.9)),
+                      fraction_blocks_factored_faster=float(np.mean(ratio>1)),
+                      descriptive_local_gain=bool(np.median(ratio)>1.05 and np.mean(ratio>1)>=.8 and np.quantile(ratio,.1)>1),
+                      claim_limit='Hot-array CPU normalization microbenchmark against equally allocation-optimized baseline; no full-model speedup or independent statistical significance claim.')
+    except Exception as exc:
+        result.update(state='stopped',error_type=type(exc).__name__,error=str(exc));raise
+    finally:
+        elapsed=time.monotonic()-start;result.update(elapsed_seconds=elapsed,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
+        ledger=json.loads(ledger_path.read_text());ledger['spent_seconds']+=elapsed;ledger['calls'].append({'module':'functional.benchmark_factorization','seconds':elapsed});write_json(ledger_path,ledger)
+        write_json(root/'gain-factorization-optimized-summary.json',result)
+
+
+if __name__=='__main__':main()
@@ -0,0 +1,135 @@
+"""Architecture-derived Q/K gain consolidation on non-RoPE dimensions only.
+
+Fixed layer3 interventions; no fitting, layer selection or learned alignment.
+Run only after baseline/candidate pilot finishes, within the same cumulative budget.
+"""
+import argparse
+import csv
+import json
+from pathlib import Path
+import resource
+import time
+import numpy as np
+from .model import StreamingLM,norm,rope
+from .run import probability_metrics,write_json,save_csv
+
+LAYERS=[3,7,11,15,19,23]
+
+
+def normalized(x):
+    return x/np.sqrt(np.mean(x*x,axis=-1,keepdims=True)+np.float32(1e-6))
+
+
+def consolidate(q,k,a,b,r,move,joint=None):
+    q,k=normalized(q),normalized(k)
+    c=a[r:]*b[r:] if joint is None else joint
+    if move=='q_to_k':
+        q=np.concatenate([q[...,:r]*a[:r],q[...,r:]],axis=-1)
+        k=np.concatenate([k[...,:r]*b[:r],k[...,r:]*c],axis=-1)
+    else:
+        q=np.concatenate([q[...,:r]*a[:r],q[...,r:]*c],axis=-1)
+        k=np.concatenate([k[...,:r]*b[:r],k[...,r:]],axis=-1)
+    return q,k
+
+
+class FactoredLM(StreamingLM):
+    def configure(self,layer,move):
+        self.factor_layer=layer;self.move=move
+        p=f'layers.{layer}.self_attn.'
+        self.a=1+self.small(p+'q_norm.weight');self.b=1+self.small(p+'k_norm.weight')
+        self.r=int(self.config['head_dim']*self.config['rope_parameters']['partial_rotary_factor'])
+        self.joint=self.a[self.r:]*self.b[self.r:]
+
+    def normalization(self,x,name):
+        p=f'layers.{self.factor_layer}.self_attn.'
+        if name not in (p+'q_norm',p+'k_norm'):return super().normalization(x,name)
+        # This path does not multiply the unit-gain tail, unlike a full-vector mask.
+        unit=x/np.sqrt(np.mean(x*x,axis=-1,keepdims=True)+self.eps)
+        isq=name==p+'q_norm';head=self.a if isq else self.b
+        skip=(isq and self.move=='q_to_k') or (not isq and self.move=='k_to_q')
+        tail=unit[...,self.r:] if skip else unit[...,self.r:]*self.joint
+        return np.concatenate([unit[...,:self.r]*head[:self.r],tail],axis=-1)
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',required=True,type=Path);a=p.parse_args();root=a.root
+    # Protocol is fixed before inspecting functional measurements.
+    protocol={'fixed_intervention_layer':3,'directions':['q_to_k','k_to_q'],'diagnostic_layers':LAYERS,'no_selection':True,'no_training':True,'precision':'FP32 product; no BF16 requantization','quality_gate':{'mean_nll_delta':.01,'sequence_p95_nll_delta':.05}}
+    protocol_path=root/'gain-factorization-protocol.json'
+    if protocol_path.exists():
+        old=json.loads(protocol_path.read_text());assert all(old[k]==v for k,v in protocol.items()), 'Protocol differs from preregistration'
+    else:write_json(protocol_path,{**protocol,'registered_unix':time.time()})
+    ledger_path=root/'compute-budget-ledger.json';ledger=json.loads(ledger_path.read_text())
+    remaining=int(7200-ledger['spent_seconds']-30)
+    if remaining<60:raise TimeoutError('No remaining cumulative budget for deterministic identity verification')
+    start=time.monotonic()
+    report={'state':'running','records':[],'interventions':[]}
+    try:
+        cfg=json.loads((root/'model_config.json').read_text());c=cfg['text_config'];r=int(c['head_dim']*c['rope_parameters']['partial_rotary_factor'])
+        assert c['rms_norm_eps']==1e-6
+        assert c['head_dim']==256 and r==64 and c['num_attention_heads']==8 and c['num_key_value_heads']==2
+        lm=FactoredLM(root/'weights',cfg,seconds=remaining)
+        data=np.load(root/'baseline-activations.npz');samples=json.loads((root/'samples-tokenized.json').read_text())['samples']
+        rp=c['rope_parameters'];repeats=c['num_attention_heads']//c['num_key_value_heads']
+        records=[]
+        for layer in LAYERS:
+            lm.guard()
+            pfx=f'layers.{layer}.self_attn.'
+            aw=lm.small(pfx+'q_norm.weight');bw=lm.small(pfx+'k_norm.weight');ag,bg=1+aw,1+bw
+            q,k=data[f'qraw_{layer}'],data[f'kraw_{layer}']
+            qr,kr=rope(norm(q,aw),rp['rope_theta'],rp['partial_rotary_factor']),rope(norm(k,bw),rp['rope_theta'],rp['partial_rotary_factor'])
+            ref=np.einsum('bthd,bshd->bhts',qr,np.repeat(kr,repeats,axis=2))/np.float32(np.sqrt(c['head_dim']))
+            mask=np.triu(np.ones(ref.shape[-2:],dtype=bool),1)
+            def probs(s):
+                s=np.where(mask,-np.inf,s);e=np.exp(s-s.max(-1,keepdims=True));return e/e.sum(-1,keepdims=True)
+            pref=probs(ref)
+            for move in ('q_to_k','k_to_q'):
+                qq,kk=consolidate(q,k,ag,bg,r,move)
+                qq,kk=rope(qq,rp['rope_theta'],rp['partial_rotary_factor']),rope(kk,rp['rope_theta'],rp['partial_rotary_factor'])
+                changed=np.einsum('bthd,bshd->bhts',qq,np.repeat(kk,repeats,axis=2))/np.float32(np.sqrt(c['head_dim']))
+                pp=probs(changed)
+                for bi,s in enumerate(samples):
+                    diff=changed[bi].astype(np.float64)-ref[bi]
+                    records.append({'layer':layer,'direction':move,'sample_id':s['id'],'split':s['split'],
+                                    'score_max_abs_error':float(np.abs(diff).max()),'score_relative_l2':float(np.linalg.norm(diff)/max(np.linalg.norm(ref[bi].astype(np.float64)),1e-12)),
+                                    'probability_max_abs_error':float(np.abs(pp[bi]-pref[bi]).max()),**probability_metrics(pref[bi:bi+1],pp[bi:bi+1])})
+            save_csv(root/'gain-factorization-score-metrics.csv',records)
+        held=np.array([i for i,s in enumerate(samples) if s['split']=='heldout']);ids=np.array([s['token_ids'] for s in samples],dtype=np.int64)
+        base=np.load(root/'baseline-heldout-token-nll.npy').mean(1)
+        for move in ('q_to_k','k_to_q'):
+            lm.configure(3,move)
+            h,_=lm.forward(ids[held])
+            nll=lm.nll(h,ids[held]);np.save(root/f'gain-factorization-{move}-token-nll.npy',nll)
+            delta=nll.mean(1)-base
+            report['interventions'].append({'layer':3,'direction':move,'sequence_nll_deltas':delta.tolist(),'mean_nll_delta':float(delta.mean()),'sequence_p95_delta':float(np.quantile(delta,.95)),'passed':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05)})
+            write_json(root/'gain-factorization-summary.json',report)
+        # Local normalization timing uses precomputed gains for both implementations.
+        q,k=data['qraw_3'][:1],data['kraw_3'][:1];pfx='layers.3.self_attn.'
+        ag,bg=1+lm.small(pfx+'q_norm.weight'),1+lm.small(pfx+'k_norm.weight')
+        def baseline():return normalized(q)*ag,normalized(k)*bg
+        joint=ag[r:]*bg[r:]
+        def factored():return consolidate(q,k,ag,bg,r,'q_to_k',joint=joint)
+        for _ in range(3):baseline();factored()
+        timing=[]
+        for repeat in range(10):
+            for label,fn in (('baseline',baseline),('factored',factored)) if repeat%2==0 else (('factored',factored),('baseline',baseline)):
+                lm.guard();t=time.perf_counter()
+                for _ in range(20):fn()
+                timing.append({'repeat':repeat,'implementation':label,'seconds_per_call':(time.perf_counter()-t)/20})
+        save_csv(root/'gain-factorization-kernel-timing.csv',timing)
+        report.update(state='completed',maximum_score_abs_error=max(x['score_max_abs_error'] for x in records),maximum_probability_abs_error=max(x['probability_max_abs_error'] for x in records),
+                      bidirectional_heldout_pass=all(x['passed'] for x in report['interventions']),
+                      elementwise_gain_multiplies_saved_per_token_layer={'q_to_k':1536,'k_to_q':384},
+                      original_bf16_payload_bytes=1024,factored_lossless_mixed_payload_bytes=1024,
+                      fp32_cached_gain_payload_before=2048,fp32_cached_gain_payload_after=1280,
+                      claim_limit='Exact real-arithmetic identity only on non-RoPE coordinates; empirical FP32/NLL checks on tiny text pilot. Not evidence that two existing full layers interchange, not model-wide speedup.')
+    except Exception as exc:
+        report.update(state='stopped',error_type=type(exc).__name__,error=str(exc));raise
+    finally:
+        elapsed=time.monotonic()-start
+        report.update(elapsed_seconds=elapsed,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
+        ledger=json.loads(ledger_path.read_text());ledger['spent_seconds']+=elapsed;ledger['calls'].append({'module':'functional.gain_factorization','seconds':elapsed});write_json(ledger_path,ledger)
+        write_json(root/'gain-factorization-summary.json',report)
+
+
+if __name__=='__main__':main()
@@ -6,6 +6,7 @@ No cached decode, padding, images, generation, fused kernels or autograd.
 Full-sequence gated delta recurrence replaces the algebraically equivalent
 chunk algorithm; both are independently compared in remote tests.
