diff --git a/.gitattributes b/.gitattributes
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+# Scientific artifacts must retain the bytes recorded in checksums.json.
+results/** -text
+# Generated CSV uses RFC 4180 line endings; Matplotlib SVG paths contain spaces.
+results/**/*.csv -whitespace
+results/**/*.svg -whitespace
diff --git a/.gitignore b/.gitignore
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+__pycache__/
+.venv/
+weights/
+*.partial
+*.log
diff --git a/README.md b/README.md
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+# 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. No model weights are committed here.
+
+- [Training-free functional reuse plan (proposed; not executed)](docs/TRAINING_FREE_FUNCTIONAL_PLAN.md)
+- [Primary-source literature and applicability](docs/FUNCTIONAL_REUSE_LITERATURE.md)
+- [Procedure and limits](docs/SOP.md)
+- [2B pilot findings and limitations](docs/PILOT_FINDINGS.md)
+- [Raw result report, data and figures](results/qwen35-2b/REPORT.md)
+- `pilot/download.py`: revision-pinned official checkpoint download and verification.
+- `pilot/tensors.py`: memory-mapped safetensors/BF16 reader and metrics.
+- `pilot/analyze.py`: bounded tensor/block search, exact verification, representative groups.
+- `pilot/plot.py`: scientific PNG/PDF/SVG figures and report from saved data.
+- `pilot/validate.py`: result/group validation, checksums and guarded weight cleanup.
+- `pilot/audit.py`: finite-metric checks, test evidence and archive provenance.
+- `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.
+
+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.
diff --git a/docs/FUNCTIONAL_REUSE_LITERATURE.md b/docs/FUNCTIONAL_REUSE_LITERATURE.md
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+# 功能复用文献核对
+
+核对日期：2026-11-09。以下为原始论文摘要及相关章节核对，不是复现实验。
+本项目约束：不训练任何参数；已有静态试验没有执行模型前向。下面的方法不自动获得执行授权。
+
+| 原始来源 | 研究的共享对象 / 证据 | 训练条件与适用限制 | 对本项目的用途 |
+|---|---|---|---|
+| [LISA, 2408.01890v2](https://arxiv.org/html/2408.01890v2), §3–5 | 跨层 token 注意力分布；考察头对齐、浅层敏感性；基准能力和吞吐验证 | 学习头对齐网络及低秩差异修正；不能归类为免训练替换。论文的 attention weights 在此主要指注意力分布，不是 W_Q/W_K 权重 | 借鉴逐头比较、首 token 对照和替换敏感性验证；不采用训练模块 |
+| [CLA, 2405.12981](https://arxiv.org/abs/2405.12981) | 相邻层共享 K/V heads，降低 KV cache；语言模型能力验证 | 摘要明确实验为从头训练 1B/3B 模型；不是现成 Qwen 的直接合并证据 | 区分 KV 缓存收益和参数/计算收益；作为架构可行性依据 |
+| [Systematic Study, NAACL 2025](https://aclanthology.org/2025.naacl-short.34/) | 系统比较跨层 KV 共享配置；语言建模、下游任务及生成吞吐 | 摘要报告激进配置涉及额外训练及 prefill 代价；不能由摘要推断零训练可迁移性 | 复用方向、共享拓扑及 prefill/decode 成本须分别记录 |
+| [MiniLM, 2002.10957v2](https://arxiv.org/abs/2002.10957) | 注意力分布及 value-value 关系蒸馏 | 明确训练学生模型；本项目不进行蒸馏 | 提醒检索分布和 value 内容需要分开考察 |
+| [CKA, ICML 2019](https://proceedings.mlr.press/v97/kornblith19a.html) | 表征之间的关系相似性，不是模块替换实验 | 论文讨论表征比较及可逆变换不变性的局限；高 CKA 不是可替换保证 | 可作为候选诊断，不能取代输出误差、干预和成本评测 |
+| [ALBERT, 1909.11942v6](https://arxiv.org/html/1909.11942v6), §3.1/4.5 | 跨层参数共享及训练后能力验证 | 共享架构参与训练；不能直接外推为已训练 checkpoint 可无损合并 | 参数存储减少与前向运算减少分别记账 |
+
+## 对当前试验的推论
+
+以上文献给出研究动机，没有证明 Qwen3.5-2B 已存在免训练的有效复用组。
+静态权重试验也没有否定该可能性。应使用同输入功能探测筛选，再对实际计算图实施单处干预。
+Qwen 的 full attention 与线性注意力需分别定义探测量；不能把 softmax attention 的结论照搬到状态递推模块。
+
+数学工具参考：[SciPy orthogonal_procrustes](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.orthogonal_procrustes.html)。
+纯正交最小二乘有直接的矩阵分解解法，无须枚举旋转矩阵；但求解一个对齐并不自动带来可省略的计算。
+本项目下一阶段默认不求 R，直接测目标函数和干预效果。
diff --git a/docs/PILOT_FINDINGS.md b/docs/PILOT_FINDINGS.md
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+# Qwen3.5-2B 有界试验结果
+
+本轮已完成约定时间预算内的试验。整 tensor 精确比较 2,000 对；子块在内部截止时
+完成 14,778 / 20,000 对（73.89%），其余 5,222 对未计算。不能把本轮写成全量搜索。
+
+结论：在本次候选、坐标对齐及误差阈值范围内，没有获得显著的直接权重共享收益。
+这不等于证明该模型不能通过其他方法压缩，也不代表 MoE 模型具有同样结果。
+
+## 已确认的试验范围
+
+- 模型：官方 `Qwen/Qwen3.5-2B`，revision `15852e8c16360a2fea060d615a32b45270f8a8fc`。
+- 原始 safetensors 文件为 4,548,221,488 字节，已核对官方 LFS SHA256。
+- 共 632 个存储 tensor；权重数据本身为 4,548,144,832 字节。
+- 实际存储参数 2,274,069,824 个，包含官方 checkpoint 中的视觉部分。
+- 维数分布：319 个一维、294 个二维、18 个三维、1 个五维。
+- 三维权重是 `[6144, 1, 4]` 的卷积核，包含长度为 1 的轴；不能把它解释为一般三维体积。
+- 五维视觉权重纳入完整清单和全模型字节分母，本轮不扩展五维子块搜索。
+- 不执行前向计算、能力评测、训练或蒸馏。
+
+## 整 tensor 的结果
+
+已完成 2,000 对同角色、同形状候选的精确比较。它们是经过近邻筛选的候选，
+不是所有可能 tensor 对的穷举。
+
+| 最大直接替换相对误差 | 非单成员共享组 | 最大组大小 | 净节省字节 | 占模型权重 |
+|---|---:|---:|---:|---:|
+| 1% | 0 | 1 | 0 | 0% |
+| 2% | 0 | 1 | 0 | 0% |
+| 5% | 1 | 2 | 0 | 0% |
+| 10% | 11 | 6 | 32,704 | 0.000719% |
+| 20% | 16 | 15 | 119,424 | 0.002626% |
+
+20% 阈值下找到的共享组均为一维权重。组数增加不代表显著的字节收益。
+这里的百分比是权重误差，绝不是任务能力损失百分比。
+
+净节省按本轮声明的理论布局计算：保留组代表，每个分析对象 16 字节映射，
+每个共享组 64 字节描述；若大于原存储，则保持原表示。
+
+这部分结果不支持在本次搜索范围内通过直接共享整 tensor 显著压缩该 checkpoint。
+它不排除其他未比较组合、子块共享、重参数化或带残差的压缩方法。
+
+## 子块结果
+
+共筛选 13,328 个抽样子块。20% 直接替换误差阈值下：
+
+这里的 tensor 指完整命名权重数组，block 指其局部子张量，不是 Transformer block。
+三维原始权重为 6144×1×4，本轮切片实际是 1×1×4（4 个数），局部块是
+4×1×4（16 个数）；小块的高余弦不能推广为整份三维权重相似。
+
+| 表示 | 非单成员共享组 | 最大组 | 映射开销前节省 | 当前布局净节省 |
+|---|---:|---:|---:|---:|
+| 64×64 二维块 | 3 | 3 | 40,960 字节 | 0 |
+| 128×128 二维块 | 1 | 2 | 32,768 字节 | 0 |
+| 三维 axis-0 切片 | 45 | 12 | 872 字节 | 0 |
+| 三维局部块 | 2 | 2 | 64 字节 | 0 |
+
+二维共享候选均来自 `model.visual.pos_embed.weight`，未在已复核候选中形成大规模
+投影矩阵共享。不同块尺度是替代方案，不能相加。
+
+净收益为零与声明的映射成本有关，不应解读为完全没有相似子块。更稀疏或更紧凑的
+索引格式可能降低开销，但本轮找到的二维毛节省也仅为几十 KB，尚不足以支持大幅
+减容。三维切片组数量较多，但整组三维权重合计只占模型字节的约 0.01945%。
+
+模型字节占比：一维 0.01972%、二维 99.89167%、三维 0.01945%、五维 0.06917%。
+因此，应按字节贡献评估价值，不能按 tensor 数量或共享组数量推断收益。
+
+全部分组是满足代表半径约束的可行方案，不保证达到最少组数或最优压缩率。
+
+## 运行经验
+
+1. 实际服务器内存约 1.7 GiB，新增的 60 GB 是磁盘扩容，不是内存。
+   使用分块 BF16 解码和 mmap 页面释放完成了完整权重扫描与整 tensor 比较。
+2. 只看进程 RSS 不足以解释运行成本。`vmstat` 快照观察到 75%～85% 的 I/O 等待，
+   同时存在交换活动；这些是共享主机的快照，不能全部归因于本进程。
+3. 与权重读取并行的绘图合成测试触发过 300 秒超时；观察到测试处于磁盘等待。
+   后续验证应在权重读取结束后串行执行，避免把资源争用误判为数值算法失败。
+4. Matplotlib 图例改为固定位置，避免自动定位引入额外布局开销。
+5. 公平地选择各角色的候选，与高效地安排读取顺序，是两个不同问题。
+   下一轮应评估“先固定公平候选集合，再按文件/源 tensor 分批执行”的效果，
+   比较是否得到相同指标及更低 I/O 开销；本轮不在中途改变候选集合。
+6. 从已保存元数据计算，13,328 个抽样子块的原始数据合计 256,387,072 字节
+   （约 244.5 MiB），对应指纹约 100.1 MiB。这是下一轮原始子块缓存的容量估计，
+   不是本轮已经实现的缓存或实测加速。应先用小样本验证数值一致性、峰值内存和
+   I/O 改善，再决定是否作为默认方案；不能仅凭容量估计保证运行效率。
+
+参数分析及分组汇总耗时 6,661.13 秒（约 111.0 分钟），分析进程峰值 RSS 约
+201.3 MiB；这不包含系统文件缓存和同机服务内存。内部比较截止后保存覆盖率与
+分组，整个参数分析未触及 7,200 秒进程硬上限。块比较阶段将本进程 IO 优先级
+调整为 best-effort 7，未改变服务器全局设置。
+
+最终修图版 6 项串行合成测试通过（约 77.2 秒），六类图表的 PNG/PDF/SVG 已生成，
+并对全部六类 PNG 做视觉检查；程序还核验了分组成员与代表之间的精确误差边。
+
+## 三张诊断图的修订与解读
+
+- 热力图只展示 `linear_attn.in_proj_a.weight`（16×2048），不是整个层。18 个相关层
+  的 153 种配对中核验了 74 对；已测非对角余弦约 0.011～0.298。
+  对角线 1 是身份比较，不是跨层发现。未测格子改用斜线，避免与接近零的底色混淆。
+- 原混合层距曲线受维数组成影响：层距 1 含 183 对一维、19 对二维；层距 8
+  含 52 对一维、75 对二维。一维余弦中位数约从 0.941 变为 0.883，
+  不能将混合曲线的陡降全部归因于层距。修订版按维数/块尺度分开，模块和筛选偏差仍存在。
+- 随机对照分为保留分布的元素打乱、匹配范数的零均值高斯向量、真实同角色随机配对。
+  元素打乱后仍高余弦的情况主要出现在一维归一化、偏置和 A_log，提示均值/分布影响。
+  修订版按维数分面、按密度归一化，并显示样本数，避免将不同样本量的峰高当概率比较。
+  它们是诊断对照，不是显著性检验，也没有完全匹配每个模块的采样构成。
+  三维对照分层仅有约 3～6 个样本，尤其不能据其峰形作总体推断。
+
+这次只从已有 CSV/JSON 重绘并导出诊断表，模型权重已删除，没有增加参数比较或重新运行模型。
+
+## 限制及后续决策
+
+- 只研究一个 checkpoint；近邻筛选、同角色分组和子块抽样均可能漏掉其他共享机会。
+- 直方图和层距曲线存在候选筛选与模块混合偏差，不能用作总体分布或独立样本推断。
+- 光谱/子空间分析仅使用有界子体积；分布或子空间相似不等于可直接替换。
+- 本轮没有测量能力损失、困惑度、运行内存或推理速度，也没有产出可运行的压缩模型。
+- 大幅减少推理计算量需要进一步的计算图和路由分析，单纯共享存储不能保证加速。
+
+下一轮先讨论候选选择与执行局部性、原始子块缓存、映射格式和阶段预算，
+再修订 SOP；不自动下载第二个模型。只有获得额外授权后，才进行校准前向及
+留出任务测试，以验证替换造成的能力损失。
+
+数据与标准化报告见 `../results/qwen35-2b/`；具体阈值、分组、覆盖率、来源和校验记录
+随原始图表数据归档。权重在交付副本校验后按授权删除，重现实验需要按固定 revision
+重新下载。
+
+## 功能复用假设仍待验证
+
+本轮坐标对齐权重结果不能排除 Q/K 的联合功能接近或 MLP 分支可复用。
+下一阶段采用同一隐藏输入下的函数比较，不训练参数、不默认求正交 R；
+注意力分布、完整分支输出、真实网络单处替换和运行收益分别验证。
+详见 [免训练有界方案](TRAINING_FREE_FUNCTIONAL_PLAN.md) 与
+[文献核对](FUNCTIONAL_REUSE_LITERATURE.md)。这些是方案，尚未执行。
diff --git a/docs/SOP.md b/docs/SOP.md
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+# Parameter similarity SOP — 2B pilot draft
+
+Status: v0.1, executed for the bounded Qwen3.5-2B pilot. See [findings](PILOT_FINDINGS.md).
+Changes proposed from this experience require a small equivalence/performance check before
+the next model; no second model is started automatically.
+
+## Scope and decision
+
+Analyze official, unquantized safetensors without instantiating a model, running inference,
+training, or executing repository-supplied code. Start with Qwen/Qwen3.5-2B only.
+The question is the number of representative-centered sharing groups under direct weight
+replacement error limits, and the net byte savings of a theoretical shared representation.
+Weight error is not task-accuracy loss, and parameter sharing does not imply fewer FLOPs.
+
+## Prerequisites
+
+1. Confirm authorized remote host, free disk, memory, CPU, existing work, and access.