 """
+import ctypes
 import json
 import time
 from pathlib import Path
@@ -87,6 +88,7 @@ class StreamingLM:
        self.qk_inputs = {}
        self.swap_observation = None
        self.swap_pressure_windows = 0
+        self.malloc_trim = getattr(ctypes.CDLL(None), 'malloc_trim', None)

    def guard(self):
        if time.monotonic() > self.deadline:
@@ -104,8 +106,9 @@ class StreamingLM:
                elapsed = now-self.swap_observation[0]
                rate = (count-self.swap_observation[1])*4096/elapsed
                self.swap_pressure_windows = self.swap_pressure_windows+1 if rate > 4*1024**2 else 0
-                if self.swap_pressure_windows >= 3:
-                    raise MemoryError('Sustained host swap IO above 4 MiB/s in three observation windows')
+                own_swap_kib = int(status.get('VmSwap', '0 kB').split()[0])
+                if self.swap_pressure_windows >= 3 and (available < 384*1024 or own_swap_kib > 128*1024):
+                    raise MemoryError(f'Sustained host swap pressure plus constrained memory: available={available} KiB, process_swap={own_swap_kib} KiB')
            self.swap_observation = (now,count)
        if rss_kib > 512*1024 or available < 256*1024:
            raise MemoryError(f'Soft memory stop: RSS {rss_kib} KiB, available {available} KiB')
@@ -130,6 +133,8 @@ class StreamingLM:
            result[:, start:end] = flat @ w.T
            del w
            t.evict()
+        if self.malloc_trim is not None:
+            self.malloc_trim(0)  # Release only this process's freed temporary heap pages.
        return result.reshape(*x.shape[:-1], outdim)

    def embedding(self, ids):
@@ -0,0 +1,134 @@
+"""Find repeated learned scalar MLP functions on real hidden inputs, no training.
+
+Four fixed layers; calibration-only selection; one frozen pair; independent heldout
+functional test and two actual network substitutions. No scale fitting or R.
+"""
+import argparse
+import hashlib
+import json
+from pathlib import Path
+import resource
+import time
+import numpy as np
+from .model import StreamingLM,silu
+from .run import write_json,save_csv
+
+LAYERS=[5,6,20,21]
+
+
+def pair_metrics(x,y):
+    x,y=x.astype(np.float64),y.astype(np.float64)
+    xx,yy=float(x@x),float(y@y);delta=float((x-y)@(x-y))
+    xc,yc=x-x.mean(),y-y.mean()
+    return {'relative_l2_a_to_b':float(np.sqrt(delta/max(xx,1e-30))),
+            'relative_l2_b_to_a':float(np.sqrt(delta/max(yy,1e-30))),
+            'cosine':float(x@y/max(np.sqrt(xx*yy),1e-30)),
+            'pearson':float(xc@yc/max(np.linalg.norm(xc)*np.linalg.norm(yc),1e-30)),
+            'rms_a':float(np.sqrt(xx/x.size)),'rms_b':float(np.sqrt(yy/y.size)),
+            'std_over_rms_a':float(np.std(x)/max(np.sqrt(xx/x.size),1e-30)),
+            'std_over_rms_b':float(np.std(y)/max(np.sqrt(yy/y.size),1e-30)),
+            'effective_energy_tokens_a':float(xx*xx/max(float((x**4).sum()),1e-30)),
+            'effective_energy_tokens_b':float(yy*yy/max(float((y**4).sum()),1e-30))}
+
+
+class NeuronReuseLM(StreamingLM):
+    def configure(self,layer,target,donor):self.reused_layer,self.target,self.donor=layer,target,donor
+    def mlp(self,h,layer):
+        p=f'layers.{layer}.mlp.'
+        z=silu(self.linear(h,p+'gate_proj'))*self.linear(h,p+'up_proj')
+        if layer==self.reused_layer:z[...,self.target]=z[...,self.donor]
+        return self.linear(z,p+'down_proj')
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);a=p.parse_args();root=a.root
+    protocol={'layers':LAYERS,'unit':'post_silu_gate_times_up_scalar_function; keep each output column',
+              'thresholds':[.05,.10,.20],'maximum_directional_relative_l2':.20,'cosine_min':.98,'pearson_min':.95,
+              'rms_floor':'at least within-layer median calibration neuron RMS','std_over_rms_min':.25,
+              'effective_energy_tokens_min':16,'max_selected_pairs':1,'selection':'smallest worst directional calibration error; then layer/id',
+              'heldout_per_text_error_max':.35,'heldout_quality_mean_nll_delta_max':.01,'heldout_quality_sequence_p95_delta_max':.05,
+              'training':False,'scale_fitting':False,'cost_required':False}
+    protocol_path=root/'neuron-reuse-protocol.json'
+    if protocol_path.exists():
+        old=json.loads(protocol_path.read_text());assert all(old[k]==v for k,v in protocol.items())
+    else:write_json(protocol_path,{**protocol,'registered_unix':time.time()})
+    ledger_path=root/'compute-budget-ledger.json';ledger=json.loads(ledger_path.read_text());remaining=int(7200-ledger['spent_seconds']-30)
+    if remaining<120:raise TimeoutError('Remaining cumulative budget insufficient')
+    started=time.monotonic();result={'state':'running','selected':None,'function_pass':False,'interventions':[]}
+    try:
+        cfg=json.loads((root/'model_config.json').read_text());lm=NeuronReuseLM(root/'weights',cfg,seconds=remaining)
+        data=np.load(root/'baseline-activations.npz');samples=json.loads((root/'samples-tokenized.json').read_text())['samples']
+        cal=np.array([i for i,s in enumerate(samples) if s['split']=='calibration']);held=np.array([i for i,s in enumerate(samples) if s['split']=='heldout'])
+        activations={};summaries=[];best=None
+        for layer in LAYERS:
+            h=data[f'mlp_{layer}'];pfx=f'layers.{layer}.mlp.'
+            z=silu(lm.linear(h,pfx+'gate_proj'))*lm.linear(h,pfx+'up_proj');activations[layer]=z
+            # Profiles use all calibration tokens; never use heldout in selection.
+            x=np.ascontiguousarray(z[cal].reshape(-1,z.shape[-1]).T,dtype=np.float64)
+            n=x.shape[1];normsq=(x*x).sum(1);means=x.mean(1);variance=np.maximum(normsq-n*means**2,0)
+            rms=np.sqrt(normsq/n);floor=float(np.median(rms));effective=normsq**2/np.maximum((x**4).sum(1),1e-30)
+            active=(rms>=floor)&(np.sqrt(variance/np.maximum(normsq,1e-30))>=.25)&(effective>=16)
+            counts={str(t):0 for t in protocol['thresholds']};tested=0;layerbest=None
+            for start in range(0,x.shape[0],256):
+                lm.guard();end=min(start+256,x.shape[0]);dot=x[start:end]@x.T
+                delta=np.maximum(normsq[start:end,None]+normsq[None,:]-2*dot,0)
+                error=np.sqrt(delta/np.maximum(np.minimum(normsq[start:end,None],normsq[None,:]),1e-30))
+                cosine=dot/np.maximum(np.sqrt(normsq[start:end,None]*normsq[None,:]),1e-30)
+                pearson=(dot-n*means[start:end,None]*means[None,:])/np.maximum(np.sqrt(variance[start:end,None]*variance[None,:]),1e-30)
+                unique=np.arange(start,end)[:,None]<np.arange(x.shape[0])[None,:]
+                allowed=unique&active[start:end,None]&active[None,:]&(cosine>=.98)&(pearson>=.95)
+                tested+=int((unique&active[start:end,None]&active[None,:]).sum())
+                for threshold in protocol['thresholds']:counts[str(threshold)]+=int((allowed&(error<=threshold)).sum())
+                mask=allowed&(error<=.20)
+                if mask.any():
+                    score=np.where(mask,error,np.inf);row,col=np.unravel_index(np.argmin(score),score.shape)
+                    candidate={'layer':layer,'a':int(start+row),'b':int(col),'calibration_worst_relative_l2':float(score[row,col])}
+                    key=lambda c:(c['calibration_worst_relative_l2'],c['layer'],c['a'],c['b'])
+                    if layerbest is None or key(candidate)<key(layerbest):layerbest=candidate
+                    if best is None or key(candidate)<key(best):best=candidate
+            summaries.append({'layer':layer,'neurons':int(x.shape[0]),'eligible_neurons':int(active.sum()),'eligible_unique_pairs_checked':tested,'rms_floor':floor,'calibration_pair_counts':counts,'best':layerbest})
+            write_json(root/'neuron-reuse-search-summary.json',summaries)
+            print(json.dumps(summaries[-1]),flush=True)
+        np.savez(root/'neuron-reuse-activations.npz',**{f'layer{k}':v for k,v in activations.items()})
+        write_json(root/'neuron-reuse-selection.json',{'selected':best,'selection_uses_heldout':False})
+        result['selected']=best
+        if best is not None:
+            layer,i,j=best['layer'],best['a'],best['b'];z=activations[layer]
+            metrics=[]
+            for split,index in [('calibration',cal),('heldout',held)]:
+                metrics.append({'split':split,'scope':'pooled',**pair_metrics(z[index,:,i].reshape(-1),z[index,:,j].reshape(-1))})
+                for idx in index:metrics.append({'split':split,'scope':samples[idx]['id'],**pair_metrics(z[idx,:,i],z[idx,:,j])})
+            save_csv(root/'neuron-reuse-function-metrics.csv',metrics)
+            pooled=next(r for r in metrics if r['split']=='heldout' and r['scope']=='pooled')
+            floor=next(r['rms_floor'] for r in summaries if r['layer']==layer)
+            function_pass=(max(pooled['relative_l2_a_to_b'],pooled['relative_l2_b_to_a'])<=.20 and pooled['cosine']>=.98 and pooled['pearson']>=.95 and
+                           min(pooled['std_over_rms_a'],pooled['std_over_rms_b'])>=.25 and min(pooled['effective_energy_tokens_a'],pooled['effective_energy_tokens_b'])>=16 and
+                           min(pooled['rms_a'],pooled['rms_b'])>=floor and
+                           all(max(r['relative_l2_a_to_b'],r['relative_l2_b_to_a'])<=.35 for r in metrics if r['split']=='heldout' and r['scope']!='pooled'))
+            result.update(function_pass=bool(function_pass),heldout_function_metrics=pooled)
+            # Show parameter similarity separately from actual functional similarity.
+            wd=[]
+            for role in ('gate_proj','up_proj'):
+                t=lm.tensors[f'model.language_model.layers.{layer}.mlp.{role}.weight'];d=t.shape[1]
+                wa,wb=t.read(slice(i*d,(i+1)*d)),t.read(slice(j*d,(j+1)*d))
+                wd.append({'role':role,'weight_cosine':float(wa.astype(np.float64)@wb/max(np.linalg.norm(wa.astype(np.float64))*np.linalg.norm(wb.astype(np.float64)),1e-30))});t.evict()
+            result['weight_diagnostics']=wd
+            if function_pass:
+                ids=np.array([s['token_ids'] for s in samples],dtype=np.int64);base=np.load(root/'baseline-heldout-token-nll.npy').mean(1)
+                for target,donor in ((i,j),(j,i)):
+                    lm.configure(layer,target,donor);h,_=lm.forward(ids[held]);nll=lm.nll(h,ids[held])
+                    np.save(root/f'neuron-reuse-{target}-uses-{donor}-token-nll.npy',nll)
+                    delta=nll.mean(1)-base
+                    result['interventions'].append({'target':target,'donor':donor,'sequence_nll_deltas':delta.tolist(),'mean_nll_delta':float(delta.mean()),'sequence_p95_delta':float(np.quantile(delta,.95)),'passed':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05)})
+                    write_json(root/'neuron-reuse-summary.json',result)
+            # No fallback after looking at heldout.
+        result.update(state='completed',empirical_repeated_function_positive=bool(result['function_pass'] and len(result['interventions'])==2 and all(r['passed'] for r in result['interventions'])),
+                      claim_limit='Distinct learned scalar MLP subfunctions on the specified short-text distribution; not whole-layer duplication, global equivalence or broad task-capability proof. No performance requirement.')
+    except Exception as exc:result.update(state='stopped',error_type=type(exc).__name__,error=str(exc));raise
+    finally:
+        elapsed=time.monotonic()-started;result.update(elapsed_seconds=elapsed,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
+        ledger=json.loads(ledger_path.read_text());ledger['spent_seconds']+=elapsed;ledger['calls'].append({'module':'functional.neuron_reuse','seconds':elapsed});write_json(ledger_path,ledger)
+        write_json(root/'neuron-reuse-summary.json',result)
+
+
+if __name__=='__main__':main()
@@ -0,0 +1,108 @@
+"""Existing learned RMSNorm modules: uniform function bound + independent probes.
+
+Deterministic exhaustive selection by a mathematical all-input error bound, not
+calibration/heldout fitting. Same role, shape and eps; one pair only.
+"""
+import argparse
+import json
+from pathlib import Path
+import resource
+import time
+import numpy as np
+from .model import StreamingLM,norm
+from .run import write_json,save_csv,error_rows
+
+ROLES=['input_layernorm','post_attention_layernorm']
+
+
+def uniform_bounds(a,b):
+    if np.any(a==0) or np.any(b==0):return None
+    diff=np.abs(a.astype(np.float64)-b.astype(np.float64))
+    return float(np.max(diff/np.abs(a))),float(np.max(diff/np.abs(b)))
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);args=p.parse_args();root=args.root
+    protocol={'roles':ROLES,'layers':list(range(24)),'same_role_only':True,'same_eps':1e-6,
+              'uniform_worst_error_limit':.20,'empirical_function_worst_relative_error_limit':.05,
+              'selection':'smallest maximum two-direction uniform bound, then role/layer; no input data used',
+              'minimum_gain_standard_deviation':.001,'max_selected_pairs':1,'heldout_nll_mean_delta_limit':.01,
+              'heldout_nll_sequence_p95_delta_limit':.05,'training':False,'cost_required':False}
+    path=root/'norm-repetition-protocol.json'
+    if path.exists():
+        existing=json.loads(path.read_text());assert all(existing[k]==v for k,v in protocol.items())
+    else:write_json(path,{**protocol,'registered_unix':time.time()})
+    ledger_path=root/'compute-budget-ledger.json';ledger=json.loads(ledger_path.read_text());remaining=int(7200-ledger['spent_seconds']-30)
+    if remaining<120:raise TimeoutError('Insufficient cumulative computation budget')
+    started=time.monotonic();result={'state':'running','selected':None,'interventions':[]}
+    try:
+        cfg=json.loads((root/'model_config.json').read_text());lm=StreamingLM(root/'weights',cfg,seconds=remaining)
+        assert lm.eps==1e-6
+        candidates=[];gains={}
+        for role in ROLES:
+            for layer in range(24):gains[(role,layer)]=1+lm.small(f'layers.{layer}.{role}.weight')
+            for i in range(24):
+                for j in range(i+1,24):
+                    a,b=gains[(role,i)],gains[(role,j)]
+                    bounds=uniform_bounds(a,b)
+                    if bounds is None:continue
+                    eligible=min(float(np.std(a)),float(np.std(b)))>=.001
+                    candidates.append({'role':role,'a':i,'b':j,'a_to_b_uniform_bound':bounds[0],'b_to_a_uniform_bound':bounds[1],
+                                       'worst_uniform_bound':max(bounds),'eligible_nonconstant_gains':eligible,
+                                       'gain_std_a':float(np.std(a)),'gain_std_b':float(np.std(b)),
+                                       'gain_min_a':float(np.min(a)),'gain_min_b':float(np.min(b))})
+        save_csv(root/'norm-repetition-uniform-bounds.csv',candidates)
+        feasible=[c for c in candidates if c['eligible_nonconstant_gains'] and c['worst_uniform_bound']<=.20]
+        counts={str(t):sum(c['eligible_nonconstant_gains'] and c['worst_uniform_bound']<=t for c in candidates) for t in (.05,.10,.20)}
+        selected=sorted(feasible,key=lambda c:(c['worst_uniform_bound'],c['role'],c['a'],c['b']))[:1]
+        write_json(root/'norm-repetition-selection.json',{'selected':selected,'uniform_bound_counts':counts,'selection_reads_no_input_data':True})
+        result['uniform_bound_counts']=counts
+        if selected:
+            c=selected[0];result['selected']=c
+            # Selection is frozen before reading tokenized inputs or their activations.
+            samples=json.loads((root/'samples-tokenized.json').read_text())['samples'];ids=np.array([s['token_ids'] for s in samples],dtype=np.int64)
+            held=np.array([i for i,s in enumerate(samples) if s['split']=='heldout'])
+            kind='attn' if c['role']=='input_layernorm' else 'mlp'
+            captures=[(kind,c['a']),(kind,c['b'])]
+            h,saved=lm.forward(ids,capture=captures)
+            # The untouched recomputation must reproduce the saved real baseline.
+            original=np.load(root/'baseline-activations.npz')['final']
+            np.testing.assert_array_equal(h,original)
+            np.savez(root/'norm-repetition-input-profiles.npz',**saved)
+            ga,gb=gains[(c['role'],c['a'])],gains[(c['role'],c['b'])]
+            rows=[]
+            for source in (c['a'],c['b']):
+                n=saved[f'{kind}_{source}'];g=gains[(c['role'],source)]
+                # n = u * gamma_source. Reconstruct common normalized input u;
+                # source gains are nonzero. Additional rounding is ~FP32 only.
+                unit=n/g
+                fa,fb=unit*ga,unit*gb
+                rec=error_rows(fa,fb,samples,{'source_layer':source,'role':c['role'],'a':c['a'],'b':c['b']})
+                rows.extend(rec)
+            save_csv(root/'norm-repetition-function-metrics.csv',rows)
+            heldrows=[r for r in rows if r['split']=='heldout']
+            function_pass=all(max(r['relative_l2'],r['reverse_relative_l2'])<=.05 for r in heldrows)
+            result.update(uniform_function_similarity_proven=True,heldout_function_pass=bool(function_pass),
+                          worst_heldout_relative_l2=max(max(r['relative_l2'],r['reverse_relative_l2']) for r in heldrows),
+                          worst_heldout_cosine=min(r['cosine'] for r in heldrows),untouched_full_forward_bitwise_matches_baseline=True)
+            if function_pass:
+                baseline=np.load(root/'baseline-heldout-token-nll.npy').mean(1)
+                for target,donor in ((c['a'],c['b']),(c['b'],c['a'])):
+                    lm.norm_replacement=(f'layers.{target}.{c["role"]}',f'layers.{donor}.{c["role"]}')
+                    changed,_=lm.forward(ids[held]);nll=lm.nll(changed,ids[held]);np.save(root/f'norm-repetition-{target}-uses-{donor}-token-nll.npy',nll)
+                    delta=nll.mean(1)-baseline
+                    result['interventions'].append({'target':target,'donor':donor,'sequence_nll_deltas':delta.tolist(),
+                         'mean_nll_delta':float(delta.mean()),'sequence_p95_nll_delta':float(np.quantile(delta,.95)),
+                         'passed_quality':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05)})
+                    write_json(root/'norm-repetition-summary.json',result)
+            result['learned_function_repetition_positive']=bool(function_pass)
+            result['bidirectional_network_quality_pass']=bool(len(result['interventions'])==2 and all(x['passed_quality'] for x in result['interventions']))
+        result.update(state='completed',claim_limit='Existing learned RMSNorm module functions with identical eps and shape; uniform real-arithmetic relative bound and tiny real-text probes. Does not establish attention/MLP-body duplication or broad task capability.')
+    except Exception as exc:result.update(state='stopped',error_type=type(exc).__name__,error=str(exc));raise
+    finally:
+        elapsed=time.monotonic()-started;result.update(elapsed_seconds=elapsed,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
+        ledger=json.loads(ledger_path.read_text());ledger['spent_seconds']+=elapsed;ledger['calls'].append({'module':'functional.norm_repetition','seconds':elapsed});write_json(ledger_path,ledger)
+        write_json(root/'norm-repetition-summary.json',result)
+
+
+if __name__=='__main__':main()
@@ -0,0 +1,46 @@
+"""Package code-independent scientific outputs, retain large activation caches externally.
+
+The experiment directory remains immutable and complete. The delivery directory
+contains all figure source tables/NLL arrays plus hashes/locations for three large
+reproducible activation caches. Never deletes model weights or cache files.