+2. Install prebuilt wheels only. Never compile locally or silently use another runner.
+3. Freeze the official repository revision and download file manifest including SHA256.
+4. Allocate checkpoint size plus 3 GB disk margin. Use an experiment-owned directory,
+   not a shared Hugging Face cache. Never delete pre-existing weights.
+
+## Bounded procedure
+
+1. Run synthetic correctness tests on the remote host: BF16, 3D indexing, directional
+   replacement error, and non-transitive groups.
+2. Download one checkpoint sequentially and verify upstream sizes and LFS SHA256.
+3. Scan each tensor in 262,144-element chunks. Record shape, dtype, axis role,
+   layer, bytes, moments, max magnitude, and finite-value checks. Memory-map weights;
+   evict scanned pages. Do not search for exactly equal weight tensors.
+4. Whole-tensor screening: same role, same shape, same dtype. Sample 2,048 aligned,
+   evenly spaced elements. Select up to 10 neighbors by each of cosine and relative
+   error. Fairly distribute the 2,000-pair exact-verification cap across role groups.
+5. Block screening: non-overlapping 64x64 and 128x128 2D partitions. For 3D tensors,
+   use axis-0 slices and bounded volumes adapted to actual dimensions. Keep up to
+   256 uniformly reservoir-sampled blocks per role/shape/scale for the 1.7 GiB pilot host
+   (below the planned maximum of 512). Record edge bytes
+   and sampling coverage. Verify at most 20,000 block pairs. Never extrapolate sampled
+   discoveries to unseen blocks or add savings across alternative scales.
+6. Exact metrics: full-element cosine, Pearson, directional replacement errors,
+   symmetric error, optimal-scalar residual. Tail diagnostics use samples and say so.
+   Controls include random actual comparable pairs, shuffled sampled elements,
+   and norm-matched Gaussian vectors. Save sampled versus exact error discrepancies.
+   Bounded mode-unfolding SVD probes are diagnostic only, not a replacement test.
+7. Form deterministic star groups at 1%, 2%, 5%, 10%, 20% relative weight error.
+   Every member requires an exact verified edge to its representative. Unknown
+   pairs remain independent; never use graph connected components as sharing groups.
+8. Compute original bytes minus representatives, 16-byte mapping per analyzed object,
+   and 64-byte descriptor per shared group. Fall back to unchanged storage if larger.
+   Count savings against the entire model, preserving unsampled blocks and edge regions.
+9. Save raw data, groups, thresholds, coverage, timings, resource usage, figures
+   (PNG/PDF/SVG), model metadata and report. Validate every group against exact edges.
+10. Copy results to the deliverable workspace and verify checksums before deleting
+    the experiment-owned checkpoint. Save a cleanup record. Do not delete the venv,
+    result data or any unrelated cache.
+11. Review full diff, commit and push a work branch with Codex Bot identity, then
+    create a PR for manual merge. Never silently initialize/push main or auto-merge.
+
+## Budget and stop conditions
+
+- Real 2B analysis computation: hard wall-time limit 7,200 seconds. Reserve final time
+  for summaries; download and report-generation time are recorded separately.
+- Use one BLAS thread to avoid interference with existing services.
+- Save exact-pair tables every 100 pairs; stage deadlines prevent whole-tensor work
+  from consuming the complete block budget.
+- On timeout or failure, retain source weights until partial results are recoverable
+  and report coverage/failure truthfully. An internal planned cutoff may produce a complete
+  bounded report with partial candidate coverage; always state verified/selected counts.
+  An external kill or missing required report is an incomplete run, not a completed experiment.
+- Empty or negligible low-error sharing is a valid negative result. Do not add scales,
+  increase error thresholds, align permutations, train or expand to other models automatically.
+- No runtime capability/performance claim without separately authorized evaluation.
+
+## Commands (remote, from project root)
+
+```sh
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 timeout 300 venv/bin/python -B -m unittest discover -s tests -v
+venv/bin/python -B -m pilot.download --root runs/qwen35-2b
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 timeout 7200 venv/bin/python -B -m pilot.analyze --root runs/qwen35-2b --seconds 6600
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m pilot.plot --root runs/qwen35-2b
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 timeout 300 venv/bin/python -B -m unittest discover -s tests -v > runs/qwen35-2b/tests.log 2>&1
+venv/bin/python -B -m pilot.audit --root runs/qwen35-2b --analysis-code-commit 6121016
+venv/bin/python -B -m pilot.validate --root runs/qwen35-2b
+# Copy archived results, verify checksums at the destination, then:
+venv/bin/python -B -m pilot.validate --root runs/qwen35-2b --cleanup-weights
+```
+
+## Follow-up decisions after the pilot
+
+- The pilot verified 2,000 tensor pairs and 14,778/20,000 selected block pairs before cutoff.
+  It took about 111 minutes; the final six-test suite including revised figure checks passed in about 77 seconds.
+- Hatch missing heatmap cells and label identity values as constructed. Separate layer-distance
+  curves by dimension/scale; retain warnings about within-stratum module/selection bias.
+  Normalize control histograms, display sample counts, and verify their areas equal one.
+  Keep these diagnostics distinct from task-equivalence or statistical-significance claims.
+- Avoid concurrent plotting tests and heavy weight IO on the shared host. Record IO wait,
+  swap, CPU and RSS; a low process RSS is not proof of low total resource cost.
+- Distinguish tensor/group counts from byte contributions: 1D tensors were about half the
+  inventory count but only 0.0197% of weight bytes in this checkpoint.
+- Keep fair candidate selection separate from IO-local execution order. Benchmark batching
+  by source file/tensor without changing the selected pairs or metrics.
+- The sampled raw blocks total about 244.5 MiB. Test an explicitly bounded raw BF16/F32
+  cache against the uncached implementation before adopting it; include fingerprint and
+  temporary conversion memory in the budget. This was not enabled in the current run.
+- Report both pre-metadata and net savings. Tiny blocks can have groups yet lose all savings
+  to mapping overhead. Any alternative sparse encoding needs its own explicit accounting.
+- Separate low-byte normalization groups from matrix/expert compression opportunities.
+- Decide whether approximate-neighbor recall and block coverage justify another bounded pass.
+- If meaningful candidates exist, request a small calibration-forward experiment to measure
+  output distortion, then held-out loss/tasks, then implementation-specific memory and latency.
+- For MoE, analyze router behavior and joint gate/up/down expert consistency before treating
+  independently similar matrix blocks as interchangeable experts.
+- Use these observations to revise this SOP before downloading a second model.
+
+## Training-free functional follow-up (design only)
+
+Direct coordinate-aligned parameter similarity is a baseline, not a test of Q/K/V or MLP
+functional interchangeability. The follow-up uses same-input module probes without solving
+rotations or training any parameters, then single-site intervention and held-out quality/cost
+validation. See the [bounded plan](TRAINING_FREE_FUNCTIONAL_PLAN.md) and
+[primary-source literature](FUNCTIONAL_REUSE_LITERATURE.md). No forward pass, new download,
+or new model is started by adopting this document. The original static pilot remains
+inference-free; the proposed follow-up requires a separately confirmed execution environment.
diff --git a/docs/TRAINING_FREE_FUNCTIONAL_PLAN.md b/docs/TRAINING_FREE_FUNCTIONAL_PLAN.md
new file mode 100644
index 0000000..3377f83
--- /dev/null
+++ b/docs/TRAINING_FREE_FUNCTIONAL_PLAN.md
@@ -0,0 +1,119 @@
+# 免训练功能复用：下一阶段有界方案
+
+状态：研究设计，尚未执行。原始参数试验已完成；没有新增前向、下载或训练。
+目标：寻找并验证至少一个在留出数据上保持约定质量、同时降低明确运行成本的复用实例；
+若没有通过者，报告有界搜索的阴性结果，不保证一定能找到。
+
+## 1. 硬约束与研究对象
+
+- 冻结所有模型参数。不进行优化器更新、微调、蒸馏、LoRA、学习投影或低秩修正。
+- 默认不求正交 R。校准集只用于选择有限候选和阈值，留出集不参与选择。
+- 同类型模块配对；Q/K/V 联合作为功能系统，MLP 独立验证。保留归一化、位置处理、门控及头结构。
+- 已存配置为 24 个文本层，其中 6 个 full attention、18 个线性注意力；执行前按固定 revision 的实际实现复核。
+  full attention 的 15 个无序层对可穷举；线性注意力首轮只检查 6 个预先固定的相邻同类型层对。
+  MLP 首轮检查 6 个预先固定、覆盖浅中深层的相邻层对。具体名单在运行前写入 manifest。
+- 之前热力图的 in_proj_a 是线性注意力控制投影示例，不是 Q/K/V 功能相似性的测量。
+
+## 2. 为什么不用先找 R
+
+给定相同隐藏输入 H，且位置、mask 和缓存/递推状态一致，比较两套模块的实际检索函数：
+
+S_i(H) = Q_i(H) K_i(H)^T / sqrt(d)，P_i(H) = softmax(S_i(H) + mask)。
+
+这里 Q/K 指完成该架构实际归一化和位置处理后的量。直接比较 P 可避开内部坐标差异；
+logit 每行加常数不改变 softmax，因此同时报告去行均值的分数误差。
+
+这不是“存在共同正交 R”的数学证明。反例 Q_2=2Q_1、K_2=K_1/2 保持所有点积，
+但非零 Q 的范数改变，不能由正交变换产生。功能等价允许比共同正交旋转更广的关系。
+相反，在单套 Q/K 上共同右乘 R 保持点积，只说明坐标不唯一，并未说明两个已训练层可复用。
+实际 RoPE、GQA、归一化、门控也必须包含在函数边界内。
+
+有限探针只能提供相应输入分布上的经验证据，不是对所有 H 的形式证明。
+在无位置处理/归一化的纯线性特例中，可研究 W_Q W_K^T 的代数相等性；
+一般模块不据此简化，首轮不物化所有大算子。
+
+## 3. 同输入与真实输入分开
+
+对候选 i/j，分别从原模型 i 和 j 的入口收集 H_i、H_j；分别比较 f_i(H_i) 与 f_j(H_i)，
+以及 f_i(H_j) 与 f_j(H_j)。避免只选有利于某一层的输入。模块前归一化如位于边界外，
+必须明确分别测“归一化后核心”和“包含归一化的完整分支”。
+
+这检验模块函数接近程度。实际网络的 f_i(H_i) 与 f_j(H_j) 使用不同输入，是另一项量。
+权重共享仍需对每个输入计算；缓存上游输出用于下游则改变了输入，必须实际干预后检验。
+MLP 测残差相加前的分支输出，防止共同输入 H 掩盖分支差异。
+
+## 4. 指标和对照
+
+| 对象 | 首轮指标 | 不能据此宣称的结论 |
+|---|---|---|
+| full attention 逐头 P | 平均及 P95 行 JS（自然对数，范围 0–ln2）、top-5 重合率；去行均值 S 相对误差 | P 相似不保证 V/O 输出相似 |
+| 完整 attention 分支 | 相对 L2、逐 token 误差 P50/P95、余弦；包含 V/O/门控 | 平均低误差不保证长尾任务能力 |
+| 线性注意力 | 同输入、同合法初始状态的分支输出及最终状态误差；后续状态继续使用测试 | 不构造不存在的 softmax 图；状态数值相似不等于长期稳定 |
+| MLP | 同输入分支输出相对 L2、逐 token P95、余弦 | 不以各矩阵单独相似代替 gate/up/down 联合验证 |
+
+相对误差统一为 ||candidate-reference|| / max(||reference||, epsilon)，并记录低范数样本数量。
+首轮筛选门槛预设：P 平均 JS ≤0.01 且 top-5 重合 ≥0.8；完整分支相对 L2 ≤0.05、
+逐 token 相对误差 P95 ≤0.1。它们是筛选约定，不是文献保证或能力损失界。
+线性模块和 MLP 使用输出门槛；只通过 P 的候选不能自动通过完整模块门槛。
+
+对照：同模块重复测量、随机同类型层对；首 token 保留/移除并重新归一化两个版本；
+排除自注意与局部窗口主导后再测；按序列/位置/浅中深层分层。零分母与无效行单独标记。
+逐头记录，不先平均头。默认只用原头序；可选的离散头排列仅在校准集选定后冻结，
+须满足 GQA 分组和 V/O 一致性，不允许把最优头匹配分数当作可执行替换结果。
+CKA 仅作辅助筛选；共享位置模板、attention sink 和共同残差都可能制造高相似。
+
+## 5. 资源与阶段预算
+
+当前服务器实际约 1.7 GiB RAM，不按“60 GB 内存”安排前向；60 GB 是磁盘扩容。
+后续需要用户确认可执行前向的远程环境；建议先评估至少 16 GiB RAM/显存的单模型环境，
+这不是已验证最低配置。先做内存估算及一次短样本容量探测，不足即停止，不自动换机器。
+不在本机编译、不自动重下已删除权重。本轮只交付设计。
+
+后续若获执行授权，首轮固定模型/revision/dtype、batch=1、长度最多 256，
+校准和留出各 16 条固定且去重的文本序列（中文/英文各半），只处理文本路径。
+数据来源、许可、tokenizer revision、截断、样本 hash 和抽样 seed 必须先入 manifest。
+每序列至多 32 个固定 query 位置，保留这些行的全部合法 key，避免采样 key 改变 softmax。
+流式聚合，不保存全部层的 N×N attention，不调用生成式聊天作替代评测。
+
+| 阶段 | 上限 | 完成条件 / 停止条件 |
+|---|---|---|
+| 容量探测及基线 | 15 分钟 | 无 OOM，固定输出重复测量可复现；否则停止 |
+| 同输入探测与筛选 | 45 分钟 | 固定 27 对候选；每类别最多保留 1 对，总计最多 3 对；无通过者即停止后续干预 |
+| 单处干预与质量 | 45 分钟 | 逐个候选，不叠加；留出集测 token NLL 和逐序列变化；失败不追加搜索 |
+| 成本测量和归档 | 15 分钟 | 保存基线/干预的质量、内存和计时，超时则输出已完成覆盖率 |
+
+总计算上限 120 分钟；下载单独记录，不触发无限重试。16 条留出序列只是先导证据，
+不能据此宣布广泛能力保持。首轮通过后才另立更大留出任务验证计划。
+
+## 6. 干预必须对应可节省的对象
+
+| 干预 | 可以争取的收益 | 必须额外确认 |
+|---|---|---|
+| 跨层绑定权重 | 权重存储/加载内存 | 各层输入仍不同，通常不减少投影 FLOPs |
+| 重用上游 attention 分布，保留下游 V/O | 部分 Q/K 投影与 QK/softmax 计算 | 保留分布的内存及实现开销；decode 未必值得缓存完整图 |
+| 跨层共享 K/V cache | KV 内存，可能减少 K/V 投影 | GQA、位置、缓存布局与层依赖合法性 |
+| 重用完整 attention/MLP 输出 | 对应分支计算 | 下游输入不同，必须测试整个原模型中的替换效应 |
+
+选择的每个候选首先给出一种具体可实现干预；只有同输入指标通过却无实际可省操作，
+标记“功能相似但暂无复用实现”。不把整模块可替换和其中 Q/K 可替换混为一谈。
+
+首轮探索性质量门槛：留出平均 token NLL 增量 ≤0.01 nat/token，逐序列增量 P95 ≤0.05，
+输出有限；同时保存全部逐序列差值，不将该小样本阈值称为统计显著性或普适能力保证。
+候选只在校准集筛选；留出失败不回调阈值、不反复选择。
+
+性能分别测 prefill 和固定 32-token decode；固定 batch/长度/dtype/backend，预热 3 次、
+交错运行基线与干预各 10 次，报告原始时延和中位数/P95、峰值内存、KV/权重字节及理论运算变化。
+插桩诊断时延不作为优化实现时延。若速度差落在运行波动内，不宣称加速；
+若只减内存，则结论限定为内存收益。计入缓存、索引与数据移动开销。
+
+## 7. 分组与最终证据
+
+单处通过后，才讨论多成员组：每个成员必须通过对同一代表的定向替换检查；
+不能用 A≈B、B≈C 推出 A 可代表 C。多处同时替换须再测累积误差和性能，
+单处收益不能相加为最终模型收益。首轮不自动执行多处替换。
+
+输出每对候选/每序列/每头指标 CSV、覆盖率和失败原因、质量—成本散点图、逐层热图、
+基线与干预原始计时、环境及代码版本。结论分为：功能接近、单处干预通过、测得实际收益三档。
+“存在一个免训练复用点”的操作性证据必须同时具有后两项；当前静态试验尚未提供这类证据。
+
+相关依据与训练限制见 [文献核对](FUNCTIONAL_REUSE_LITERATURE.md)。
diff --git a/pilot/__init__.py b/pilot/__init__.py
new file mode 100644
index 0000000..d86dfa6
--- /dev/null
+++ b/pilot/__init__.py
@@ -0,0 +1 @@
+"""Bounded, inference-free weight similarity pilot."""