+"""
+import argparse
+import hashlib
+import json
+from pathlib import Path
+import shutil
+from .run import write_json
+
+CACHES={'baseline-activations.npz','neuron-reuse-activations.npz','norm-repetition-input-profiles.npz'}
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',required=True,type=Path);p.add_argument('--output',required=True,type=Path);a=p.parse_args()
+    root,out=a.root.resolve(),a.output.resolve();assert root!=out
+    out.mkdir(parents=True,exist_ok=True)
+    full=json.loads((root/'checksums-functional.json').read_text())
+    files=json.loads((root/'archive-files.json').read_text())
+    for name in files:
+        src=root/name
+        if name in CACHES:continue
+        dest=out/name;dest.parent.mkdir(parents=True,exist_ok=True);shutil.copy2(src,dest)
+    external=[]
+    for name in sorted(CACHES):
+        src=root/name;digest=hashlib.sha256(src.read_bytes()).hexdigest();assert digest==full[name]
+        external.append({'name':name,'server_path':str(src),'bytes':src.stat().st_size,'sha256':digest,'retained':True,'reproducible_by':'functional.run / functional.neuron_reuse / functional.norm_repetition'})
+    write_json(out/'external-activation-cache.json',{'host':'8.211.154.134','files':external,'not_model_weights':True,
+               'reason':'Large intermediate caches retained on the authorized server; all figure source tables and token NLL arrays are delivered in Git.'})
+    shutil.copy2(root/'checksums-functional.json',out/'checksums-full-server.json')
+    (out/'DELIVERY.md').write_text('# Scientific delivery and retained caches\n\nAll figure CSV/JSON source data, per-token NLL, protocols, reports, metadata and historical logs are included.\nThree large reproducible intermediate activation caches remain on 8.211.154.134 and in the original full local archive;\n`external-activation-cache.json` records exact locations, sizes and SHA256. No weight or cache was deleted.\nThe original full experiment manifest is `checksums-full-server.json`; the delivery-only manifest is\n`checksums-functional.json`. No model checkpoint or tokenizer payload is in this Git delivery.\n')
+    validation=json.loads((out/'audit-validation.json').read_text());validation.update(delivery_excludes_external_activation_caches=True,full_experiment_checksums='checksums-full-server.json',figure_source_tables_and_token_nll_complete=True)
+    write_json(out/'audit-validation.json',validation)
+    checks={}
+    for file in sorted(out.rglob('*')):
+        if file.is_file() and file.name not in ('checksums-functional.json','audit-validation.json','archive-files.json'):
+            checks[str(file.relative_to(out))]=hashlib.sha256(file.read_bytes()).hexdigest()
+    write_json(out/'checksums-functional.json',checks)
+    write_json(out/'archive-files.json',[*checks,'checksums-functional.json','audit-validation.json','archive-files.json'])
+    print(json.dumps({'delivery_files':len(checks)+3,'checksummed_files':len(checks),'external_caches':len(external),'weights_and_caches_retained':True}))
+
+
+if __name__=='__main__':main()
@@ -14,14 +14,26 @@ def main():
    samples,ids=tokenize(a.root)
    start=time.time()
    lm=StreamingLM(a.root/'weights',json.loads((a.root/'model_config.json').read_text()),seconds=1200)
-    def progress(layer):print(json.dumps({'preflight_layer':layer,'seconds':time.time()-start}),flush=True)
-    h,_=lm.forward(ids[:1,:8],progress=progress)
-    nll=lm.nll(h,ids[:1,:8])
-    if not np.isfinite(nll).all():raise FloatingPointError('Nonfinite NLL')
-    write_json(a.root/'preflight.json',{'passed':True,'sample_id':samples[0]['id'],'tokens':8,
-               'elapsed_seconds':time.time()-start,'peak_rss_kib':resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,
-               'mean_nll':float(nll.mean()),'token_nll':nll.tolist(),
-               'limitation':'Real-weight capacity check, not an independent correctness oracle or quality benchmark'})
+    snapshots=[]
+    result={'passed':False,'sample_id':samples[0]['id'],'tokens':8}
+    def progress(layer):
+        status=dict(line.split(':',1) for line in Path('/proc/self/status').read_text().splitlines() if ':' in line)
+        snapshots.append({'layer':layer,'seconds':time.time()-start,'rss_kib':int(status['VmRSS'].split()[0]),
+                          'process_swap_kib':int(status.get('VmSwap','0 kB').split()[0]),
+                          'available_kib':int(next(line.split()[1] for line in Path('/proc/meminfo').read_text().splitlines() if line.startswith('MemAvailable:')))})
+        print(json.dumps(snapshots[-1]),flush=True)
+    try:
+        h,_=lm.forward(ids[:1,:8],progress=progress)
+        nll=lm.nll(h,ids[:1,:8])
+        if not np.isfinite(nll).all():raise FloatingPointError('Nonfinite NLL')
+        result.update(passed=True,mean_nll=float(nll.mean()),token_nll=nll.tolist())
+    except Exception as exc:
+        result.update(error_type=type(exc).__name__,error=str(exc))
+        raise
+    finally:
+        result.update(elapsed_seconds=time.time()-start,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,
+                      resource_snapshots=snapshots,limitation='Real-weight capacity check, not an independent correctness oracle or quality benchmark')
+        write_json(a.root/'preflight.json',result)


 if __name__=='__main__':main()
@@ -14,7 +14,7 @@ import numpy as np
 from .model import StreamingLM, delta_rule, rope, norm

 EXPECTED_COMMIT = 'fd3cf0140de03f7619ac95b5cfed05b711ff9e22'
-NAMES = {'l2norm', 'torch_chunk_gated_delta_rule', 'torch_recurrent_gated_delta_rule',
+NAMES = {'apply_mask_to_padding_states', 'l2norm', 'torch_chunk_gated_delta_rule', 'torch_recurrent_gated_delta_rule',
         'causal_conv1d_fn', 'Qwen3_5RMSNormGated', 'Qwen3_5GatedDeltaNet',
         'rotate_half', 'apply_rotary_pos_emb', 'repeat_kv', 'eager_attention_forward',
         'Qwen3_5Attention', 'Qwen3_5MLP', 'Qwen3_5RMSNorm', 'Qwen3_5DecoderLayer',
@@ -30,7 +30,7 @@ def official_namespace(source):
        if isinstance(node, (ast.FunctionDef, ast.ClassDef)) and node.name in NAMES:
            for child in ast.walk(node):
                if isinstance(child, (ast.FunctionDef, ast.ClassDef)):
-                    child.decorator_list = []
+                    child.decorator_list = [d for d in child.decorator_list if isinstance(d, ast.Name) and d.id in ('staticmethod','classmethod')]
            nodes.append(node)
    assert {x.name for x in nodes} == NAMES
    tree = ast.Module(body=[ast.ImportFrom(module='__future__',names=[ast.alias(name='annotations')],level=0),*nodes],type_ignores=[])
@@ -134,7 +134,7 @@ def main():
        actual,_=lm.forward(ids)
        check('complete_two_layer_text_forward',actual,expected)
    result={'passed':True,'reference_commit':EXPECTED_COMMIT,'source_sha256':hashlib.sha256(a.source.read_bytes()).hexdigest(),
-            'torch':torch.__version__,'numpy':np.__version__,'tests':evidence,'limitations':'FP32 CPU semantics on tiny synthetic configurations; not full-checkpoint BF16 kernel equivalence'}
+            'torch':torch.__version__,'numpy':np.__version__,'implementation_sha256':hashlib.sha256(Path(__file__).with_name('model.py').read_bytes()).hexdigest(),'tests':evidence,'limitations':'FP32 CPU semantics on tiny synthetic configurations; not full-checkpoint BF16 kernel equivalence'}
    a.output.write_text(json.dumps(result,indent=2))
    print(json.dumps({'passed':True,'checks':len(evidence),'maximum_absolute_discrepancy':max(x['max_abs_error'] for x in evidence)}))

@@ -23,9 +23,9 @@ def main():
        ax.bar(np.arange(len(c)),[max(v['maximum_symmetric_relative_error'],1e-8) for v in c],color=['#238b45' if v['passed'] else '#9e9e9e' for v in c])
        ax.axhline(.05,color='#b2182b',linestyle='--',label='Relative L2 screen = 0.05; other gates also apply')
        ax.set_yscale('log');ax.set_xticks(np.arange(len(c)),[v['id'] for v in c],rotation=45,ha='right')
-        ax.set_ylabel('Worst calibration symmetric relative L2')
+        ax.set_ylabel('Worst calibration directional relative L2')
        ax.set_title('Fixed candidates; same-input function comparison\n4 calibration texts, 32 tokens; no training')
-        ax.legend(loc='upper left',bbox_to_anchor=(0,1.01),fontsize=8);fig.tight_layout();save(fig,'candidate_function_errors')
+        ax.legend(loc='upper left',bbox_to_anchor=(0,-.38),fontsize=8);fig.tight_layout();save(fig,'candidate_function_errors')
    if (root/'interventions.json').exists():
        interventions=json.loads((root/'interventions.json').read_text())
        fig,ax=plt.subplots(figsize=(8,4.5))
@@ -36,13 +36,68 @@ def main():
        ax.set_xticks(np.arange(4),['heldout zh1','heldout zh2','heldout en1','heldout en2'])
        ax.set_ylabel('Token NLL change (nat/token)');ax.set_title('Single-site interventions; frozen candidate\n4 heldout texts, 32 tokens; limited pilot evidence')
        ax.legend(loc='upper left',bbox_to_anchor=(0,-.18),fontsize=8);fig.tight_layout();save(fig,'heldout_intervention_nll')
-    text=['# CPU functional reuse pilot','',f'- State: {status["state"]}',f'- Positive bidirectional pilot: {status.get("positive_bidirectional_quality",False)}',
-          f'- Elapsed analysis seconds: {status.get("elapsed_seconds")}',f'- Peak RSS KiB: {status.get("peak_rss_kib")}',
-          '- Original BF16 weights decoded into FP32 CPU operators, no training.',
-          '- Calibration and heldout samples are distinct but very small, short and experiment-authored.',
-          '- A positive result is limited to these empirical tests, not global equivalence or GPU speedup.',
-          '- Weight retention: keep until explicit user deletion request; no automatic cleanup.','']
-    if status['state']!='completed':text+=['## Stop evidence','',f'{status.get("error_type")}: {status.get("error")}','']
+    norm_path=root/'norm-repetition-summary.json'
+    repetition=json.loads(norm_path.read_text()) if norm_path.exists() else {}
+    if (root/'norm-repetition-function-metrics.csv').exists():
+        with (root/'norm-repetition-function-metrics.csv').open() as f:rows=list(csv.DictReader(f))
+        sample_ids=list(dict.fromkeys(row['sample_id'] for row in rows))
+        fig,ax=plt.subplots(figsize=(9,4.8))
+        for source in sorted({row['source_layer'] for row in rows},key=int):
+            rr=[r for r in rows if r['source_layer']==source]
+            ax.plot(range(len(rr)),[max(float(r['relative_l2']),float(r['reverse_relative_l2'])) for r in rr],'o-',label=f'Input captured at layer {source}')
+        ax.axhline(.05,color='#b2182b',ls='--',label='Heldout empirical screen = 0.05')
+        chosen=repetition.get('selected')
+        if chosen:ax.axhline(chosen['worst_uniform_bound'],color='#6a51a3',ls=':',label='Proven uniform all-input bound')
+        ax.axvline(3.5,color='gray',linewidth=.8)
+        ax.set_xticks(range(len(sample_ids)),sample_ids,rotation=30,ha='right');ax.set_ylabel('Worst of two directional relative L2 errors')
+        ax.set_title('Learned input RMSNorm at layer indices 12/13: same-input comparison\nCalibration (left) / independent heldout texts (right), 32 tokens each')
+        ax.legend(loc='upper left',bbox_to_anchor=(0,-.25),fontsize=8);fig.tight_layout();save(fig,'learned_norm_function_errors')
+    if repetition.get('interventions'):
+        fig,ax=plt.subplots(figsize=(8,4.5))
+        for v in repetition['interventions']:
+            ax.plot(range(4),v['sequence_nll_deltas'],'o-',label=f'Layer {v["target"]} uses {v["donor"]}; mean={v["mean_nll_delta"]:.5g}')
+        ax.axhline(0,color='gray',linewidth=.8);ax.set_xticks(range(4),['zh1','zh2','en1','en2'])
+        ax.set_xlabel('Independent heldout text (32 tokens)');ax.set_ylabel('Mean token NLL change (nat/token)')
+        ax.set_title('Bidirectional single-site substitution of existing learned RMSNorm modules')
+        ax.legend(loc='upper left',bbox_to_anchor=(0,-.18),fontsize=8);fig.tight_layout();save(fig,'learned_norm_network_substitution')
+    if (root/'gain-factorization-score-metrics.csv').exists():
+        with (root/'gain-factorization-score-metrics.csv').open() as f:rr=list(csv.DictReader(f))
+        layers=sorted({int(v['layer']) for v in rr});fig,axes=plt.subplots(1,2,figsize=(10,4))
+        for ax,key,label in zip(axes,['score_max_abs_error','probability_max_abs_error'],['Maximum absolute score error','Maximum absolute probability error']):
+            for direction in ('q_to_k','k_to_q'):
+                ax.plot(layers,[max(float(v[key]) for v in rr if int(v['layer'])==layer and v['direction']==direction) for layer in layers],'o-',label=direction)
+            ax.set_xlabel('Full-attention layer index');ax.set_xticks(layers);ax.set_ylabel(label);ax.ticklabel_format(axis='y',style='sci',scilimits=(0,0));ax.legend(loc='upper left',fontsize=8)
+        fig.suptitle('Supplement: algebraic reparameterization, separate from learned-function repetition');fig.tight_layout();save(fig,'algebraic_gain_identity')
+    neuron_path=root/'neuron-reuse-summary.json'
+    neuron=json.loads(neuron_path.read_text()) if neuron_path.exists() else {}
+    text=['# CPU learned-function repetition pilot','',
+          'Primary question: do two distinct trained parts implement similar functions? Cost is not a success condition.',
+          f'- Existing learned RMSNorm function positive: {repetition.get("learned_function_repetition_positive",False)}',
+          f'- Bidirectional network quality screen passed: {repetition.get("bidirectional_network_quality_pass",False)}',
+          f'- Learned scalar MLP neuron search positive: {neuron.get("empirical_repeated_function_positive",False)}',
+          f'- Original 11 candidate run state: {status["state"]}; selected: {status.get("selected_candidate",[])}',
+          '- All model weights are retained on the server until the user explicitly requests deletion.','']
+    if repetition.get('selected'):
+        c=repetition['selected'];text += ['## Existing learned module evidence','',
+          f'Role: {c["role"]}; layers {c["a"]} and {c["b"]}; each maps 2048-dimensional input to output.',
+          f'Uniform real-arithmetic relative-error bound, first-to-second: {c["a_to_b_uniform_bound"]:.12g}; reverse: {c["b_to_a_uniform_bound"]:.12g}.',
+          f'Maximum heldout directional output relative L2: {repetition.get("worst_heldout_relative_l2")}; minimum cosine: {repetition.get("worst_heldout_cosine")}.',
+          'Same epsilon and nonzero gains: the common RMS denominator cancels. For any nonzero input x,',
+          '`||f_a(x)-f_b(x)|| / ||f_a(x)|| <= max_j |a_j-b_j|/|a_j|`; swap a/b for the reverse direction.',
+          'Selection used only the tight all-input bound across 552 same-role pairs, before inspecting inputs.',
+          'Real probes use four calibration and four heldout experiment-authored texts, 32 tokens each, and both source layers.',
+          'These are learned, nonconstant gain vectors. The claim concerns RMSNorm modules; it does not establish attention/MLP-body duplication.','']
+        for v in repetition.get('interventions',[]):text += [f'- Layer {v["target"]} uses {v["donor"]}: mean heldout NLL delta {v["mean_nll_delta"]:.12g}; sequence P95 delta {v["sequence_p95_nll_delta"]:.12g}; screen pass={v["passed_quality"]}.']