diff --git a/pilot/analyze.py b/pilot/analyze.py
new file mode 100644
index 0000000..04854b5
--- /dev/null
+++ b/pilot/analyze.py
@@ -0,0 +1,396 @@
+"""Bounded CPU-only screening, exact verification and conservative sharing groups."""
+import argparse
+import collections
+import csv
+import hashlib
+import itertools
+import json
+import math
+import pathlib
+import platform
+import resource
+import time
+
+import numpy as np
+
+from .tensors import decode, finish, load_tensors, metrics
+
+THRESHOLDS = [.01, .02, .05, .10, .20]
+
+
+def write_json(path, obj):
+    path.write_text(json.dumps(obj, indent=2, allow_nan=False))
+
+
+def write_csv(path, rows):
+    if not rows:
+        path.write_text('')
+        return
+    with path.open('w', newline='') as f:
+        writer = csv.DictWriter(f, fieldnames=list(rows[0]))
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def stable_seed(key, seed):
+    return int.from_bytes(hashlib.sha256((str(seed) + str(key)).encode()).digest()[:8], 'little')
+
+
+def read_object(obj, tensors):
+    t = tensors[obj['tensor']]
+    if obj['kind'] == 'tensor':
+        return t.read()
+    slices = tuple(slice(s, s + b) for s, b in zip(obj['start'], obj['shape']))
+    return decode(t.raw.reshape(t.shape)[slices], t.dtype).ravel()
+
+
+def sample_object(obj, tensors, sample_size=2048):
+    t = tensors[obj['tensor']]
+    n = math.prod(obj['shape'])
+    indices = np.linspace(0, n - 1, min(n, sample_size), dtype=np.int64)
+    if obj['kind'] == 'tensor':
+        return t.read(indices)
+    coords = np.unravel_index(indices, obj['shape'])
+    global_coords = tuple(c + s for c, s in zip(coords, obj['start']))
+    flat = np.ravel_multi_index(global_coords, t.shape)
+    return t.read(flat)
+
+
+def exact_pair(a, b, tensors, deadline):
+    if a['kind'] != 'tensor':
+        result = metrics(read_object(a, tensors), read_object(b, tensors))
+        tensors[a['tensor']].evict()
+        tensors[b['tensor']].evict()
+        return result
+    ta, tb = tensors[a['tensor']], tensors[b['tensor']]
+    xx = yy = xy = delta = sx = sy = 0.
+    for start in range(0, ta.numel, 262144):
+        if time.monotonic() > deadline:
+            raise TimeoutError('Analysis deadline reached')
+        sl = slice(start, min(start + 262144, ta.numel))
+        x, y = ta.read(sl).astype(np.float64), tb.read(sl).astype(np.float64)
+        xx += float(x @ x)
+        yy += float(y @ y)
+        xy += float(x @ y)
+        d = x - y
+        delta += float(d @ d)
+        sx += float(x.sum())
+        sy += float(y.sum())
+        ta.evict()
+        tb.evict()
+    ta.evict()
+    tb.evict()
+    return finish(xx, yy, xy, delta, sx, sy, ta.numel)
+
+
+def sharing_groups(objects, pairs, threshold):
+    """Star groups; every member must have an exact verified edge to its representative."""
+    adjacent = collections.defaultdict(dict)
+    for pair in pairs:
+        a, b = pair['a'], pair['b']
+        if pair['error_member_to_rep'] <= threshold:
+            adjacent[b][a] = pair['error_member_to_rep']
+        if pair['error_rep_to_member'] <= threshold:
+            adjacent[a][b] = pair['error_rep_to_member']
+    unassigned = set(range(len(objects)))
+    order = sorted(unassigned, key=lambda i: (-len(adjacent[i]), objects[i]['id']))
+    groups = []
+    for rep in order:
+        if rep not in unassigned:
+            continue
+        members = sorted(set(adjacent[rep]) & unassigned)
+        members.append(rep)
+        unassigned.difference_update(members)
+        groups.append({'representative': rep, 'members': members,
+                       'max_error': max([adjacent[rep][i] for i in members if i != rep] or [0.])})
+    return groups
+
+
+def nearest_candidates(objects, features, cap, neighbors=10):
+    buckets = collections.defaultdict(list)
+    for i, obj in enumerate(objects):
+        buckets[(obj['role'], tuple(obj['shape']), obj['scale'], obj['dtype'])].append(i)
+    candidates = []
+    eligible_pairs = 0
+    for key, ids in sorted(buckets.items(), key=lambda item: str(item[0])):
+        eligible_pairs += len(ids) * (len(ids) - 1) // 2
+        if len(ids) < 2:
+            continue
+        x = np.stack([features[i] for i in ids]).astype(np.float64)
+        norm = np.linalg.norm(x, axis=1)
+        u = x / np.maximum(norm[:, None], 1e-30)
+        # Cap each bucket's reservoir to bound the screening matrix.
+        cos = u @ u.T
+        sq = np.maximum(0., norm[:, None] ** 2 + norm[None, :] ** 2 - 2 * (x @ x.T))
+        err = np.sqrt(sq / np.maximum(norm[:, None] ** 2, 1e-30))
+        np.fill_diagonal(cos, -np.inf)
+        np.fill_diagonal(err, np.inf)
+        seen = set()
+        for row, i in enumerate(ids):
+            near = set(np.argsort(err[row])[:neighbors]) | set(np.argsort(-cos[row])[:neighbors])
+            for col in near:
+                j = ids[col]
+                edge = (min(i, j), max(i, j))
+                if i == j or edge in seen:
+                    continue
+                seen.add(edge)
+                candidates.append({'a': edge[0], 'b': edge[1], 'screen_error': float(min(err[row, col], err[col, row])),
+                                   'screen_cosine': float(cos[row, col])})
+    # Low-error candidates first, then guarantee role/scale diversity round-robin.
+    queues = collections.defaultdict(list)
+    for c in sorted(candidates, key=lambda c: (c['screen_error'], c['a'], c['b'])):
+        o = objects[c['a']]
+        queues[(o['role'], tuple(o['shape']), o['scale'])].append(c)
+    queues = [collections.deque(q) for _, q in sorted(queues.items(), key=lambda x: str(x[0]))]
+    chosen = []
+    while queues and len(chosen) < cap:
+        for q in queues:
+            if q and len(chosen) < cap:
+                chosen.append(q.popleft())
+        queues = [q for q in queues if q]
+    return chosen, {'eligible_pairs_in_screened_objects': eligible_pairs,
+                    'candidate_pairs_before_cap': len(candidates), 'selected_pairs': len(chosen)}
+
+
+def object_record(t, kind='tensor', shape=None, start=None, scale='whole'):
+    shape = tuple(shape or t.shape)
+    return {'id': t.name if kind == 'tensor' else f'{t.name}|{scale}|{start}',
+            'tensor': t.name, 'role': t.role, 'layer': t.layer, 'kind': kind, 'dtype': t.dtype,
+            'shape': shape, 'start': start, 'scale': scale,
+            'bytes': math.prod(shape) * (t.bytes // t.numel)}
+
+
+def build_blocks(tensors, seed, deadline, reservoir_limit=256):
+    reservoirs, counts, rngs = {}, collections.Counter(), {}
+    totals = collections.defaultdict(lambda: {'blocks': 0, 'bytes': 0, 'edge_bytes': 0})
+    for t in tensors.values():
+        if len(t.shape) == 2:
+            specs = [(f'2d-{s}', (s, s)) for s in (64, 128) if min(t.shape) >= s]
+        elif len(t.shape) == 3:
+            specs = [('3d-axis0-slice', (1, min(64, t.shape[1]), min(64, t.shape[2]))),
+                     ('3d-volume', (min(4, t.shape[0]), min(32, t.shape[1]), min(32, t.shape[2])))]
+        else:
+            continue
+        for scale, shape in specs:
+            nblocks = math.prod(d // b for d, b in zip(t.shape, shape))
+            totals[scale]['blocks'] += nblocks
+            block_bytes = math.prod(shape) * (t.bytes // t.numel)
+            totals[scale]['bytes'] += nblocks * block_bytes
+            totals[scale]['edge_bytes'] += t.bytes - nblocks * block_bytes
+            for grid in itertools.product(*(range(d // b) for d, b in zip(t.shape, shape))):
+                if time.monotonic() > deadline:
+                    raise TimeoutError('Deadline during block enumeration')
+                start = tuple(i * b for i, b in zip(grid, shape))
+                obj = object_record(t, 'block', shape, start, scale)
+                key = (t.role, shape, scale)
+                if key not in reservoirs:
+                    reservoirs[key], rngs[key] = [], np.random.default_rng(stable_seed(key, seed))
+                counts[key] += 1
+                reservoir = reservoirs[key]
+                if len(reservoir) < reservoir_limit:
+                    reservoir.append(obj)
+                else:
+                    j = int(rngs[key].integers(counts[key]))
+                    if j < reservoir_limit:
+                        reservoir[j] = obj
+    objects = [obj for key in sorted(reservoirs, key=str) for obj in reservoirs[key]]
+    coverage = [{'role': key[0], 'shape': str(key[1]), 'scale': key[2],
+                 'total_blocks': counts[key], 'sampled_blocks': len(reservoirs[key])}
+                for key in sorted(reservoirs, key=str)]
+    return objects, coverage, dict(totals)
+
+
+def structural_probe(a, b, tensors):
+    """Bounded mode unfoldings of aligned subvolumes; exploratory, never a grouping criterion."""
+    def small(obj):
+        t = tensors[obj['tensor']]
+        shape = obj['shape']
+        starts = obj['start'] or [0] * len(shape)
+        slices = tuple(slice(s, s + min(d, 32)) for s, d in zip(starts, shape))
+        return decode(t.raw.reshape(t.shape)[slices], t.dtype)
+    x, y = small(a), small(b)
+    result = []
+    if x.ndim < 2:
+        return result
+    for axis in range(x.ndim):
+        mx = np.moveaxis(x, axis, 0).reshape(x.shape[axis], -1)
+        my = np.moveaxis(y, axis, 0).reshape(y.shape[axis], -1)
+        if min(mx.shape) < 2:
+            result.append({'axis': axis, 'probe_shape': list(x.shape), 'trivial_mode_skipped': True})
+            continue
+        ux, sx, _ = np.linalg.svd(mx, full_matrices=False)
+        uy, sy, _ = np.linalg.svd(my, full_matrices=False)
+        k = min(4, max(1, min(len(sx), len(sy)) // 2))
+        result.append({'axis': axis, 'probe_shape': list(x.shape),
+                       'k': k,
+                       'normalized_spectrum_distance': float(np.linalg.norm(sx / max(np.linalg.norm(sx), 1e-30) - sy / max(np.linalg.norm(sy), 1e-30))),
+                       'left_subspace_overlap': float(np.linalg.norm(ux[:, :k].T @ uy[:, :k]) ** 2 / k)})
+    return result
+
+
+def analyze_mode(mode, objects, tensors, root, deadline, cap, seed, baseline_cap=100):
+    print(f'{mode}: sampling {len(objects)} objects', flush=True)
+    features = []
+    for obj in objects:
+        features.append(sample_object(obj, tensors))
+        tensors[obj['tensor']].evict()
+    candidates, coverage = nearest_candidates(objects, features, cap)
+    write_json(root / f'{mode}_objects.json', objects)
+    pairs, nulls, structures = [], [], []
+    rng = np.random.default_rng(seed)
+    random_pairs = []
+    comparable = collections.defaultdict(list)
+    for j, obj in enumerate(objects):
+        comparable[(obj['role'], tuple(obj['shape']), obj['scale'], obj['dtype'])].append(j)
+    for key, ids in sorted(comparable.items(), key=lambda item: str(item[0])):
+        if len(ids) < 2:
+            continue
+        for _ in range(3):
+            a, b = rng.choice(ids, size=2, replace=False).tolist()
+            random_pairs.append({'a': a, 'b': b, 'role': key[0], 'sampled': True,
+                                 **metrics(features[a], features[b])})
+    write_json(root / f'{mode}_random_pairs.json', random_pairs)
+    timeout = False
+    structural_counts = collections.Counter()
+    for i, candidate in enumerate(candidates):
+        if time.monotonic() > deadline:
+            timeout = True
+            break
+        a, b = candidate['a'], candidate['b']
+        try:
+            measured = exact_pair(objects[a], objects[b], tensors, deadline)
+        except TimeoutError:
+            timeout = True
+            break
+        row = {**candidate, **measured,
+               'a_id': objects[a]['id'], 'b_id': objects[b]['id'],
+               'role': objects[a]['role'], 'scale_name': objects[a]['scale'],
+               'layer_distance': abs(objects[a]['layer'] - objects[b]['layer'])}
+        row['screen_vs_exact_min_error_absolute_difference'] = abs(candidate['screen_error'] - min(measured['error_member_to_rep'], measured['error_rep_to_member']))
+        # Tail diagnostics are sampled, explicitly labelled, not claimed exact.