+    text += ['', '## Negative results and separate algebraic result','',
+             'The original whole-attention, whole-MLP and within-layer Q/K norm candidates did not pass their gates.',
+             'The scalar neuron search excludes low-RMS/near-constant profiles and selects at most one pair without heldout feedback; consult its search summary for coverage.',
+             'Q/K non-RoPE gain consolidation is a separately verified real-arithmetic identity, not evidence that two existing learned modules duplicate functions.',
+             'Earlier local CPU timing records are preserved for provenance; performance is outside the current success criterion.','',
+             '## Limits and provenance','',
+             'Original BF16 weights were decoded into streaming FP32 CPU operators; no training, scale fitting, R fitting or GPU.',
+             'The numerical reference includes 22 independent official Torch comparisons. Early adapter errors and two resource-guard stops are preserved as historical logs.',
+             'The persistent computation ledger includes a conservative 180-second reserve for early checks; no budget reset after that reserve.',
+             'The text sample is tiny and short. Network NLL checks are not broad task-accuracy validation.','']
    (root/'FUNCTIONAL_REPORT.md').write_text('\n'.join(text))


@@ -0,0 +1,62 @@
+"""Measure an explicit two-component alias storage layout, not inference speed.
+
+Payloads remain on the server under weights/ and must not be committed or cleaned
+without the user's explicit deletion request. Manifest/byte/hash evidence may be archived.
+"""
+import argparse
+import hashlib
+import json
+from pathlib import Path
+import numpy as np
+from pilot.tensors import load_tensors,decode
+from .run import write_json
+
+
+def payload(t,path):
+    digest=hashlib.sha256()
+    with path.open('wb') as f:
+        for start in range(0,t.raw.size,262144):
+            b=t.raw[start:start+262144].tobytes();f.write(b);digest.update(b)
+            t.evict()
+    return {'file':path.name,'dtype':t.dtype,'shape':list(t.shape),'bytes':path.stat().st_size,'sha256':digest.hexdigest()}
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);a=p.parse_args();root=a.root
+    selected=json.loads((root/'selection.json').read_text())['selected']
+    if not selected:
+        write_json(root/'storage-evidence.json',{'measured':False,'reason':'No calibration candidate selected'});return
+    c=selected[0]
+    if c['kind']!='qk_norm':
+        write_json(root/'storage-evidence.json',{'measured':False,'reason':'Only two-gain alias layout implemented in this bounded pilot; larger-module savings remain theoretical'});return
+    tensors=load_tensors(root/'weights')
+    prefix=f'model.language_model.layers.{c["i"]}.self_attn.'
+    names=[prefix+'q_norm.weight',prefix+'k_norm.weight']
+    # Original pair plus two alternative layouts; each is counted independently.
+    path=root/'component-layout'/'weights';path.mkdir(parents=True,exist_ok=True)
+    original={'format':'two_component_raw_v1','tensors':{}}
+    for short,name in zip(('q','k'),names):original['tensors'][name]=payload(tensors[name],path/f'{short}.bin')
+    original_bytes=json.dumps(original,separators=(',',':'),sort_keys=True).encode()
+    (path/'original-layout.json').write_bytes(original_bytes)
+    evidence=[]
+    for target,donor in ((names[0],names[1]),(names[1],names[0])):
+        entry=original['tensors'][donor]
+        layout={'format':'two_component_raw_v1','tensors':{donor:entry},'aliases':{target:donor}}
+        encoded=json.dumps(layout,separators=(',',':'),sort_keys=True).encode()
+        file=path/('share-'+('k' if donor==names[1] else 'q')+'.json');file.write_bytes(encoded)
+        t=tensors[donor]
+        raw=np.fromfile(path/entry['file'],dtype=t.raw.dtype)
+        reconstructed=decode(raw,t.dtype).reshape(t.shape)
+        np.testing.assert_array_equal(reconstructed,t.read().reshape(t.shape));t.evict()
+        before=sum(e['bytes'] for e in original['tensors'].values())+len(original_bytes)
+        after=entry['bytes']+len(encoded)
+        evidence.append({'target':target,'donor':donor,'original_payload_bytes':sum(e['bytes'] for e in original['tensors'].values()),
+                         'shared_payload_bytes':entry['bytes'],'original_descriptor_bytes':len(original_bytes),
+                         'shared_descriptor_bytes':len(encoded),'original_total_bytes':before,'shared_total_bytes':after,
+                         'net_saved_bytes':before-after,'shared_descriptor_sha256':hashlib.sha256(encoded).hexdigest(),
+                         'decoded_donor_exactly_matches_intervention_gain':True})
+    write_json(root/'storage-evidence.json',{'measured':True,'candidate':c['id'],'layouts':evidence,
+               'claim_limit':'Measured standalone component layout including descriptors, not an integrated compressed checkpoint, latency gain or full-model RSS reduction. Two sharing directions are alternatives and must not be added.'})
+
+
+if __name__=='__main__':main()
@@ -22,13 +22,32 @@ def main():
        state.update(kwargs,supervisor_elapsed_seconds=time.monotonic()-start)
        write_json(root/'supervisor-status.json',state)
    def execute(module,args,timeout,log):
-        with (root/log).open('w') as out:
-            r=subprocess.run([sys.executable,'-B','-m',module,*args],stdout=out,stderr=subprocess.STDOUT,timeout=timeout)
+        ledger_path=root/'compute-budget-ledger.json'
+        ledger=json.loads(ledger_path.read_text()) if ledger_path.exists() else {'spent_seconds':0.,'calls':[]}
+        timeout=min(timeout,int(7200-ledger['spent_seconds']))
+        if timeout<1:raise TimeoutError('Cumulative two-hour compute budget exhausted')
+        t=time.monotonic()
+        try:
+            with (root/log).open('w') as out:
+                r=subprocess.run([sys.executable,'-B','-m',module,*args],stdout=out,stderr=subprocess.STDOUT,timeout=timeout)
+        finally:
+            elapsed=time.monotonic()-t
+            ledger['spent_seconds']+=elapsed
+            ledger['calls'].append({'module':module,'seconds':elapsed})
+            write_json(ledger_path,ledger)
        if r.returncode:raise RuntimeError(f'{module} failed: exit {r.returncode}, see {log}')
    status()
    try:
-        while not all(importlib.util.find_spec(name) for name in ('torch','tokenizers')):
+        while True:
+            ready=False
+            if all(importlib.util.find_spec(name) for name in ('torch','tokenizers')):
+                try:
+                    r=subprocess.run([sys.executable,'-B','-c','import torch, tokenizers; torch.empty(1)'],capture_output=True,timeout=30)
+                    ready=r.returncode==0
+                except subprocess.TimeoutExpired:pass
+            if ready:break
            if time.monotonic()-start>3600:raise TimeoutError('Binary reference dependencies not ready within one hour')
+            status()
            time.sleep(15)
        compute_start=time.monotonic()
        status(state='running',phase='reference_check')
@@ -47,15 +66,18 @@ def main():
            except (OSError,ValueError,KeyError):ready=False
            if ready:break
            if time.monotonic()-start>a.download_wait_seconds:raise TimeoutError('Verified weight download not ready before wait deadline')
+            status()
            time.sleep(15)
        status(state='running',phase='real_weight_preflight')
        compute_start=time.monotonic()
        execute('functional.preflight',['--root',str(root)],1200,'preflight.log')
        preflight_seconds=time.monotonic()-compute_start
-        remaining=int(7200-reference_seconds-preflight_seconds-60)
+        remaining=int(7200-json.loads((root/'compute-budget-ledger.json').read_text())['spent_seconds']-60)
        if remaining<60:raise TimeoutError('No remaining bounded functional computation budget')
        status(phase='functional_analysis',preflight_seconds=preflight_seconds,remaining_seconds=remaining)
        execute('functional.run',['--root',str(root),'--seconds',str(remaining)],remaining+15,'functional-run.log')
+        status(phase='storage_accounting')
+        execute('functional.storage',['--root',str(root)],120,'storage-accounting.log')
        status(phase='report')
        execute('functional.report',['--root',str(root)],120,'functional-report.log')
        status(state='completed',phase='done')
@@ -0,0 +1,7 @@
+# Scientific delivery and retained caches
+
+All figure CSV/JSON source data, per-token NLL, protocols, reports, metadata and historical logs are included.