+        fx, fy = features[a].astype(np.float64), features[b].astype(np.float64)
+        row['sampled_error_q99_over_member_rms'] = float(np.quantile(np.abs(fx - fy), .99) / max(np.sqrt(np.mean(fx * fx)), 1e-30))
+        pairs.append(row)
+        if i < baseline_cap:
+            for method, y in [('shuffled', rng.permutation(fy)), ('gaussian_norm_matched', rng.normal(size=fy.size))]:
+                if method == 'gaussian_norm_matched':
+                    y *= np.linalg.norm(fy) / max(np.linalg.norm(y), 1e-30)
+                nulls.append({'a': a, 'b': b, 'method': method, 'sampled': True, **metrics(fx, y)})
+        ndim = len(objects[a]['shape'])
+        if ndim >= 2 and structural_counts[ndim] < 20:
+            structures.append({'a': a, 'b': b, 'modes': structural_probe(objects[a], objects[b], tensors)})
+            tensors[objects[a]['tensor']].evict()
+            tensors[objects[b]['tensor']].evict()
+            structural_counts[ndim] += 1
+        if (i + 1) % 100 == 0:
+            write_csv(root / f'{mode}_pairs.csv', pairs)
+            print(f'{mode}: verified {i + 1}/{len(candidates)}', flush=True)
+    write_csv(root / f'{mode}_pairs.csv', pairs)
+    write_json(root / f'{mode}_nulls.json', nulls)
+    write_json(root / f'{mode}_structure.json', structures)
+    coverage.update({'objects_screened': len(objects), 'pairs_verified': len(pairs), 'timed_out': timeout,
+                     'screen_sample_elements': 2048, 'candidate_neighbors_per_metric': 10})
+    write_json(root / f'{mode}_coverage.json', coverage)
+    return pairs
+
+
+def summarize(mode, objects, pairs, model_bytes, root, partition_totals=None):
+    rows = []
+    scales = sorted({o['scale'] for o in objects})
+    for scale in scales:
+        ids = [i for i, o in enumerate(objects) if o['scale'] == scale]
+        selected = set(ids)
+        for threshold in THRESHOLDS:
+            groups = sharing_groups(objects, [p for p in pairs if p['a'] in selected], threshold)
+            groups = [g for g in groups if g['representative'] in selected]
+            original = sum(objects[i]['bytes'] for i in ids)
+            rep_bytes = sum(objects[g['representative']]['bytes'] for g in groups)
+            # Concrete theoretical layout: 16 bytes per object mapping, plus 64 bytes per shared group.
+            # Identity case falls back to original checkpoint at zero additional cost.
+            mapping_bytes = 16 * len(ids) + 64 * sum(len(g['members']) > 1 for g in groups)
+            proposed = rep_bytes + mapping_bytes
+            saved = max(0, original - proposed)
+            partition_count = partition_totals[scale]['blocks'] if partition_totals else len(ids)
+            rows.append({'mode': mode, 'scale': scale, 'threshold': threshold,
+                         'objects': len(ids), 'groups': len(groups),
+                         'total_partition_objects': partition_count,
+                         'unexamined_partition_objects': partition_count - len(ids),
+                         'conservative_partition_groups': partition_count - len(ids) + len(groups),
+                         'shared_groups': sum(len(g['members']) > 1 for g in groups),
+                         'max_group_size': max([len(g['members']) for g in groups] or [0]),
+                         'verified_shared_bytes': sum(sum(objects[i]['bytes'] for i in g['members']) for g in groups if len(g['members']) > 1),
+                         'examined_object_bytes': original, 'representative_bytes': rep_bytes,
+                         'mapping_bytes_if_encoded': mapping_bytes, 'net_saved_bytes': saved,
+                         'net_saved_model_fraction': saved / model_bytes,
+                         'max_replacement_error': max([g['max_error'] for g in groups] or [0.])})
+            write_json(root / f'groups_{mode}_{scale}_{threshold:.2f}.json', groups)
+    return rows
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--root', type=pathlib.Path, required=True)
+    p.add_argument('--seconds', type=int, default=7200)
+    p.add_argument('--seed', type=int, default=20261109)
+    args = p.parse_args()
+    started = time.monotonic()
+    deadline = started + args.seconds
+    root = args.root
+    output = root / 'analysis'
+    output.mkdir(exist_ok=True)
+    tensors = load_tensors(root / 'weights')
+    inventory = []
+    for t in tensors.values():
+        if time.monotonic() > deadline:
+            raise TimeoutError('Deadline during inventory')
+        n = 0
+        total = squared = 0.
+        max_abs = 0.
+        for x in t.chunked():
+            if not np.isfinite(x).all():
+                raise ValueError(f'Nonfinite weights: {t.name}')
+            z = x.astype(np.float64)
+            n += z.size
+            total += float(z.sum())
+            squared += float(z @ z)
+            max_abs = max(max_abs, float(np.abs(z).max()))
+            t.evict()
+        inventory.append({'name': t.name, 'shape': str(t.shape), 'ndim': len(t.shape), 'dtype': t.dtype,
+                          'numel': t.numel, 'bytes': t.bytes, 'role': t.role, 'layer': t.layer,
+                          'mean': total / n, 'rms': math.sqrt(squared / n), 'max_abs': max_abs})
+        t.evict()
+    write_csv(output / 'inventory.csv', inventory)
+    print(f'Inventory: {len(tensors)} tensors, {sum(t.bytes for t in tensors.values())} bytes', flush=True)
+    whole = [object_record(t) for t in tensors.values()]
+    # Stage budgets prevent the whole-tensor stage from consuming block-analysis time.
+    whole_deadline = min(deadline, started + args.seconds * .50)
+    whole_pairs = analyze_mode('tensor', whole, tensors, output, whole_deadline, 2000, args.seed)
+    blocks, block_coverage, block_totals = build_blocks(tensors, args.seed, deadline)
+    write_csv(output / 'block_sampling.csv', block_coverage)
+    write_json(output / 'block_partitions.json', block_totals)
+    block_pairs = analyze_mode('block', blocks, tensors, output, deadline, 20000, args.seed)
+    model_bytes = sum(t.bytes for t in tensors.values())
+    rows = summarize('tensor', whole, whole_pairs, model_bytes, output)
+    rows += summarize('block', blocks, block_pairs, model_bytes, output, block_totals)
+    write_csv(output / 'compression_curve.csv', rows)
+    summary = {'model': json.loads((root / 'manifest.json').read_text())['repo'],
+               'revision': json.loads((root / 'manifest.json').read_text())['revision'],
+               'tensor_count': len(tensors), 'parameter_count': sum(t.numel for t in tensors.values()),
+               'model_weight_bytes': model_bytes, 'ndim_counts': dict(collections.Counter(len(t.shape) for t in tensors.values())),
+               'elapsed_seconds': time.monotonic() - started, 'analysis_budget_seconds': args.seconds,
+               'peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,
+               'python': platform.python_version(), 'numpy': np.__version__, 'seed': args.seed,
+               '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}
+    write_json(output / 'summary.json', summary)
+    print(json.dumps(summary), flush=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pilot/audit.py b/pilot/audit.py
new file mode 100644
index 0000000..edb384f
--- /dev/null
+++ b/pilot/audit.py
@@ -0,0 +1,75 @@
+"""Audit saved numerical data and freeze provenance before archive/cleanup."""
+import argparse
+import csv
+import hashlib
+import json
+import math
+import os
+import pathlib
+import platform
+import re
+import shutil
+import time
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--root', required=True, type=pathlib.Path)
+    p.add_argument('--analysis-code-commit', help='Commit actually used for the weight analysis; omit if unknown')
+    args = p.parse_args()
+    root = args.root.resolve()
+    if not __debug__:
+        raise RuntimeError('Validation requires Python assertions enabled')
+    checked = {}
+    for mode in ('tensor', 'block'):
+        with (root / 'analysis' / f'{mode}_pairs.csv').open() as f:
+            rows = list(csv.DictReader(f))
+        coverage = json.loads((root / 'analysis' / f'{mode}_coverage.json').read_text())
+        if len(rows) != coverage['pairs_verified']:
+            raise ValueError('Coverage count mismatch')
+        for row in rows:
+            for key in ('cosine', 'pearson'):
+                if row[key] and (not math.isfinite(float(row[key])) or abs(float(row[key])) > 1 + 1e-7):
+                    raise ValueError(f'Invalid correlation: {mode}/{key}')
+            for key in ('error_member_to_rep', 'error_rep_to_member', 'symmetric_error', 'scaled_error',
+                        'sampled_error_q99_over_member_rms', 'screen_vs_exact_min_error_absolute_difference'):
+                value = float(row[key])
+                if not math.isfinite(value) or value < 0:
+                    raise ValueError(f'Invalid error: {mode}/{key}')
+            if not math.isfinite(float(row['scale'])):
+                raise ValueError('Invalid scaling diagnostic')
+        checked[mode] = {'rows': len(rows), 'finite_metrics': True, 'coverage_matches': True}
+    test_log = (root / 'tests.log').read_text()
+    match = re.search(r'Ran (\d+) tests in ([\d.]+)s', test_log)
+    if not match or '\nOK\n' not in test_log:
+        raise ValueError('Final tests did not pass')
+    qc = {'passed': True, 'checked_unix': time.time(), 'numerical_tables': checked,
+          'final_tests': int(match[1]), 'final_test_seconds': float(match[2]),
+          'earlier_parallel_test_timeout_seconds': 300 if (root / 'tests-during-io-timeout.txt').exists() else None}
+    (root / 'numerical-qc.json').write_text(json.dumps(qc, indent=2))
+    for src, dest in [('tests.log', 'test-results.txt'), ('analysis.log', 'analysis-run.txt'),
+                      ('plot.log', 'plot-run.txt'), ('download-ranged.log', 'download-progress.txt'),
+                      ('supervisor.log', 'supervisor-run.txt')]:
+        shutil.copy2(root / src, root / dest)
+    mem = {}
+    for line in pathlib.Path('/proc/meminfo').read_text().splitlines():
+        key, value = line.split(':', 1)
+        if key in ('MemTotal', 'MemAvailable', 'SwapTotal', 'SwapFree'):
+            mem[key + '_KiB'] = int(value.split()[0])
+    environment = {'recorded_unix': time.time(), 'python': platform.python_version(),
+                   'platform': platform.platform(), 'machine': platform.machine(),
+                   'logical_cpus': os.cpu_count(), 'memory_snapshot': mem,
+                   'blas_threads': 1, 'assertions_enabled': __debug__,
+                   'analysis_io_priority': 'best-effort 7, applied during block verification',
+                   'analysis_code_commit': args.analysis_code_commit,
+                   'note': 'Shared host; see resource-observation.txt. Memory snapshot is post-analysis.'}
+    (root / 'execution-environment.json').write_text(json.dumps(environment, indent=2))
+    project = pathlib.Path(__file__).resolve().parents[1]
+    files = sorted((project / 'pilot').glob('*.py')) + [project / 'requirements.txt', project / 'tests/test_pilot.py']
+    hashes = [f'{hashlib.sha256(file.read_bytes()).hexdigest()}  {file.relative_to(project)}' for file in files]
+    (root / 'code_checksums.txt').write_text('\n'.join(hashes) + '\n')
+    print(json.dumps(qc))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pilot/download.py b/pilot/download.py
new file mode 100644
index 0000000..2e903b0
--- /dev/null
+++ b/pilot/download.py
@@ -0,0 +1,140 @@
+"""Download pinned official safetensors, no framework or shared cache."""
+import argparse
+import concurrent.futures
+import hashlib
+import json
+import pathlib
+import os
+import shutil
+import time
+import threading
+import urllib.parse
+import urllib.request
+
+
+def fetch_json(url):
+    with urllib.request.urlopen(url, timeout=90) as response:
+        return json.load(response)
+
+
+def ranged_download(url, part, total, workers=8):
+    """Resume a sequential prefix; journal completed byte ranges before final hashing."""
+    journal = part.with_suffix(part.suffix + '.ranges.json')
+    if journal.exists():
+        state = json.loads(journal.read_text())
+        if state['total'] != total:
+            raise RuntimeError('Range journal size mismatch')
+    else:
+        state = {'total': total, 'prefix': part.stat().st_size if part.exists() else 0, 'done': []}
+        journal.write_text(json.dumps(state))
+    lock = threading.Lock()
+    fd = os.open(part, os.O_CREAT | os.O_RDWR, 0o600)
+    chunk_size = 64 * 1024 * 1024
+    ranges = [(start, min(start + chunk_size, total) - 1)
+              for start in range(state['prefix'], total, chunk_size)]
+
+    def one(bounds):
+        start, end = bounds
+        if start in state['done']:
+            return
+        for attempt in range(3):
+            try:
+                req = urllib.request.Request(url, headers={'Range': f'bytes={start}-{end}'})
+                with urllib.request.urlopen(req, timeout=180) as response:
+                    expected = f'bytes {start}-{end}/{total}'
+                    if response.status != 206 or response.headers.get('Content-Range') != expected:
+                        raise RuntimeError('Server did not honor exact requested range')
+                    offset = start
+                    while chunk := response.read(min(4 * 1024 * 1024, end - offset + 1)):
+                        written = os.pwrite(fd, chunk, offset)
+                        if written != len(chunk):
+                            raise RuntimeError('Short positional write')
+                        offset += written
+                        if offset > end:
+                            break
+                    if offset != end + 1:
+                        raise RuntimeError('Incomplete range')
+                with lock:
+                    os.fsync(fd)
+                    state['done'].append(start)
+                    tmp = journal.with_suffix('.tmp')
+                    tmp.write_text(json.dumps(state))
+                    tmp.replace(journal)
+                    done = state['prefix'] + sum(b - a + 1 for a, b in ranges if a in state['done'])
+                    print(f'Weight download {done}/{total} bytes ({100 * done / total:.1f}%)', flush=True)
+                return
+            except Exception:
+                if attempt == 2:
+                    raise
+                time.sleep(2 ** attempt)
+    try:
+        with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as pool:
+            list(pool.map(one, ranges))
+    finally:
+        os.close(fd)
+    journal.unlink()
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--root', type=pathlib.Path, required=True)
+    p.add_argument('--repo', default='Qwen/Qwen3.5-2B')
+    args = p.parse_args()
+    root = args.root.resolve()
+    root.mkdir(parents=True, exist_ok=True)
+    weights = root / 'weights'
+    weights.mkdir(exist_ok=True)
+    marker = weights / '.pilot-owned'
+    marker.write_text(args.repo)
+    old_manifest = root / 'manifest.json'
+    pinned = json.loads(old_manifest.read_text())['revision'] if old_manifest.exists() else None
+    endpoint = f'https://huggingface.co/api/models/{args.repo}'
+    if pinned:
+        endpoint += f'/revision/{pinned}'
+    info = fetch_json(endpoint + '?blobs=true')
+    revision = info['sha']
+    files = [f for f in info['siblings'] if f['rfilename'].endswith('.safetensors')
+             or f['rfilename'] in ('config.json', 'model.safetensors.index.json', 'README.md')]
+    total = sum(f.get('size', 0) for f in files)
+    if shutil.disk_usage(root).free < total + 3_000_000_000:
+        raise RuntimeError('Insufficient space for checkpoint plus 3 GB margin')
+    manifest = {'repo': args.repo, 'revision': revision, 'expected_bytes': total,
+                'started_unix': time.time(), 'files': [], 'license': info.get('cardData', {}).get('license')}
+    (root / 'source_metadata.json').write_text(json.dumps(info, indent=2))
+    for entry in files:
+        name = entry['rfilename']
+        if pathlib.PurePosixPath(name).name != name:
+            raise RuntimeError(f'Unexpected nested path: {name}')
+        dest = weights / name
+        part = dest.with_suffix(dest.suffix + '.partial')
+        digest = hashlib.sha256()
+        url = f'https://huggingface.co/{args.repo}/resolve/{revision}/{urllib.parse.quote(name)}?download=true'
+        print(f'Downloading {name}, expected {entry.get("size")} bytes', flush=True)
+        if entry.get('size', 0) > 128 * 1024 * 1024:
+            ranged_download(url, part, entry['size'])
+            with part.open('rb') as stream:
+                while chunk := stream.read(4 * 1024 * 1024):
+                    digest.update(chunk)
+        else:
+            with urllib.request.urlopen(url, timeout=180) as response, part.open('wb') as out:
+                while chunk := response.read(4 * 1024 * 1024):
+                    out.write(chunk)
+                    digest.update(chunk)
+        sha = digest.hexdigest()
+        if entry.get('size') and part.stat().st_size != entry['size']:
+            raise RuntimeError(f'Size mismatch: {name}')
+        expected_sha = entry.get('lfs', {}).get('sha256')
+        if expected_sha and sha != expected_sha:
+            raise RuntimeError(f'SHA256 mismatch: {name}')
+        part.rename(dest)
+        manifest['files'].append({'name': name, 'bytes': dest.stat().st_size,
+                                  'sha256': sha, 'upstream_sha256': expected_sha})
+        (root / 'manifest.json').write_text(json.dumps(manifest, indent=2))
+    manifest['completed_unix'] = time.time()
+    (root / 'manifest.json').write_text(json.dumps(manifest, indent=2))
+    shutil.copy2(weights / 'config.json', root / 'model_config.json')
+    print(json.dumps({'revision': revision, 'bytes': sum(f['bytes'] for f in manifest['files'])}), flush=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pilot/plot.py b/pilot/plot.py
new file mode 100644
index 0000000..61c0f3e
--- /dev/null
+++ b/pilot/plot.py
@@ -0,0 +1,261 @@
+"""Scientific figures and a compact result report from saved data only."""
+import argparse
+import csv
+import json
+import pathlib
+
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch, Rectangle
+import numpy as np
+
+
+def read_csv(path):
+    with path.open() as f:
+        return list(csv.DictReader(f))
+
+
+def write_csv(path, rows):
+    with path.open('w', newline='') as f:
+        if rows:
+            writer = csv.DictWriter(f, fieldnames=list(rows[0]))
+            writer.writeheader()
+            writer.writerows(rows)
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--root', type=pathlib.Path, required=True)
+    args = p.parse_args()
+    root = args.root
+    out = root / 'analysis'
+    figures = root / 'figures'
+    figures.mkdir(exist_ok=True)
+    plt.rcParams.update({'font.size': 10, 'axes.spines.top': False, 'axes.spines.right': False,
+                         'pdf.fonttype': 42, 'svg.fonttype': 'none', 'figure.dpi': 120})
+    def save(fig, name):
+        fig.tight_layout()
+        for ext in ('png', 'pdf', 'svg'):
+            fig.savefig(figures / f'{name}.{ext}', dpi=200, bbox_inches='tight')
+        plt.close(fig)
+
+    curves = read_csv(out / 'compression_curve.csv')
+    summary = json.loads((out / 'summary.json').read_text())
+    dimensional = {}
+    for row in read_csv(out / 'inventory.csv'):
+        ndim = int(row['ndim'])
+        item = dimensional.setdefault(ndim, {'ndim': ndim, 'tensor_count': 0, 'weight_bytes': 0})
+        item['tensor_count'] += 1
+        item['weight_bytes'] += int(row['bytes'])
+    dimensional_rows = [dimensional[key] for key in sorted(dimensional)]
+    for item in dimensional_rows:
+        item['model_byte_fraction'] = item['weight_bytes'] / summary['model_weight_bytes']
+    with (out / 'dimensional_bytes.csv').open('w', newline='') as f:
+        writer = csv.DictWriter(f, fieldnames=list(dimensional_rows[0]))
+        writer.writeheader()
+        writer.writerows(dimensional_rows)
+    fig, axes = plt.subplots(1, 2, figsize=(11, 4))
+    for scale in sorted({r['scale'] for r in curves}):
+        rows = [r for r in curves if r['scale'] == scale]
+        x = [100 * float(r['threshold']) for r in rows]
+        axes[0].plot(x, [100 * float(r['net_saved_model_fraction']) for r in rows], 'o-', label=scale)
+        axes[1].plot(x, [int(r['shared_groups']) for r in rows], 'o-', label=scale)
+    axes[0].set(xlabel='Maximum relative weight error (%)', ylabel='Verified net savings / model bytes (%)')
+    axes[1].set(xlabel='Maximum relative weight error (%)', ylabel='Non-singleton sharing groups')
+    axes[0].legend(fontsize=8, loc='upper left')
+    fig.suptitle('Conservative verified sharing; block scales are separate alternatives')
+    save(fig, 'compression_frontier')
+
+    pair_sets = {mode: read_csv(out / f'{mode}_pairs.csv') for mode in ('tensor', 'block')}
+    role_rows = []
+    for mode, pairs in pair_sets.items():
+        mode_objects = json.loads((out / f'{mode}_objects.json').read_text())
+        buckets = {}
+        for row in pairs:
+            obj = mode_objects[int(row['a'])]
+            key = (obj['role'], obj['scale'], tuple(obj['shape']), obj['dtype'])
+            buckets.setdefault(key, []).append(row)
+        for (role_name, scale_name, shape, dtype), rows in sorted(buckets.items(), key=lambda item: str(item[0])):
+            errors = [float(r['error_member_to_rep']) for r in rows]
+            cosines = [float(r['cosine']) for r in rows if r['cosine']]
+            role_rows.append({'mode': mode, 'role': role_name, 'scale': scale_name,
+                              'shape': str(shape), 'ndim': len(shape), 'dtype': dtype,
+                              'verified_pairs': len(rows), 'minimum_direct_error': min(errors),
+                              'median_direct_error': float(np.median(errors)),
+                              'maximum_cosine': max(cosines) if cosines else '',
+                              'minimum_scaled_error': min(float(r['scaled_error']) for r in rows),
+                              'pairs_under_5pct_direct_error': sum(e <= .05 for e in errors),
+                              'pairs_under_20pct_direct_error': sum(e <= .20 for e in errors)})
+    with (out / 'role_summary.csv').open('w', newline='') as f:
+        if role_rows:
+            writer = csv.DictWriter(f, fieldnames=list(role_rows[0]))
+            writer.writeheader()
+            writer.writerows(role_rows)
+    fig, axes = plt.subplots(2, 2, figsize=(11, 7))
+    for row, (mode, pairs) in enumerate(pair_sets.items()):
+        for scale in sorted({r['scale_name'] for r in pairs}):
+            group = [r for r in pairs if r['scale_name'] == scale]
+            axes[row, 0].hist([float(r['cosine']) for r in group if r['cosine']], bins=np.linspace(-1, 1, 46), histtype='step', label=scale)
+            errors = [float(r['error_member_to_rep']) for r in group]
+            axes[row, 1].hist(errors, bins=np.linspace(0, max(1e-6, max(errors, default=0.) * 1.01), 46), histtype='step', label=scale)
+        axes[row, 0].set(title=f'{mode}: exact cosine of screened candidates', xlabel='Cosine', ylabel='Candidate pairs')
+        axes[row, 1].set(title=f'{mode}: exact direct replacement error', xlabel='Relative Frobenius error', ylabel='Candidate pairs')
+        axes[row, 0].legend(fontsize=7, loc='upper right')
+    save(fig, 'candidate_distributions')
+
+    pairs = pair_sets['tensor']
+    roles = sorted({r['role'] for r in pairs})
+    # Illustrate one repeated 2D projection; this is not a byte-weighted model summary.
+    objects = json.loads((out / 'tensor_objects.json').read_text())
+    candidates = [role for role in roles if any(o['role'] == role and len(o['shape']) == 2 for o in objects)]
+    role = max(candidates or roles, key=lambda k: sum(r['role'] == k for r in pairs))
+    chosen = [(i, o) for i, o in enumerate(objects) if o['role'] == role]
+    chosen.sort(key=lambda item: item[1]['layer'])
+    indices = {i: j for j, (i, _) in enumerate(chosen)}
+    matrix = np.full((len(chosen), len(chosen)), np.nan)
+    np.fill_diagonal(matrix, 1.)
+    for r in pairs:
+        a, b = int(r['a']), int(r['b'])
+        if a in indices and b in indices and r['cosine']:
+            matrix[indices[a], indices[b]] = matrix[indices[b], indices[a]] = float(r['cosine'])
+    fig, ax = plt.subplots(figsize=(7, 6))
+    cmap = plt.get_cmap('coolwarm').with_extremes(bad='white')
+    im = ax.imshow(matrix, cmap=cmap, vmin=-1, vmax=1)
+    for y, x in zip(*np.where(np.isnan(matrix))):
+        ax.add_patch(Rectangle((x - .5, y - .5), 1, 1, facecolor='none',
+                               edgecolor='#999999', hatch='///', linewidth=0))
+    labels = [o['layer'] for _, o in chosen]
+    ticks = np.arange(0, len(labels), max(1, len(labels) // 10))
+    ax.set_xticks(ticks, [labels[i] for i in ticks])
+    ax.set_yticks(ticks, [labels[i] for i in ticks])
+    ax.set(xlabel='Layer', ylabel='Layer', title=role.removeprefix('model.') + '\nHatching = unverified; diagonal is identity')
+    ax.legend(handles=[Patch(facecolor='white', edgecolor='#999999', hatch='///', label='Unverified pair')],
+              loc='upper center', bbox_to_anchor=(.5, -.12), fontsize=8, frameon=False)
+    fig.colorbar(im, ax=ax, label='Exact cosine')
+    save(fig, 'layer_heatmap')
+    heatmap_rows = [{'a_layer': oa['layer'], 'b_layer': ob['layer'], 'role': role,
+                     'cosine': float(matrix[i, j]) if not np.isnan(matrix[i, j]) else '',
+                     'verified': bool(i != j and not np.isnan(matrix[i, j])), 'identity': i == j}
+                    for i, (_, oa) in enumerate(chosen) for j, (_, ob) in enumerate(chosen)]
+    write_csv(out / 'heatmap_pairs.csv', heatmap_rows)
+
+    distance_rows = []
+    fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)
+    for ax, mode in zip(axes, ('tensor', 'block')):
+        mode_objects = json.loads((out / f'{mode}_objects.json').read_text())
+        buckets = {}
+        for row in pair_sets[mode]:
+            if not row['cosine']:
+                continue
+            obj = mode_objects[int(row['a'])]
+            stratum = f"{len(obj['shape'])}D" if mode == 'tensor' else obj['scale']
+            buckets.setdefault(stratum, {}).setdefault(int(row['layer_distance']), []).append(float(row['cosine']))
+        for stratum, by_distance in sorted(buckets.items()):
+            distances = sorted(by_distance)
+            medians = []
+            for distance in distances:
+                values = by_distance[distance]
+                median = float(np.median(values))
+                medians.append(median)
+                distance_rows.append({'mode': mode, 'stratum': stratum, 'layer_distance': distance,
+                                      'pairs': len(values), 'median_cosine': median,
+                                      'q25': float(np.quantile(values, .25)), 'q75': float(np.quantile(values, .75))})
+            ax.plot(distances, medians, 'o-', markersize=3, label=stratum)
+        ax.set(xlabel='Absolute layer distance', ylabel='Median exact cosine',
+               title=f'{mode}: dimension / scale strata', ylim=(-.1, 1.05))
+        ax.legend(fontsize=8, loc='upper center', bbox_to_anchor=(.5, -.23), ncol=2, frameon=False)
+    fig.suptitle('Screened candidates: module mixture and selection bias still apply')
+    write_csv(out / 'layer_distance_stratified.csv', distance_rows)
+    save(fig, 'layer_distance')
+
+    null_rows = []
+    fig, axes = plt.subplots(3, 2, figsize=(12, 10))
+    bins = np.linspace(-1, 1, 26)
+    for col, mode in enumerate(('tensor', 'block')):
+        mode_objects = json.loads((out / f'{mode}_objects.json').read_text())
+        nulls = json.loads((out / f'{mode}_nulls.json').read_text())
+        real_random = json.loads((out / f'{mode}_random_pairs.json').read_text())
+        cohorts = [('shuffled', [r for r in nulls if r['method'] == 'shuffled']),
+                   ('gaussian_norm_matched', [r for r in nulls if r['method'] == 'gaussian_norm_matched']),
+                   ('random_actual_same_role', real_random)]
+        for row, ndim in enumerate((1, 2, 3)):
+            ax = axes[row, col]
+            any_values = False
+            for method, records in cohorts:
+                values = np.asarray([r['cosine'] for r in records if r['cosine'] is not None
+                                     and len(mode_objects[r['a']]['shape']) == ndim])
+                if not len(values):
+                    continue
+                any_values = True
+                values = np.clip(values, -1, 1)
+                ax.hist(values, bins=bins, density=True, histtype='step', label=f'{method} (n={len(values)})')
+                counts, _ = np.histogram(values, bins=bins)
+                for i, count in enumerate(counts):
+                    null_rows.append({'mode': mode, 'ndim': ndim, 'method': method, 'samples': len(values),
+                                      'bin_left': float(bins[i]), 'bin_right': float(bins[i + 1]),
+                                      'count': int(count), 'density': float(count / (len(values) * (bins[i + 1] - bins[i])))})
+            ax.set(title=f'{mode}: {ndim}D sampled controls', xlabel='Sampled cosine', ylabel='Density')
+            if any_values:
+                ax.legend(fontsize=7, loc='upper right')
+            else:
+                ax.text(.5, .5, 'No eligible samples', transform=ax.transAxes, ha='center')
+    fig.suptitle('Dimension-stratified controls: density normalized; role mixtures may differ')
+    write_csv(out / 'null_histograms.csv', null_rows)
+    save(fig, 'null_controls')
+
+    fig, axes = plt.subplots(1, 2, figsize=(11, 4))
+    structure = json.loads((out / 'block_structure.json').read_text())
+    for ax, ndim in zip(axes, (2, 3)):
+        probes = [m for pair in structure for m in pair['modes'] if len(m['probe_shape']) == ndim and not m.get('trivial_mode_skipped')]
+        for axis in sorted({m['axis'] for m in probes}):
+            selected = [m for m in probes if m['axis'] == axis]
+            ax.scatter([m['normalized_spectrum_distance'] for m in selected],
+                       [m['left_subspace_overlap'] for m in selected], s=18, alpha=.65, label=f'mode {axis}')
+        ax.set(title=f'{ndim}D blocks: bounded mode-unfolding probes', xlabel='Normalized spectrum distance', ylabel='Leading left subspace overlap (k <= 4)')
+        if probes:
+            ax.legend(fontsize=8, loc='upper right')
+        else:
+            ax.text(.5, .5, 'No eligible probes', transform=ax.transAxes, ha='center')
+    save(fig, 'block_structure')
+
+    parts = json.loads((out / 'block_partitions.json').read_text())
+    lines = ['# Qwen3.5-2B parameter similarity pilot', '',
+             f"- Revision: `{summary['revision']}`", f"- Stored parameters: {summary['parameter_count']:,}",
+             f"- Weight bytes: {summary['model_weight_bytes']:,}",
+             f"- Tensor dimensionalities: {summary['ndim_counts']}",
+             f"- Analysis and grouping/export wall time: {summary['elapsed_seconds']:.1f} seconds",
+             f"- Pair-comparison cutoff: {summary['analysis_budget_seconds']} seconds; final grouping/export is additional and remains under the supervisor hard limit.",
+             f"- Peak analysis RSS: {summary['peak_rss_kib'] / 1024:.1f} MiB", '',
+             '## Verified sharing estimates', '',
+             '| Representation | Weight-error limit | Verified sharing groups | Conservative total groups* | Largest group | Net model savings |',
+             '|---|---:|---:|---:|---:|---:|---:|']
+    for r in curves:
+        lines.append(f"| {r['scale']} | {float(r['threshold']):.0%} | {r['shared_groups']} | {r['conservative_partition_groups']} | {r['max_group_size']} | {float(r['net_saved_model_fraction']):.6%} |")