+Three large reproducible intermediate activation caches remain on 8.211.154.134 and in the original full local archive;
+`external-activation-cache.json` records exact locations, sizes and SHA256. No weight or cache was deleted.
+The original full experiment manifest is `checksums-full-server.json`; the delivery-only manifest is
+`checksums-functional.json`. No model checkpoint or tokenizer payload is in this Git delivery.
@@ -0,0 +1,36 @@
+# CPU learned-function repetition pilot
+
+Primary question: do two distinct trained parts implement similar functions? Cost is not a success condition.
+- Existing learned RMSNorm function positive: True
+- Bidirectional network quality screen passed: True
+- Learned scalar MLP neuron search positive: False
+- Original 11 candidate run state: completed; selected: []
+- All model weights are retained on the server until the user explicitly requests deletion.
+
+## Existing learned module evidence
+
+Role: input_layernorm; layers 12 and 13; each maps 2048-dimensional input to output.
+Uniform real-arithmetic relative-error bound, first-to-second: 0.101851851852; reverse: 0.113402061856.
+Maximum heldout directional output relative L2: 0.04528510757179746; minimum cosine: 0.9994870299724158.
+Same epsilon and nonzero gains: the common RMS denominator cancels. For any nonzero input x,
+`||f_a(x)-f_b(x)|| / ||f_a(x)|| <= max_j |a_j-b_j|/|a_j|`; swap a/b for the reverse direction.
+Selection used only the tight all-input bound across 552 same-role pairs, before inspecting inputs.
+Real probes use four calibration and four heldout experiment-authored texts, 32 tokens each, and both source layers.
+These are learned, nonconstant gain vectors. The claim concerns RMSNorm modules; it does not establish attention/MLP-body duplication.
+
+- Layer 12 uses 13: mean heldout NLL delta 3.25651368766e-05; sequence P95 delta 0.000984639618287; screen pass=True.
+- Layer 13 uses 12: mean heldout NLL delta -0.000363972997404; sequence P95 delta 0.000709721155035; screen pass=True.
+
+## Negative results and separate algebraic result
+
+The original whole-attention, whole-MLP and within-layer Q/K norm candidates did not pass their gates.
+The scalar neuron search excludes low-RMS/near-constant profiles and selects at most one pair without heldout feedback; consult its search summary for coverage.
+Q/K non-RoPE gain consolidation is a separately verified real-arithmetic identity, not evidence that two existing learned modules duplicate functions.
+Earlier local CPU timing records are preserved for provenance; performance is outside the current success criterion.
+
+## Limits and provenance
+
+Original BF16 weights were decoded into streaming FP32 CPU operators; no training, scale fitting, R fitting or GPU.
+The numerical reference includes 22 independent official Torch comparisons. Early adapter errors and two resource-guard stops are preserved as historical logs.
+The persistent computation ledger includes a conservative 180-second reserve for early checks; no budget reset after that reserve.
+The text sample is tiny and short. Network NLL checks are not broad task-accuracy validation.
@@ -0,0 +1,88 @@
+[
+  "DELIVERY.md",
+  "FUNCTIONAL_REPORT.md",
+  "attention-head-metrics.csv",
+  "baseline-heldout-token-nll.npy",
+  "calibration-metrics.csv",
+  "candidate-summary.json",
+  "checksums-full-server.json",
+  "compute-budget-ledger.json",
+  "download.log",
+  "experiment.json",
+  "external-activation-cache.json",
+  "figures/algebraic_gain_identity.pdf",
+  "figures/algebraic_gain_identity.png",
+  "figures/algebraic_gain_identity.svg",
+  "figures/candidate_function_errors.pdf",
+  "figures/candidate_function_errors.png",
+  "figures/candidate_function_errors.svg",
+  "figures/learned_norm_function_errors.pdf",
+  "figures/learned_norm_function_errors.png",
+  "figures/learned_norm_function_errors.svg",
+  "figures/learned_norm_network_substitution.pdf",
+  "figures/learned_norm_network_substitution.png",
+  "figures/learned_norm_network_substitution.svg",
+  "final-report.log",
+  "final-source-hashes.json",
+  "final-tests.txt",
+  "functional-code-hashes.json",
+  "functional-environment.json",
+  "functional-report.log",
+  "functional-run.log",
+  "functional-status.json",
+  "gain-factorization-k_to_q-token-nll.npy",
+  "gain-factorization-kernel-timing.csv",
+  "gain-factorization-optimized-summary.json",
+  "gain-factorization-optimized-timing.csv",
+  "gain-factorization-optimized.log",
+  "gain-factorization-protocol.json",
+  "gain-factorization-q_to_k-token-nll.npy",
+  "gain-factorization-score-metrics.csv",
+  "gain-factorization-summary.json",
+  "gain-factorization.log",
+  "install-torch-disk-tmp.log",
+  "install-torch.log",
+  "manifest.json",
+  "model_config.json",
+  "neuron-reuse-protocol.json",
+  "neuron-reuse-search-summary.json",
+  "neuron-reuse-selection.json",
+  "neuron-reuse-summary.json",
+  "neuron-reuse.log",
+  "norm-repetition-12-uses-13-token-nll.npy",
+  "norm-repetition-13-uses-12-token-nll.npy",
+  "norm-repetition-function-metrics.csv",
+  "norm-repetition-protocol.json",
+  "norm-repetition-selection.json",
+  "norm-repetition-summary.json",
+  "norm-repetition-uniform-bounds.csv",
+  "norm-repetition.log",
+  "preflight-first-attempt.log",
+  "preflight-second-attempt.json",
+  "preflight-second-attempt.log",
+  "preflight.json",
+  "preflight.log",
+  "reference-check-first-attempt.log",
+  "reference-check-second-attempt.log",
+  "reference-check.json",
+  "reference-check.log",
+  "retention-policy.json",
+  "samples-tokenized.json",
+  "selection.json",
+  "source_metadata.json",
+  "storage-accounting.log",
+  "storage-evidence.json",
+  "supervisor-error.txt",
+  "supervisor-first-attempt-error.txt",
+  "supervisor-first-attempt.json",
+  "supervisor-second-attempt.json",
+  "supervisor-status.json",
+  "supervisor-third-attempt.json",
+  "supervisor.log",
+  "tokenizer-download.log",
+  "tokenizers-wheel.log",
+  "unit-tests.txt",
+  "checksums-functional.json",
+  "audit-validation.json",
+  "archive-files.json"
+]
@@ -0,0 +1,193 @@
+candidate,source,sample_id,head,first_key_mass_a,first_key_mass_b,js_without_first_key,without_first_key_valid_rows,js_mean,js_p95,js_without_first_query,top5_overlap
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+{
+  "passed": true,
+  "validated_unix": 1794204667.0190518,
+  "checksummed_files": 83,
+  "proof_recomputed_from_retained_weights": true,
+  "heldout_nll_recomputed_from_saved_arrays": true,
+  "weights_present_and_retained": true,
+  "archive_excludes_weights_and_tokenizer": true,
+  "delivery_excludes_external_activation_caches": true,
+  "full_experiment_checksums": "checksums-full-server.json",
+  "figure_source_tables_and_token_nll_complete": true
+}
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+test_causal_convolution_and_norm_boundary (test_functional.FunctionalTests.test_causal_convolution_and_norm_boundary) ... ok
+test_causality_and_identical_function_metrics (test_functional.FunctionalTests.test_causality_and_identical_function_metrics) ... ok
+test_neuron_function_metrics_and_targeted_replacement (test_functional.FunctionalTests.test_neuron_function_metrics_and_targeted_replacement) ... ok
+test_nonrotary_gain_identity_and_rotary_negative_control (test_functional.FunctionalTests.test_nonrotary_gain_identity_and_rotary_negative_control) ... ok
+test_streamed_projection_embedding_and_output_head (test_functional.FunctionalTests.test_streamed_projection_embedding_and_output_head) ... ok
+test_uniform_rmsnorm_function_bound_is_tight (test_functional.FunctionalTests.test_uniform_rmsnorm_function_bound_is_tight) ... ok
+test_3d_reader_and_exact_block (test_pilot.PilotTests.test_3d_reader_and_exact_block) ... ok
+test_bfloat16 (test_pilot.PilotTests.test_bfloat16) ... ok
+test_directional_replacement_error_and_scaling (test_pilot.PilotTests.test_directional_replacement_error_and_scaling) ... ok
+test_end_to_end_synthetic_groups_and_savings (test_pilot.PilotTests.test_end_to_end_synthetic_groups_and_savings) ... Inventory: 4 tensors, 33792 bytes
+tensor: sampling 4 objects
+block: sampling 22 objects
+block: verified 100/109
+{"model": "synthetic", "revision": "test", "tensor_count": 4, "parameter_count": 8448, "model_weight_bytes": 33792, "ndim_counts": {"2": 2, "3": 2}, "elapsed_seconds": 0.1379160056822002, "analysis_budget_seconds": 60, "peak_rss_kib": 53592, "python": "3.14.4", "numpy": "2.5.4", "seed": 20261109, "limitations": ["No inference or functional equivalence measured", "No exhaustive pair search", "Block search uses per-role/shape/scale reservoirs of 256", "Different block scales overlap; savings must not be added", "Block savings count only verified disjoint sampled blocks, no extrapolation", "Spectral probes use aligned bounded subvolumes, not whole tensors", "Shared encoding is theoretical, not a runnable compressed checkpoint", "No cross-role grouping, neuron permutation alignment, or training"], "completed": true}
+ok
+test_no_transitive_chaining (test_pilot.PilotTests.test_no_transitive_chaining) ... ok
+test_ranged_download_preserves_prefix (test_pilot.PilotTests.test_ranged_download_preserves_prefix) ... Weight download 36/36 bytes (100.0%)
+ok
+
+----------------------------------------------------------------------
+Ran 12 tests in 40.946s
+
+OK
@@ -0,0 +1,12 @@
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+  "claim_limit": "Hot-array CPU normalization microbenchmark against equally allocation-optimized baseline; no full-model speedup or independent statistical significance claim.",
+  "elapsed_seconds": 4.185962562914938,
+  "peak_rss_kib": 62168
+}
@@ -0,0 +1,91 @@
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@@ -0,0 +1,23 @@
+{
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+  "directions": [
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+  "diagnostic_layers": [
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+  "quality_gate": {
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+    "sequence_p95_nll_delta": 0.05
+  },
+  "registered_unix": 1794202505.9768724
+}
@@ -0,0 +1,97 @@
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@@ -0,0 +1,46 @@
+{
+  "state": "completed",
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+  "peak_rss_kib": 83704
+}
@@ -0,0 +1,39 @@
+Looking in indexes: https://download.pytorch.org/whl/cpu
+Collecting torch
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+Collecting filelock (from torch)
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+  Downloading typing_extensions-4.16.0-py3-none-any.whl.metadata (3.3 kB)
+Collecting setuptools>=77.0.3 (from torch)
+  Downloading https://download.pytorch.org/whl/setuptools-78.1.0-py3-none-any.whl.metadata (6.6 kB)
+Collecting sympy>=1.13.3 (from torch)
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+Collecting networkx>=2.5.1 (from torch)
+  Downloading networkx-3.7-py3-none-any.whl.metadata (6.7 kB)
+Collecting jinja2 (from torch)
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+Collecting fsspec>=0.8.5 (from torch)
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+  Downloading mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
+Collecting MarkupSafe>=2.0 (from jinja2->torch)
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+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 209.5/209.5 MB 235.8 kB/s eta 0:00:00
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+Downloading networkx-3.7-py3-none-any.whl (2.1 MB)