+    lines += ['', '*Conservative total groups counts unexamined full blocks as singletons; boundary regions and ineligible tensors stay unchanged and are outside that block count.',
+              'Savings count only verified replacement edges. Unsampled blocks remain unchanged. Different block scales must not be summed.',
+              'Theoretical encoding: one stored representative per group, 16 bytes per object mapping and 64 bytes per shared-group descriptor; identity fallback if larger.',
+              'Exact grouping uses direct replacement only. Scaled and spectral results are diagnostic.', '',
+              '## Stratified diagnostics', '',
+              'See `analysis/role_summary.csv` for same-role, same-shape statistics, separated by tensor dimensionality and block scale.',
+              'See `analysis/dimensional_bytes.csv` for tensor counts versus weight-byte contributions by dimensionality.',
+              'Heatmap missing pairs are hatched (`heatmap_pairs.csv`); identity entries are not empirical matches.',
+              'Layer-distance medians are split by dimension/scale (`layer_distance_stratified.csv`); role and selection bias remain.',
+              'Controls are split by dimension and density-normalized, with sample counts (`null_histograms.csv`); these are not significance tests.',
+              'These are screened-candidate statistics, not estimates of all possible pairs. Small normalization vectors must not be conflated with large matrices.', '',
+              '## Coverage', '']
+    for mode in ('tensor', 'block'):
+        coverage = json.loads((out / f'{mode}_coverage.json').read_text())
+        lines.append(f'- {mode}: `{json.dumps(coverage)}`')
+    lines += [f'- Complete-block partition totals (per alternative): `{json.dumps(parts)}`', '',
+              '## Interpretation limits', '', *['- ' + s for s in summary['limitations']],
+              '- Candidate histograms are selection-biased and do not represent the distribution of all tensor pairs.',
+              '- 1D normalization weights may cluster strongly but carry few bytes; do not infer material compression from group counts.',
+              '- No task accuracy, perplexity, inference speed or runtime-memory improvement has been measured.',
+              '- Stored parameter counts may omit tied aliases; no duplicate-weight equality search is performed.', '',
+              '## Figures', '', *[f'![{name}](figures/{name}.png)' for name in ['compression_frontier', 'candidate_distributions', 'layer_heatmap', 'layer_distance', 'null_controls', 'block_structure']]]
+    (root / 'REPORT.md').write_text('\n'.join(lines) + '\n')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pilot/run.py b/pilot/run.py
new file mode 100644
index 0000000..b3c0879
--- /dev/null
+++ b/pilot/run.py
@@ -0,0 +1,58 @@
+"""Supervise the one-model job; no automatic download of subsequent models."""
+import argparse
+import json
+import os
+import pathlib
+import subprocess
+import sys
+import time
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--root', type=pathlib.Path, required=True)
+    args = p.parse_args()
+    root = args.root.resolve()
+    root.mkdir(exist_ok=True, parents=True)
+    status_file = root / 'status.json'
+    start = time.time()
+    env = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='1')
+
+    def status(phase, **extra):
+        status_file.write_text(json.dumps({'phase': phase, 'updated_unix': time.time(),
+                                           'supervisor_started_unix': start, **extra}, indent=2))
+        print(phase, flush=True)
+
+    def command(module, log, timeout, extra=()):
+        with (root / log).open('w') as stream:
+            subprocess.run([sys.executable, '-B', '-m', module, '--root', str(root), *extra],
+                           check=True, timeout=timeout, env=env, stdout=stream, stderr=subprocess.STDOUT)
+
+    try:
+        status('waiting_for_download')
+        download_wait_start = time.monotonic()
+        while True:
+            manifest_path = root / 'manifest.json'
+            if manifest_path.exists():
+                manifest = json.loads(manifest_path.read_text())
+                if manifest.get('completed_unix') and all((root / 'weights' / f['name']).exists() for f in manifest['files']):
+                    break
+            if time.monotonic() - download_wait_start > 7200:
+                raise TimeoutError('Download wait exceeded 2 hours; no analysis started')
+            time.sleep(10)
+        status('analyzing', analysis_hard_limit_seconds=7200)
+        command('pilot.analyze', 'analysis.log', 7200, ('--seconds', '6600'))
+        status('plotting')
+        command('pilot.plot', 'plot.log', 300)
+        status('validating')
+        command('pilot.validate', 'validate.log', 120)
+        with (root / 'environment.txt').open('w') as f:
+            subprocess.run([sys.executable, '-B', '-m', 'pip', 'freeze'], check=True, stdout=f, env=env)
+        status('results_ready_weights_retained_until_archive_copy')
+    except Exception as e:
+        status('failed', error_type=type(e).__name__, error=str(e))
+        raise
+
+
+if __name__ == '__main__':
+    main()
diff --git a/pilot/tensors.py b/pilot/tensors.py
new file mode 100644
index 0000000..5cfd38a
--- /dev/null
+++ b/pilot/tensors.py
@@ -0,0 +1,82 @@
+"""Read safetensors BF16 directly; never instantiate a model or use pickle."""
+import json
+import math
+import mmap
+import re
+import struct
+
+import numpy as np
+
+DTYPES = {'BF16': ('<u2', 2), 'F16': ('<f2', 2), 'F32': ('<f4', 4), 'F64': ('<f8', 8)}
+
+
+def decode(raw, dtype):
+    if dtype == 'BF16':
+        return (np.asarray(raw).astype(np.uint32) << 16).view(np.float32)
+    return np.asarray(raw, dtype=np.float32)
+
+
+class Tensor:
+    def __init__(self, file, base, name, meta):
+        self.name, self.shape, self.dtype = name, tuple(meta['shape']), meta['dtype']
+        self.numel = math.prod(self.shape)
+        self.bytes = meta['data_offsets'][1] - meta['data_offsets'][0]
+        code, itemsize = DTYPES[self.dtype]
+        if self.bytes != self.numel * itemsize:
+            raise ValueError(f'Invalid byte length: {name}')
+        self.raw = np.memmap(file, mode='r', dtype=code,
+                             offset=base + meta['data_offsets'][0], shape=(self.numel,))
+        self.role = re.sub(r'\.(?:layers|blocks)\.\d+', '.layers.*', name)
+        self.role = re.sub(r'\.experts\.\d+', '.experts.*', self.role)
+        m = re.search(r'\.(?:layers|blocks)\.(\d+)', name)
+        self.layer = int(m[1]) if m else -1
+
+    def read(self, selector=slice(None)):
+        return decode(self.raw[selector], self.dtype)
+
+    def chunked(self, size=262144):
+        for start in range(0, self.numel, size):
+            yield self.read(slice(start, min(start + size, self.numel)))
+
+    def evict(self):
+        if hasattr(self.raw._mmap, 'madvise'):
+            self.raw._mmap.madvise(mmap.MADV_DONTNEED)
+
+
+def load_tensors(directory):
+    tensors = {}
+    for file in sorted(directory.glob('*.safetensors')):
+        with file.open('rb') as f:
+            size = struct.unpack('<Q', f.read(8))[0]
+            if size > 100_000_000:
+                raise ValueError('Oversized safetensors header')
+            header = json.loads(f.read(size))
+        for name, meta in header.items():
+            if name == '__metadata__':
+                continue
+            if name in tensors:
+                raise ValueError(f'Duplicate tensor name: {name}')
+            tensors[name] = Tensor(file, 8 + size, name, meta)
+    if not tensors:
+        raise ValueError('No safetensors found')
+    return tensors
+
+
+def metrics(x, y):
+    """x is member; y is representative. Float64 accumulation."""
+    x, y = x.astype(np.float64), y.astype(np.float64)
+    xx, yy, xy = float(x @ x), float(y @ y), float(x @ y)
+    delta = float((x - y) @ (x - y))
+    return finish(xx, yy, xy, delta, float(x.sum()), float(y.sum()), x.size)
+
+
+def finish(xx, yy, xy, delta, sx, sy, n):
+    tiny = 1e-30
+    vx, vy = max(0., xx - sx * sx / n), max(0., yy - sy * sy / n)
+    return {'cosine': xy / np.sqrt(xx * yy) if xx * yy > tiny else None,
+            'pearson': (xy - sx * sy / n) / np.sqrt(vx * vy) if vx * vy > tiny else None,
+            'error_member_to_rep': np.sqrt(delta / max(xx, tiny)),
+            'error_rep_to_member': np.sqrt(delta / max(yy, tiny)),
+            'symmetric_error': np.sqrt(2 * delta / max(xx + yy, tiny)),
+            'scaled_error': np.sqrt(max(0., xx - xy * xy / max(yy, tiny)) / max(xx, tiny)),
+            'scale': xy / max(yy, tiny)}
diff --git a/pilot/validate.py b/pilot/validate.py
new file mode 100644
index 0000000..895c953
--- /dev/null
+++ b/pilot/validate.py
@@ -0,0 +1,85 @@
+"""Validate archived analysis before allowing deletion of downloaded weights."""
+import argparse
+import csv
+import hashlib
+import json
+import pathlib
+import shutil
+import time
+
+
+def validate(root):
+    analysis = root / 'analysis'
+    summary = json.loads((analysis / 'summary.json').read_text())
+    assert summary['completed']
+    manifest = json.loads((root / 'manifest.json').read_text())
+    assert summary['revision'] == manifest['revision']
+    for mode in ('tensor', 'block'):
+        objects = json.loads((analysis / f'{mode}_objects.json').read_text())
+        with (analysis / f'{mode}_pairs.csv').open() as f:
+            pairs = list(csv.DictReader(f))
+        coverage = json.loads((analysis / f'{mode}_coverage.json').read_text())
+        assert len(pairs) == coverage['pairs_verified']
+        edges = {}
+        for p in pairs:
+            a, b = int(p['a']), int(p['b'])
+            assert objects[a]['shape'] == objects[b]['shape']
+            assert objects[a]['role'] == objects[b]['role']
+            edges[(b, a)] = float(p['error_member_to_rep'])
+            edges[(a, b)] = float(p['error_rep_to_member'])
+        for file in analysis.glob(f'groups_{mode}_*.json'):
+            threshold = float(file.stem.rsplit('_', 1)[1])
+            groups = json.loads(file.read_text())
+            members = [i for g in groups for i in g['members']]
+            assert len(set(members)) == len(members), file
+            for g in groups:
+                assert g['representative'] in g['members']
+                for member in g['members']:
+                    if member != g['representative']:
+                        assert edges[(g['representative'], member)] <= threshold + 1e-12
+                assert g['max_error'] <= threshold + 1e-12
+    assert (root / 'REPORT.md').is_file()
+    images = list((root / 'figures').glob('*.png'))
+    assert len(images) >= 5
+    for file in images:
+        assert file.read_bytes()[:8] == b'\x89PNG\r\n\x1a\n'
+    checksums = {}
+    for file in sorted(root.rglob('*')):
+        if (file.is_file() and 'weights' not in file.relative_to(root).parts
+                and file.name not in ('checksums.json', 'validation.json', 'cleanup.json', 'status.json')
+                and not file.name.endswith('.log')):
+            checksums[str(file.relative_to(root))] = hashlib.sha256(file.read_bytes()).hexdigest()
+    (root / 'checksums.json').write_text(json.dumps(checksums, indent=2))
+    result = {'validated_unix': time.time(), 'files_checksummed': len(checksums),
+              'group_edges_checked': True, 'passed': True}
+    (root / 'validation.json').write_text(json.dumps(result, indent=2))
+    return result
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--root', required=True, type=pathlib.Path)
+    p.add_argument('--cleanup-weights', action='store_true')
+    args = p.parse_args()
+    root = args.root.resolve()
+    result = validate(root)
+    if args.cleanup_weights:
+        weights = root / 'weights'
+        manifest = json.loads((root / 'manifest.json').read_text())
+        assert not weights.is_symlink()
+        assert (weights / '.pilot-owned').read_text() == manifest['repo']
+        expected_names = {entry['name'] for entry in manifest['files']} | {'.pilot-owned'}
+        assert {file.name for file in weights.iterdir()} == expected_names
+        assert all(file.is_file() and not file.is_symlink() for file in weights.iterdir())
+        deleted_bytes = sum(file.stat().st_size for file in weights.iterdir())
+        shutil.rmtree(weights)
+        (root / 'cleanup.json').write_text(json.dumps({'deleted_unix': time.time(),
+                                                      'deleted_bytes': deleted_bytes,
+                                                      'deleted_directory': 'weights',
+                                                      'reason': 'User-authorized delete-after-analysis; archived results validated'}, indent=2))
+        result['weights_removed'] = not weights.exists()
+    print(json.dumps(result))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..b2b9779
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+numpy==2.5.4
+matplotlib==3.11.2
diff --git a/results/qwen35-2b/REPORT.md b/results/qwen35-2b/REPORT.md
new file mode 100644
index 0000000..2c306c3
--- /dev/null
+++ b/results/qwen35-2b/REPORT.md
@@ -0,0 +1,83 @@
+# Qwen3.5-2B parameter similarity pilot
+
+- Revision: `15852e8c16360a2fea060d615a32b45270f8a8fc`
+- Stored parameters: 2,274,069,824
+- Weight bytes: 4,548,144,832
+- Tensor dimensionalities: {'1': 319, '2': 294, '3': 18, '5': 1}
+- Analysis and grouping/export wall time: 6661.1 seconds
+- Pair-comparison cutoff: 6600 seconds; final grouping/export is additional and remains under the supervisor hard limit.
+- Peak analysis RSS: 201.3 MiB
+
+## Verified sharing estimates
+
+| Representation | Weight-error limit | Verified sharing groups | Conservative total groups* | Largest group | Net model savings |
+|---|---:|---:|---:|---:|---:|---:|
+| whole | 1% | 0 | 632 | 1 | 0.000000% |
+| whole | 2% | 0 | 632 | 1 | 0.000000% |
+| whole | 5% | 1 | 631 | 2 | 0.000000% |
+| whole | 10% | 11 | 613 | 6 | 0.000719% |
+| whole | 20% | 16 | 572 | 15 | 0.002626% |
+| 2d-128 | 1% | 0 | 138576 | 1 | 0.000000% |
+| 2d-128 | 2% | 0 | 138576 | 1 | 0.000000% |
+| 2d-128 | 5% | 0 | 138576 | 1 | 0.000000% |
+| 2d-128 | 10% | 0 | 138576 | 1 | 0.000000% |
+| 2d-128 | 20% | 1 | 138575 | 2 | 0.000000% |
+| 2d-64 | 1% | 0 | 554304 | 1 | 0.000000% |
+| 2d-64 | 2% | 0 | 554304 | 1 | 0.000000% |
+| 2d-64 | 5% | 0 | 554304 | 1 | 0.000000% |
+| 2d-64 | 10% | 1 | 554303 | 2 | 0.000000% |
+| 2d-64 | 20% | 3 | 554299 | 3 | 0.000000% |
+| 3d-axis0-slice | 1% | 0 | 110592 | 1 | 0.000000% |
+| 3d-axis0-slice | 2% | 0 | 110592 | 1 | 0.000000% |
+| 3d-axis0-slice | 5% | 9 | 110581 | 3 | 0.000000% |
+| 3d-axis0-slice | 10% | 33 | 110542 | 7 | 0.000000% |
+| 3d-axis0-slice | 20% | 45 | 110483 | 12 | 0.000000% |
+| 3d-volume | 1% | 0 | 27648 | 1 | 0.000000% |
+| 3d-volume | 2% | 0 | 27648 | 1 | 0.000000% |
+| 3d-volume | 5% | 0 | 27648 | 1 | 0.000000% |
+| 3d-volume | 10% | 0 | 27648 | 1 | 0.000000% |
+| 3d-volume | 20% | 2 | 27646 | 2 | 0.000000% |
+
+*Conservative total groups counts unexamined full blocks as singletons; boundary regions and ineligible tensors stay unchanged and are outside that block count.
+Savings count only verified replacement edges. Unsampled blocks remain unchanged. Different block scales must not be summed.
+Theoretical encoding: one stored representative per group, 16 bytes per object mapping and 64 bytes per shared-group descriptor; identity fallback if larger.
+Exact grouping uses direct replacement only. Scaled and spectral results are diagnostic.
+
+## Stratified diagnostics
+
+See `analysis/role_summary.csv` for same-role, same-shape statistics, separated by tensor dimensionality and block scale.
+See `analysis/dimensional_bytes.csv` for tensor counts versus weight-byte contributions by dimensionality.
+Heatmap missing pairs are hatched (`heatmap_pairs.csv`); identity entries are not empirical matches.
+Layer-distance medians are split by dimension/scale (`layer_distance_stratified.csv`); role and selection bias remain.
+Controls are split by dimension and density-normalized, with sample counts (`null_histograms.csv`); these are not significance tests.
+These are screened-candidate statistics, not estimates of all possible pairs. Small normalization vectors must not be conflated with large matrices.
+
+## Coverage
+
+- tensor: `{"eligible_pairs_in_screened_objects": 6159, "candidate_pairs_before_cap": 4724, "selected_pairs": 2000, "objects_screened": 632, "pairs_verified": 2000, "timed_out": false, "screen_sample_elements": 2048, "candidate_neighbors_per_metric": 10}`
+- block: `{"eligible_pairs_in_screened_objects": 1678968, "candidate_pairs_before_cap": 175036, "selected_pairs": 20000, "objects_screened": 13328, "pairs_verified": 14778, "timed_out": true, "screen_sample_elements": 2048, "candidate_neighbors_per_metric": 10}`
+- Complete-block partition totals (per alternative): `{"2d-64": {"blocks": 554304, "bytes": 4540858368, "edge_bytes": 0}, "2d-128": {"blocks": 138576, "bytes": 4540858368, "edge_bytes": 0}, "3d-axis0-slice": {"blocks": 110592, "bytes": 884736, "edge_bytes": 0}, "3d-volume": {"blocks": 27648, "bytes": 884736, "edge_bytes": 0}}`
+
+## Interpretation limits
+
+- 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
+- Candidate histograms are selection-biased and do not represent the distribution of all tensor pairs.
+- 1D normalization weights may cluster strongly but carry few bytes; do not infer material compression from group counts.
+- No task accuracy, perplexity, inference speed or runtime-memory improvement has been measured.
+- Stored parameter counts may omit tied aliases; no duplicate-weight equality search is performed.
+
+## Figures
+
+![compression_frontier](figures/compression_frontier.png)
+![candidate_distributions](figures/candidate_distributions.png)
+![layer_heatmap](figures/layer_heatmap.png)
+![layer_distance](figures/layer_distance.png)
+![null_controls](figures/null_controls.png)
+![block_structure](figures/block_structure.png)
diff --git a/results/qwen35-2b/analysis-run.txt b/results/qwen35-2b/analysis-run.txt
new file mode 100644
index 0000000..8596b22
--- /dev/null
+++ b/results/qwen35-2b/analysis-run.txt
@@ -0,0 +1,171 @@
+Inventory: 632 tensors, 4548144832 bytes
+tensor: sampling 632 objects
+tensor: verified 100/2000
+tensor: verified 200/2000
+tensor: verified 300/2000
+tensor: verified 400/2000
+tensor: verified 500/2000
+tensor: verified 600/2000
+tensor: verified 700/2000
+tensor: verified 800/2000
+tensor: verified 900/2000
+tensor: verified 1000/2000
+tensor: verified 1100/2000
+tensor: verified 1200/2000
+tensor: verified 1300/2000
+tensor: verified 1400/2000
+tensor: verified 1500/2000
+tensor: verified 1600/2000
+tensor: verified 1700/2000
+tensor: verified 1800/2000
+tensor: verified 1900/2000
+tensor: verified 2000/2000
+block: sampling 13328 objects
+block: verified 100/20000
+block: verified 200/20000
+block: verified 300/20000
+block: verified 400/20000
+block: verified 500/20000
+block: verified 600/20000
+block: verified 700/20000
+block: verified 800/20000
+block: verified 900/20000
+block: verified 1000/20000
+block: verified 1100/20000
+block: verified 1200/20000
+block: verified 1300/20000
+block: verified 1400/20000
+block: verified 1500/20000
+block: verified 1600/20000
+block: verified 1700/20000
+block: verified 1800/20000
+block: verified 1900/20000
+block: verified 2000/20000
+block: verified 2100/20000
+block: verified 2200/20000
+block: verified 2300/20000
+block: verified 2400/20000
+block: verified 2500/20000
+block: verified 2600/20000
+block: verified 2700/20000
+block: verified 2800/20000
+block: verified 2900/20000
+block: verified 3000/20000
+block: verified 3100/20000
+block: verified 3200/20000
+block: verified 3300/20000
+block: verified 3400/20000
+block: verified 3500/20000
+block: verified 3600/20000
+block: verified 3700/20000
+block: verified 3800/20000
+block: verified 3900/20000
+block: verified 4000/20000
+block: verified 4100/20000
+block: verified 4200/20000
+block: verified 4300/20000
+block: verified 4400/20000
+block: verified 4500/20000
+block: verified 4600/20000
+block: verified 4700/20000
+block: verified 4800/20000
+block: verified 4900/20000
+block: verified 5000/20000
+block: verified 5100/20000
+block: verified 5200/20000
+block: verified 5300/20000
+block: verified 5400/20000
+block: verified 5500/20000
+block: verified 5600/20000
+block: verified 5700/20000
+block: verified 5800/20000
+block: verified 5900/20000
+block: verified 6000/20000
+block: verified 6100/20000
+block: verified 6200/20000
+block: verified 6300/20000
+block: verified 6400/20000
+block: verified 6500/20000
+block: verified 6600/20000
+block: verified 6700/20000
+block: verified 6800/20000
+block: verified 6900/20000
+block: verified 7000/20000
+block: verified 7100/20000
+block: verified 7200/20000
+block: verified 7300/20000
+block: verified 7400/20000
+block: verified 7500/20000
+block: verified 7600/20000
+block: verified 7700/20000
+block: verified 7800/20000
+block: verified 7900/20000
+block: verified 8000/20000
+block: verified 8100/20000
+block: verified 8200/20000
+block: verified 8300/20000
+block: verified 8400/20000
+block: verified 8500/20000
+block: verified 8600/20000
+block: verified 8700/20000
+block: verified 8800/20000
+block: verified 8900/20000
+block: verified 9000/20000
+block: verified 9100/20000
+block: verified 9200/20000
+block: verified 9300/20000
+block: verified 9400/20000
+block: verified 9500/20000
+block: verified 9600/20000
+block: verified 9700/20000
+block: verified 9800/20000
+block: verified 9900/20000
+block: verified 10000/20000
+block: verified 10100/20000
+block: verified 10200/20000
+block: verified 10300/20000
+block: verified 10400/20000
+block: verified 10500/20000
+block: verified 10600/20000
+block: verified 10700/20000
+block: verified 10800/20000
+block: verified 10900/20000
+block: verified 11000/20000
+block: verified 11100/20000
+block: verified 11200/20000
+block: verified 11300/20000
+block: verified 11400/20000
+block: verified 11500/20000
+block: verified 11600/20000
+block: verified 11700/20000
+block: verified 11800/20000
+block: verified 11900/20000
+block: verified 12000/20000
+block: verified 12100/20000
+block: verified 12200/20000
+block: verified 12300/20000
+block: verified 12400/20000
+block: verified 12500/20000
+block: verified 12600/20000
+block: verified 12700/20000
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+block: verified 13000/20000
+block: verified 13100/20000
+block: verified 13200/20000
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+block: verified 13500/20000
+block: verified 13600/20000
+block: verified 13700/20000
+block: verified 13800/20000
+block: verified 13900/20000
+block: verified 14000/20000
+block: verified 14100/20000
+block: verified 14200/20000
+block: verified 14300/20000
+block: verified 14400/20000
+block: verified 14500/20000
+block: verified 14600/20000
+block: verified 14700/20000
+{"model": "Qwen/Qwen3.5-2B", "revision": "15852e8c16360a2fea060d615a32b45270f8a8fc", "tensor_count": 632, "parameter_count": 2274069824, "model_weight_bytes": 4548144832, "ndim_counts": {"1": 319, "2": 294, "3": 18, "5": 1}, "elapsed_seconds": 6661.129063449334, "analysis_budget_seconds": 6600, "peak_rss_kib": 206180, "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}
diff --git a/results/qwen35-2b/analysis/block_coverage.json b/results/qwen35-2b/analysis/block_coverage.json
new file mode 100644
index 0000000..60d8c39
--- /dev/null
+++ b/results/qwen35-2b/analysis/block_coverage.json
@@ -0,0 +1,10 @@
+{
+  "eligible_pairs_in_screened_objects": 1678968,
+  "candidate_pairs_before_cap": 175036,
+  "selected_pairs": 20000,
+  "objects_screened": 13328,
+  "pairs_verified": 14778,
+  "timed_out": true,
+  "screen_sample_elements": 2048,
+  "candidate_neighbors_per_metric": 10
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/analysis/block_nulls.json b/results/qwen35-2b/analysis/block_nulls.json
new file mode 100644
index 0000000..b6b72d7
--- /dev/null
+++ b/results/qwen35-2b/analysis/block_nulls.json
@@ -0,0 +1,2602 @@
+[
+  {
+    "a": 167,
+    "b": 246,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": -0.02647632773380728,
+    "pearson": -0.031023068491706936,
+    "error_member_to_rep": 1.4560343846498822,
+    "error_rep_to_member": 1.4106503787493936,
+    "symmetric_error": 1.4328035867812066,
+    "scaled_error": 0.9996494405889157,
+    "scale": -0.02565107125170619
+  },
+  {
+    "a": 167,
+    "b": 246,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": -0.0007268034841842627,
+    "pearson": -0.0012872123599931657,
+    "error_member_to_rep": 1.4376648541580126,
+    "error_rep_to_member": 1.392853419131551,
+    "symmetric_error": 1.414727139237307,
+    "scaled_error": 0.9999997358783128,
+    "scale": -0.0007041493120283998
+  },
+  {
+    "a": 423,
+    "b": 496,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": 0.10874201917719165,
+    "pearson": 0.05091253855905693,
+    "error_member_to_rep": 1.1648902532656287,
+    "error_rep_to_member": 1.626882064923666,
+    "symmetric_error": 1.3394438955850516,
+    "scaled_error": 0.9940700042075846,
+    "scale": 0.15186876206321706
+  },
+  {
+    "a": 423,
+    "b": 496,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": -0.0076609245533804365,
+    "pearson": -0.007640852415879383,
+    "error_member_to_rep": 1.2343680274431665,
+    "error_rep_to_member": 1.7239145058796967,
+    "symmetric_error": 1.4193326063370328,
+    "scaled_error": 0.9999706546869199,
+    "scale": -0.010699223142856838
+  },
+  {
+    "a": 545,
+    "b": 718,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": 0.03422792942247514,
+    "pearson": -0.3816553354821003,
+    "error_member_to_rep": 1.3912321786669533,
+    "error_rep_to_member": 1.3883724117515182,
+    "symmetric_error": 1.3898000885297888,
+    "scaled_error": 0.999414052756639,
+    "scale": 0.03415757171967959
+  },
+  {
+    "a": 545,
+    "b": 718,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": 0.7242510321942823,
+    "pearson": 0.9021528689641812,
+    "error_member_to_rep": 0.7433963631895391,
+    "error_rep_to_member": 0.7418682643163939,
+    "symmetric_error": 0.7426311346273677,
+    "scaled_error": 0.6895363966938343,
+    "scale": 0.7227622877761337
+  },
+  {
+    "a": 904,
+    "b": 1005,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": 0.21729444222596106,
+    "pearson": 0.14160396193142386,
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+    "error_rep_to_member": 1.1570453565882464,
+    "symmetric_error": 1.253818725073538,
+    "scaled_error": 0.976106103544952,
+    "scale": 0.18221441672632047
+  },
+  {
+    "a": 904,
+    "b": 1005,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": 0.07913146420731042,
+    "pearson": 0.10339552747645542,