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+Downloading sympy-1.14.0-py3-none-any.whl (6.3 MB)
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+Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 19.7 kB/s eta 0:00:00
+Downloading typing_extensions-4.16.0-py3-none-any.whl (45 kB)
+Downloading filelock-4.2.4-py3-none-any.whl (162 kB)
+Downloading https://download.pytorch.org/whl/jinja2-3.1.6-py3-none-any.whl (134 kB)
+Downloading https://download.pytorch.org/whl/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (23 kB)
+Installing collected packages: mpmath, typing-extensions, sympy, setuptools, networkx, MarkupSafe, fsspec, filelock, jinja2, torch
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+Successfully installed MarkupSafe-3.0.3 filelock-4.2.4 fsspec-2026.9.0 jinja2-3.1.6 mpmath-1.3.0 networkx-3.7 setuptools-78.1.0 sympy-1.14.0 torch-2.15.0+cpu typing-extensions-4.16.0
@@ -0,0 +1,25 @@
+Looking in indexes: https://download.pytorch.org/whl/cpu
+Collecting torch
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+Collecting filelock (from torch)
+  Downloading filelock-4.2.4-py3-none-any.whl.metadata (2.0 kB)
+Collecting typing-extensions>=4.13.0 (from torch)
+  Downloading typing_extensions-4.16.0-py3-none-any.whl.metadata (3.3 kB)
+Collecting setuptools>=77.0.3 (from torch)
+  Downloading https://download.pytorch.org/whl/setuptools-78.1.0-py3-none-any.whl.metadata (6.6 kB)
+Collecting sympy>=1.13.3 (from torch)
+  Downloading sympy-1.14.0-py3-none-any.whl.metadata (12 kB)
+Collecting networkx>=2.5.1 (from torch)
+  Downloading networkx-3.7-py3-none-any.whl.metadata (6.7 kB)
+Collecting jinja2 (from torch)
+  Downloading https://download.pytorch.org/whl/jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB)
+Collecting fsspec>=0.8.5 (from torch)
+  Downloading fsspec-2026.9.0-py3-none-any.whl.metadata (10 kB)
+Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch)
+  Downloading mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
+Collecting MarkupSafe>=2.0 (from jinja2->torch)
+  Downloading https://download.pytorch.org/whl/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.7 kB)
+Downloading https://download-r2.pytorch.org/whl/cpu/torch-2.15.0%2Bcpu-cp314-cp314-manylinux_2_28_x86_64.whl (209.5 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━             145.8/209.5 MB 447.0 kB/s eta 0:02:23
+ERROR: Could not install packages due to an OSError: [Errno 28] No space left on device
+
@@ -0,0 +1,34 @@
+{
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+}
@@ -0,0 +1,96 @@
+{
+    "architectures": [
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+    "model_type": "qwen3_5",
+    "text_config": {
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+            "linear_attention",
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+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
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@@ -0,0 +1,29 @@
+{
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+  "heldout_function_pass": true,
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+  "untouched_full_forward_bitwise_matches_baseline": true,
+  "learned_function_repetition_positive": true,
+  "bidirectional_network_quality_pass": true,
+  "claim_limit": "Existing learned RMSNorm module functions with identical eps and shape; uniform real-arithmetic relative bound and tiny real-text probes. Does not establish attention/MLP-body duplication or broad task capability.",
+  "elapsed_seconds": 369.7967556482181,
+  "peak_rss_kib": 71596
+}
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+post_attention_layernorm,15,17,0.29876160990712075,0.4260485651214128,0.4260485651214128,True,0.06801187992095947,0.09242258220911026,0.736328125,0.712890625
+post_attention_layernorm,15,18,0.30124223602484473,0.4311111111111111,0.4311111111111111,True,0.06801187992095947,0.09676424413919449,0.736328125,0.8095703125
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+post_attention_layernorm,15,20,0.33996683250414594,0.2537128712871287,0.33996683250414594,True,0.06801187992095947,0.07808392494916916,0.736328125,0.822265625
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+post_attention_layernorm,15,22,0.45278969957081544,0.31166912850812406,0.45278969957081544,True,0.06801187992095947,0.05012959614396095,0.736328125,0.9713134765625
+post_attention_layernorm,15,23,0.815378923352038,0.4491508152173913,0.815378923352038,True,0.06801187992095947,0.06521295011043549,0.736328125,1.01361083984375
+post_attention_layernorm,16,17,0.17602657004830918,0.1496790757381258,0.17602657004830918,True,0.08680811524391174,0.09242258220911026,0.72265625,0.712890625
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+post_attention_layernorm,16,19,0.4420289855072464,0.3065326633165829,0.4420289855072464,True,0.08680811524391174,0.07613449543714523,0.72265625,0.81640625
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+post_attention_layernorm,16,23,1.0506634499396863,0.5123529411764706,1.0506634499396863,True,0.08680811524391174,0.06521295011043549,0.72265625,1.01361083984375
+post_attention_layernorm,17,18,0.23767123287671232,0.19203099059214168,0.23767123287671232,True,0.09242258220911026,0.09676424413919449,0.712890625,0.8095703125
+post_attention_layernorm,17,19,0.29360306178239476,0.22696534234995774,0.29360306178239476,True,0.09242258220911026,0.07613449543714523,0.712890625,0.81640625
+post_attention_layernorm,17,20,0.20969441517386722,0.17334494773519163,0.20969441517386722,True,0.09242258220911026,0.07808392494916916,0.712890625,0.822265625
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+post_attention_layernorm,17,23,0.8589393110989612,0.46205882352941174,0.8589393110989612,True,0.09242258220911026,0.06521295011043549,0.712890625,1.01361083984375
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+post_attention_layernorm,20,23,0.5675426463808206,0.4219858156028369,0.5675426463808206,True,0.07808392494916916,0.06521295011043549,0.822265625,1.01361083984375
+post_attention_layernorm,21,22,0.2875,0.22330097087378642,0.2875,True,0.06570179015398026,0.05012959614396095,0.86328125,0.9713134765625
+post_attention_layernorm,21,23,0.3960546282245827,0.4148936170212766,0.4148936170212766,True,0.06570179015398026,0.06521295011043549,0.86328125,1.01361083984375
+post_attention_layernorm,22,23,0.3124108416547789,0.34263295553618134,0.34263295553618134,True,0.05012959614396095,0.06521295011043549,0.9713134765625,1.01361083984375
@@ -0,0 +1,29 @@
+{"preflight_layer": 0, "seconds": 2.0485799312591553}
+{"preflight_layer": 1, "seconds": 4.110389947891235}
+{"preflight_layer": 2, "seconds": 5.677527189254761}
+{"preflight_layer": 3, "seconds": 6.754502058029175}
+{"preflight_layer": 4, "seconds": 8.340956926345825}
+{"preflight_layer": 5, "seconds": 10.697716236114502}
+{"preflight_layer": 6, "seconds": 12.576658964157104}
+{"preflight_layer": 7, "seconds": 14.14732575416565}
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/opt/exp-model-parameter-similarity/functional/preflight.py", line 27, in <module>
+    if __name__=='__main__':main()
+                            ~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/preflight.py", line 18, in main
+    h,_=lm.forward(ids[:1,:8],progress=progress)
+        ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 215, in forward
+    h = h + self.mlp(n,donor)
+            ~~~~~~~~^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 149, in mlp
+    gate = silu(self.linear(h, p+'gate_proj'))
+                ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 127, in linear
+    self.guard()
+    ~~~~~~~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 108, in guard
+    raise MemoryError('Sustained host swap IO above 4 MiB/s in three observation windows')
+MemoryError: Sustained host swap IO above 4 MiB/s in three observation windows
@@ -0,0 +1,61 @@
+{
+  "passed": false,
+  "sample_id": "cal-zh-1",
+  "tokens": 8,
+  "error_type": "MemoryError",
+  "error": "Sustained host swap pressure plus constrained memory: available=1071348 KiB, process_swap=23640 KiB",
+  "elapsed_seconds": 15.565188646316528,
+  "peak_rss_kib": 162324,
+  "resource_snapshots": [
+    {
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+      "seconds": 3.669800043106079,
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+      "process_swap_kib": 0,
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+    },
+    {
+      "layer": 1,
+      "seconds": 6.548107862472534,
+      "rss_kib": 117440,
+      "process_swap_kib": 0,
+      "available_kib": 984012
+    },
+    {
+      "layer": 2,
+      "seconds": 9.072005033493042,
+      "rss_kib": 115956,
+      "process_swap_kib": 0,
+      "available_kib": 925720
+    },
+    {
+      "layer": 3,
+      "seconds": 10.377433776855469,
+      "rss_kib": 114984,
+      "process_swap_kib": 0,
+      "available_kib": 958016
+    },
+    {
+      "layer": 4,
+      "seconds": 11.7694993019104,
+      "rss_kib": 108236,
+      "process_swap_kib": 0,
+      "available_kib": 1055012
+    },
+    {
+      "layer": 5,
+      "seconds": 13.289605379104614,
+      "rss_kib": 108244,
+      "process_swap_kib": 0,
+      "available_kib": 1056400
+    },
+    {
+      "layer": 6,
+      "seconds": 15.41482949256897,
+      "rss_kib": 84512,
+      "process_swap_kib": 23736,
+      "available_kib": 1072308
+    }
+  ],
+  "limitation": "Real-weight capacity check, not an independent correctness oracle or quality benchmark"
+}
@@ -0,0 +1,22 @@
+{"layer": 0, "seconds": 3.669800043106079, "rss_kib": 117576, "process_swap_kib": 0, "available_kib": 975920}
+{"layer": 1, "seconds": 6.548107862472534, "rss_kib": 117440, "process_swap_kib": 0, "available_kib": 984012}
+{"layer": 2, "seconds": 9.072005033493042, "rss_kib": 115956, "process_swap_kib": 0, "available_kib": 925720}
+{"layer": 3, "seconds": 10.377433776855469, "rss_kib": 114984, "process_swap_kib": 0, "available_kib": 958016}
+{"layer": 4, "seconds": 11.7694993019104, "rss_kib": 108236, "process_swap_kib": 0, "available_kib": 1055012}
+{"layer": 5, "seconds": 13.289605379104614, "rss_kib": 108244, "process_swap_kib": 0, "available_kib": 1056400}
+{"layer": 6, "seconds": 15.41482949256897, "rss_kib": 84512, "process_swap_kib": 23736, "available_kib": 1072308}
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/opt/exp-model-parameter-similarity/functional/preflight.py", line 39, in <module>
+    if __name__=='__main__':main()
+                            ~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/preflight.py", line 26, in main
+    h,_=lm.forward(ids[:1,:8],progress=progress)
+        ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 201, in forward
+    self.guard()
+    ~~~~~~~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 109, in guard
+    raise MemoryError(f'Sustained host swap pressure plus constrained memory: available={available} KiB, process_swap={own_swap_kib} KiB')
+MemoryError: Sustained host swap pressure plus constrained memory: available=1071348 KiB, process_swap=23640 KiB
@@ -0,0 +1,190 @@
+{
+  "passed": true,
+  "sample_id": "cal-zh-1",
+  "tokens": 8,
+  "mean_nll": 4.089094978395148,
+  "token_nll": [
+    [
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+      12.836286295347621,
+      6.470462449543003,
+      4.530625316962006,
+      3.1924382665907807,
+      0.703601432631153,
+      0.11264770899465404
+    ]
+  ],
+  "elapsed_seconds": 47.741777658462524,
+  "peak_rss_kib": 162276,
+  "resource_snapshots": [
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+  "limitation": "Real-weight capacity check, not an independent correctness oracle or quality benchmark"
+}
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+{"layer": 0, "seconds": 1.4413530826568604, "rss_kib": 48904, "process_swap_kib": 0, "available_kib": 930888}
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+{"layer": 14, "seconds": 24.617902040481567, "rss_kib": 24144, "process_swap_kib": 13456, "available_kib": 1039196}
+{"layer": 15, "seconds": 25.96587586402893, "rss_kib": 24068, "process_swap_kib": 13448, "available_kib": 1032608}
+{"layer": 16, "seconds": 27.733217239379883, "rss_kib": 24056, "process_swap_kib": 13432, "available_kib": 1015000}
+{"layer": 17, "seconds": 29.303704977035522, "rss_kib": 20852, "process_swap_kib": 16632, "available_kib": 1041560}
+{"layer": 18, "seconds": 30.38013005256653, "rss_kib": 20876, "process_swap_kib": 16616, "available_kib": 1061004}
+{"layer": 19, "seconds": 31.271567583084106, "rss_kib": 20896, "process_swap_kib": 16596, "available_kib": 1059348}
+{"layer": 20, "seconds": 32.31741189956665, "rss_kib": 20936, "process_swap_kib": 16564, "available_kib": 1078812}
+{"layer": 21, "seconds": 33.8273024559021, "rss_kib": 20396, "process_swap_kib": 17044, "available_kib": 1088472}
+{"layer": 22, "seconds": 35.43067383766174, "rss_kib": 20436, "process_swap_kib": 17012, "available_kib": 1109268}
+{"layer": 23, "seconds": 36.55965542793274, "rss_kib": 20448, "process_swap_kib": 17000, "available_kib": 1109588}
@@ -0,0 +1,15 @@
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/opt/exp-model-parameter-similarity/functional/reference_check.py", line 142, in <module>
+    if __name__=='__main__':main()
+                            ~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/reference_check.py", line 87, in main
+    rotary=ns['Qwen3_5TextRotaryEmbedding'](config)
+  File "pinned_transformers_reference", line 146, in __init__
+  File "pinned_transformers_reference", line 165, in compute_default_rope_parameters
+  File "/opt/exp-model-parameter-similarity/venv/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1976, in __getattr__
+    raise AttributeError(
+        f"'{type(self).__name__}' object has no attribute '{name}'"
+    )
+AttributeError: 'Qwen3_5TextRotaryEmbedding' object has no attribute 'rope_parameters'. Did you mean: '_parameters'?
@@ -0,0 +1,15 @@
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/opt/exp-model-parameter-similarity/functional/reference_check.py", line 142, in <module>
+    if __name__=='__main__':main()
+                            ~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/reference_check.py", line 102, in main
+    tout=layer.linear_attn(tn)
+  File "/opt/exp-model-parameter-similarity/venv/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1787, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/venv/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1798, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "pinned_transformers_reference", line 542, in forward
+NameError: name 'apply_mask_to_padding_states' is not defined
@@ -0,0 +1,288 @@
+{
+  "passed": true,
+  "reference_commit": "fd3cf0140de03f7619ac95b5cfed05b711ff9e22",
+  "source_sha256": "4463b8a108f678c166755101d95f188179f54143305953d78147de0389b80ebc",
+  "torch": "2.15.0+cpu",
+  "numpy": "2.5.4",
+  "implementation_sha256": "48a2a97d0445dc6e08ac7f27a935637a7553417d0982eb94122d6cb02bbb499c",
+  "tests": [
+    {
+      "test": "layer0_rmsnorm",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 2.384185791015625e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer0_mixer",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 1.862645149230957e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer0_mlp",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 2.9802322387695312e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer0_complete",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 2.384185791015625e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer1_rmsnorm",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 2.384185791015625e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "attention_probabilities",
+      "shape": [
+        2,
+        2,
+        7,
+        7
+      ],
+      "max_abs_error": 1.1920928955078125e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer1_mixer",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 5.21540641784668e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer1_mlp",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 4.470348358154297e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "layer1_complete",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 2.384185791015625e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_recurrent_gated_delta_rule_length1_output",
+      "shape": [
+        2,
+        1,
+        2,
+        5
+      ],
+      "max_abs_error": 4.470348358154297e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_recurrent_gated_delta_rule_length1_state",
+      "shape": [
+        2,
+        2,
+        4,
+        5
+      ],
+      "max_abs_error": 5.960464477539063e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_chunk_gated_delta_rule_length1_output",
+      "shape": [
+        2,
+        1,
+        2,
+        5
+      ],
+      "max_abs_error": 2.9802322387695312e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_chunk_gated_delta_rule_length1_state",
+      "shape": [
+        2,
+        2,
+        4,
+        5
+      ],
+      "max_abs_error": 5.960464477539063e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_recurrent_gated_delta_rule_length7_output",
+      "shape": [
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+        2,
+        5
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+      "max_abs_error": 5.960464477539063e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_recurrent_gated_delta_rule_length7_state",
+      "shape": [
+        2,
+        2,
+        4,
+        5
+      ],
+      "max_abs_error": 5.960464477539063e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_chunk_gated_delta_rule_length7_output",
+      "shape": [
+        2,
+        7,
+        2,
+        5
+      ],
+      "max_abs_error": 7.450580596923828e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_chunk_gated_delta_rule_length7_state",
+      "shape": [
+        2,
+        2,
+        4,
+        5
+      ],
+      "max_abs_error": 1.7881393432617188e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_recurrent_gated_delta_rule_length33_output",
+      "shape": [
+        2,
+        33,
+        2,
+        5
+      ],
+      "max_abs_error": 1.1920928955078125e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_recurrent_gated_delta_rule_length33_state",
+      "shape": [
+        2,
+        2,
+        4,
+        5
+      ],
+      "max_abs_error": 2.9802322387695312e-08,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_chunk_gated_delta_rule_length33_output",
+      "shape": [
+        2,
+        33,
+        2,
+        5
+      ],
+      "max_abs_error": 5.364418029785156e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "torch_chunk_gated_delta_rule_length33_state",
+      "shape": [
+        2,
+        2,
+        4,
+        5
+      ],
+      "max_abs_error": 3.5762786865234375e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
+      "test": "complete_two_layer_text_forward",
+      "shape": [
+        2,
+        7,
+        16
+      ],
+      "max_abs_error": 3.5762786865234375e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    }
+  ],
+  "limitations": "FP32 CPU semantics on tiny synthetic configurations; not full-checkpoint BF16 kernel equivalence"
+}
@@ -0,0 +1 @@
+{"passed": true, "checks": 22, "maximum_absolute_discrepancy": 5.364418029785156e-07}
@@ -0,0 +1,5 @@
+{
+  "weights": "retain_until_explicit_user_delete_request",
+  "automatic_cleanup": false,
+  "instruction_date": "2026-11-09"
+}
@@ -0,0 +1,342 @@
+{
+  "revision": "15852e8c16360a2fea060d615a32b45270f8a8fc",
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+      "text": "A small library opens every morning beside the river. The librarian checks returned books, repairs damaged covers, and places new arrivals on a wooden table. Visitors often stop to read before continuing their journey across the old bridge.",
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+      "split": "calibration",
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+      "text": "To compare two measurements, first record their units and the conditions under which they were collected. Compute the difference, inspect unusual observations, and repeat the procedure with independent examples. A short report should separate observations from explanations and unresolved questions.",
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+    {
+      "id": "test-zh-1",
+      "split": "heldout",
+      "language": "zh",
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+      ]
+    },
+    {
+      "id": "test-en-2",
+      "split": "heldout",
+      "language": "en",
+      "text": "When a program reads a table, it should check column names and handle missing entries explicitly. The developer tested a small example, compared the output with a hand calculation, and saved the input alongside the result so another person could reproduce the check.",
+      "text_sha256": "6c47a00b9b81ae364d9cedb79f7ba46fbb104fe90ce35b6e742e45c6b106ae9a",
+      "full_token_count": 48,
+      "token_ids": [
+        4350,
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+      ]
+    }
+  ]
+}
@@ -0,0 +1,4 @@
+{
+  "selected": [],
+  "rule": "calibration pass; descending component bytes, ascending maximum symmetric error; maximum one candidate"
+}
@@ -0,0 +1,4 @@
+{
+  "measured": false,
+  "reason": "No calibration candidate selected"
+}
@@ -0,0 +1,8 @@
+Traceback (most recent call last):
+  File "/opt/exp-model-parameter-similarity/functional/supervise.py", line 73, in main
+    execute('functional.preflight',['--root',str(root)],1200,'preflight.log')
+    ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/supervise.py", line 38, in execute
+    if r.returncode:raise RuntimeError(f'{module} failed: exit {r.returncode}, see {log}')
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+RuntimeError: functional.preflight failed: exit 1, see preflight.log
@@ -0,0 +1,8 @@
+Traceback (most recent call last):
+  File "/opt/exp-model-parameter-similarity/functional/supervise.py", line 45, in main
+    execute('functional.reference_check',['--source',str(root/'reference.py'),'--output',str(root/'reference-check.json')],300,'reference-check.log')
+    ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/supervise.py", line 27, in execute
+    if r.returncode:raise RuntimeError(f'{module} failed: exit {r.returncode}, see {log}')
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+RuntimeError: functional.reference_check failed: exit 1, see reference-check.log
@@ -0,0 +1,8 @@
+{
+  "state": "stopped",
+  "phase": "reference_check",
+  "weight_retention": "until_explicit_user_delete_request",
+  "supervisor_elapsed_seconds": 309.1346261948347,
+  "error_type": "RuntimeError",
+  "error": "functional.reference_check failed: exit 1, see reference-check.log"
+}
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				`{"passed": true, "checks": 22, "maximum_absolute_discrepancy": 5.364418029785156e-07}`