+    "error_member_to_rep": 1.4944475696026718,
+    "error_rep_to_member": 1.2531838800554658,
+    "symmetric_error": 1.3579981163461117,
+    "scaled_error": 0.9968641890309868,
+    "scale": 0.06635647671209555
+  },
+  {
+    "a": 1103,
+    "b": 1150,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": -0.03909898185129978,
+    "pearson": -0.03902010797464815,
+    "error_member_to_rep": 1.1098146667658475,
+    "error_rep_to_member": 2.5005579430322453,
+    "symmetric_error": 1.4345695362974515,
+    "scaled_error": 0.999235342458518,
+    "scale": -0.0880951320616931
+  },
+  {
+    "a": 1103,
+    "b": 1150,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": -0.01574018588596636,
+    "pearson": -0.015725161909525116,
+    "error_member_to_rep": 1.1004335847986113,
+    "error_rep_to_member": 2.479421135482415,
+    "symmetric_error": 1.4224433545026802,
+    "scaled_error": 0.9998761156004654,
+    "scale": -0.035464702369320346
+  },
+  {
+    "a": 1434,
+    "b": 1503,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": -0.008573446494254212,
+    "pearson": -0.007539630733282313,
+    "error_member_to_rep": 1.0873264223568002,
+    "error_rep_to_member": 2.598438519576801,
+    "symmetric_error": 1.4185245855092943,
+    "scaled_error": 0.9999632473322259,
+    "scale": -0.020488395350417195
+  },
+  {
+    "a": 1434,
+    "b": 1503,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": 0.013418136997976834,
+    "pearson": 0.01407471721831338,
+    "error_member_to_rep": 1.0788298411912456,
+    "error_rep_to_member": 2.5781338131599054,
+    "symmetric_error": 1.4074399571692688,
+    "scaled_error": 0.9999099727472986,
+    "scale": 0.032065995380603805
+  },
+  {
+    "a": 1569,
+    "b": 1706,
+    "method": "shuffled",
+    "sampled": true,
+    "cosine": 0.020397326533339102,
+    "pearson": 0.02067642581404081,
+    "error_member_to_rep": 2.22422705917342,
+    "error_rep_to_member": 1.1080941068496288,
+    "symmetric_error": 1.4026529281373024,
+    "scaled_error": 0.9997919528933469,
+    "scale": 0.010161803055970451
+  },
+  {
+    "a": 1569,
+    "b": 1706,
+    "method": "gaussian_norm_matched",
+    "sampled": true,
+    "cosine": 0.0003321817956529278,
+    "pearson": -0.0002098772761235782,
+    "error_member_to_rep": 2.242261742370197,
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diff --git a/results/qwen35-2b/analysis/block_objects.json b/results/qwen35-2b/analysis/block_objects.json
new file mode 100644
index 0000000..af68801
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diff --git a/results/qwen35-2b/analysis/block_pairs.csv b/results/qwen35-2b/analysis/block_pairs.csv
new file mode 100644
index 0000000..c7df91e
--- /dev/null
+++ b/results/qwen35-2b/analysis/block_pairs.csv
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diff --git a/results/qwen35-2b/analysis/block_partitions.json b/results/qwen35-2b/analysis/block_partitions.json
new file mode 100644
index 0000000..dc01cad
--- /dev/null
+++ b/results/qwen35-2b/analysis/block_partitions.json
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+}
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diff --git a/results/qwen35-2b/analysis/block_random_pairs.json b/results/qwen35-2b/analysis/block_random_pairs.json
new file mode 100644
index 0000000..162b05b
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new file mode 100644
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\ No newline at end of file
diff --git a/results/qwen35-2b/analysis/compression_curve.csv b/results/qwen35-2b/analysis/compression_curve.csv
new file mode 100644
index 0000000..9bce254
--- /dev/null
+++ b/results/qwen35-2b/analysis/compression_curve.csv
@@ -0,0 +1,26 @@
+mode,scale,threshold,objects,groups,total_partition_objects,unexamined_partition_objects,conservative_partition_groups,shared_groups,max_group_size,verified_shared_bytes,examined_object_bytes,representative_bytes,mapping_bytes_if_encoded,net_saved_bytes,net_saved_model_fraction,max_replacement_error
+tensor,whole,0.01,632,632,632,0,632,0,1,0,4548144832,4548144832,10112,0,0.0,0.0
+tensor,whole,0.02,632,632,632,0,632,0,1,0,4548144832,4548144832,10112,0,0.0,0.0
+tensor,whole,0.05,632,631,632,0,631,1,2,4096,4548144832,4548142784,10176,0,0.0,0.04982565601433354
+tensor,whole,0.1,632,613,632,0,613,11,6,66560,4548144832,4548101312,10816,32704,7.19062413533976e-06,0.0996284846101826
+tensor,whole,0.2,632,572,632,0,572,16,15,174592,4548144832,4548014272,11136,119424,2.6257739014763196e-05,0.1992427130434979
+block,2d-128,0.01,6160,6160,138576,132416,138576,0,1,0,201850880,201850880,98560,0,0.0,0.0
+block,2d-128,0.02,6160,6160,138576,132416,138576,0,1,0,201850880,201850880,98560,0,0.0,0.0
+block,2d-128,0.05,6160,6160,138576,132416,138576,0,1,0,201850880,201850880,98560,0,0.0,0.0
+block,2d-128,0.1,6160,6160,138576,132416,138576,0,1,0,201850880,201850880,98560,0,0.0,0.0
+block,2d-128,0.2,6160,6159,138576,132416,138575,1,2,65536,201850880,201818112,98624,0,0.0,0.1894907479098447
+block,2d-64,0.01,6656,6656,554304,547648,554304,0,1,0,54525952,54525952,106496,0,0.0,0.0
+block,2d-64,0.02,6656,6656,554304,547648,554304,0,1,0,54525952,54525952,106496,0,0.0,0.0
+block,2d-64,0.05,6656,6656,554304,547648,554304,0,1,0,54525952,54525952,106496,0,0.0,0.0
+block,2d-64,0.1,6656,6655,554304,547648,554303,1,2,16384,54525952,54517760,106560,0,0.0,0.09362662282834061
+block,2d-64,0.2,6656,6651,554304,547648,554299,3,3,65536,54525952,54484992,106688,0,0.0,0.18661711940726244
+block,3d-axis0-slice,0.01,256,256,110592,110336,110592,0,1,0,2048,2048,4096,0,0.0,0.0
+block,3d-axis0-slice,0.02,256,256,110592,110336,110592,0,1,0,2048,2048,4096,0,0.0,0.0
+block,3d-axis0-slice,0.05,256,245,110592,110336,110581,9,3,160,2048,1960,4672,0,0.0,0.04716472920859973
+block,3d-axis0-slice,0.1,256,206,110592,110336,110542,33,7,664,2048,1648,6208,0,0.0,0.09947536518147226
+block,3d-axis0-slice,0.2,256,147,110592,110336,110483,45,12,1232,2048,1176,6976,0,0.0,0.19719308219928766
+block,3d-volume,0.01,256,256,27648,27392,27648,0,1,0,8192,8192,4096,0,0.0,0.0
+block,3d-volume,0.02,256,256,27648,27392,27648,0,1,0,8192,8192,4096,0,0.0,0.0
+block,3d-volume,0.05,256,256,27648,27392,27648,0,1,0,8192,8192,4096,0,0.0,0.0
+block,3d-volume,0.1,256,256,27648,27392,27648,0,1,0,8192,8192,4096,0,0.0,0.0
+block,3d-volume,0.2,256,254,27648,27392,27646,2,2,128,8192,8128,4224,0,0.0,0.19802419391338302
diff --git a/results/qwen35-2b/analysis/dimensional_bytes.csv b/results/qwen35-2b/analysis/dimensional_bytes.csv
new file mode 100644
index 0000000..bf7a220
--- /dev/null
+++ b/results/qwen35-2b/analysis/dimensional_bytes.csv
@@ -0,0 +1,5 @@
+ndim,tensor_count,weight_bytes,model_byte_fraction
+1,319,896704,0.00019715818935468765
+2,294,4543217664,0.9989166642264043
+3,18,884736,0.00019452678678461222
+5,1,3145728,0.000691650797456399
diff --git a/results/qwen35-2b/analysis/groups_block_2d-128_0.01.json b/results/qwen35-2b/analysis/groups_block_2d-128_0.01.json
new file mode 100644
index 0000000..aa59d6e
--- /dev/null
+++ b/results/qwen35-2b/analysis/groups_block_2d-128_0.01.json
@@ -0,0 +1,43122 @@
+[
+  {
+    "representative": 236,
+    "members": [
+      236
+    ],
+    "max_error": 0.0
+  },
+  {
+    "representative": 193,
+    "members": [
+      193
+    ],
+    "max_error": 0.0
+  },
+  {
+    "representative": 42,
+    "members": [
+      42
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+    "max_error": 0.0
+  },
+  {
+    "representative": 255,
+    "members": [
+      255
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+    "max_error": 0.0
+  },
+  {
+    "representative": 60,
+    "members": [
+      60
+    ],
+    "max_error": 0.0
+  },
+  {
+    "representative": 5,
+    "members": [
+      5
+    ],
+    "max_error": 0.0
+  },
+  {
+    "representative": 126,
+    "members": [
+      126
+    ],
+    "max_error": 0.0
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new file mode 100644
index 0000000..e88c63d
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diff --git a/results/qwen35-2b/analysis/groups_block_3d-axis0-slice_0.10.json b/results/qwen35-2b/analysis/groups_block_3d-axis0-slice_0.10.json
new file mode 100644
index 0000000..4b5e5e7
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diff --git a/results/qwen35-2b/analysis/groups_block_3d-axis0-slice_0.20.json b/results/qwen35-2b/analysis/groups_block_3d-axis0-slice_0.20.json
new file mode 100644
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new file mode 100644
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diff --git a/results/qwen35-2b/analysis/groups_block_3d-volume_0.20.json b/results/qwen35-2b/analysis/groups_block_3d-volume_0.20.json
new file mode 100644
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diff --git a/results/qwen35-2b/analysis/inventory.csv b/results/qwen35-2b/analysis/inventory.csv
new file mode 100644
index 0000000..6f4b027
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diff --git a/results/qwen35-2b/analysis/layer_distance_stratified.csv b/results/qwen35-2b/analysis/layer_distance_stratified.csv
new file mode 100644
index 0000000..43b557e
--- /dev/null
+++ b/results/qwen35-2b/analysis/layer_distance_stratified.csv
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diff --git a/results/qwen35-2b/analysis/null_histograms.csv b/results/qwen35-2b/analysis/null_histograms.csv
new file mode 100644
index 0000000..68c25c2
--- /dev/null
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diff --git a/results/qwen35-2b/analysis/role_summary.csv b/results/qwen35-2b/analysis/role_summary.csv
new file mode 100644
index 0000000..8895a2e
--- /dev/null
+++ b/results/qwen35-2b/analysis/role_summary.csv
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diff --git a/results/qwen35-2b/analysis/summary.json b/results/qwen35-2b/analysis/summary.json
new file mode 100644
index 0000000..e5ea3b9
--- /dev/null
+++ b/results/qwen35-2b/analysis/summary.json
@@ -0,0 +1,30 @@
+{
+  "model": "Qwen/Qwen3.5-2B",
+  "revision": "15852e8c16360a2fea060d615a32b45270f8a8fc",
+  "tensor_count": 632,
+  "parameter_count": 2274069824,
+  "model_weight_bytes": 4548144832,
+  "ndim_counts": {
+    "1": 319,
+    "2": 294,
+    "3": 18,
+    "5": 1
+  },
+  "elapsed_seconds": 6661.129063449334,
+  "analysis_budget_seconds": 6600,
+  "peak_rss_kib": 206180,
+  "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
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/analysis/tensor_coverage.json b/results/qwen35-2b/analysis/tensor_coverage.json
new file mode 100644
index 0000000..0671007
--- /dev/null
+++ b/results/qwen35-2b/analysis/tensor_coverage.json
@@ -0,0 +1,10 @@
+{
+  "eligible_pairs_in_screened_objects": 6159,
+  "candidate_pairs_before_cap": 4724,
+  "selected_pairs": 2000,
+  "objects_screened": 632,
+  "pairs_verified": 2000,
+  "timed_out": false,
+  "screen_sample_elements": 2048,
+  "candidate_neighbors_per_metric": 10
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/analysis/tensor_nulls.json b/results/qwen35-2b/analysis/tensor_nulls.json
new file mode 100644
index 0000000..1d75887
--- /dev/null
+++ b/results/qwen35-2b/analysis/tensor_nulls.json
@@ -0,0 +1,2602 @@
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diff --git a/results/qwen35-2b/analysis/tensor_objects.json b/results/qwen35-2b/analysis/tensor_objects.json
new file mode 100644
index 0000000..5510e4e
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diff --git a/results/qwen35-2b/analysis/tensor_pairs.csv b/results/qwen35-2b/analysis/tensor_pairs.csv
new file mode 100644
index 0000000..c36c14b
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diff --git a/results/qwen35-2b/analysis/tensor_random_pairs.json b/results/qwen35-2b/analysis/tensor_random_pairs.json
new file mode 100644
index 0000000..feb261a
--- /dev/null
+++ b/results/qwen35-2b/analysis/tensor_random_pairs.json
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diff --git a/results/qwen35-2b/analysis/tensor_structure.json b/results/qwen35-2b/analysis/tensor_structure.json
new file mode 100644
index 0000000..1ed7d41
--- /dev/null
+++ b/results/qwen35-2b/analysis/tensor_structure.json
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\ No newline at end of file
diff --git a/results/qwen35-2b/cache-plan.json b/results/qwen35-2b/cache-plan.json
new file mode 100644
index 0000000..2ed86ea
--- /dev/null
+++ b/results/qwen35-2b/cache-plan.json
@@ -0,0 +1,6 @@
+{
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+  "status": "metadata-only estimate for future SOP; no cache allocated in this run"
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/checksums.json b/results/qwen35-2b/checksums.json
new file mode 100644
index 0000000..ff1c3db
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diff --git a/results/qwen35-2b/cleanup.json b/results/qwen35-2b/cleanup.json
new file mode 100644
index 0000000..93d4706
--- /dev/null
+++ b/results/qwen35-2b/cleanup.json
@@ -0,0 +1,6 @@
+{
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+  "deleted_bytes": 4548351685,
+  "deleted_directory": "weights",
+  "reason": "User-authorized delete-after-analysis; archived results validated"
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/code_checksums.txt b/results/qwen35-2b/code_checksums.txt
new file mode 100644
index 0000000..11f0d3d
--- /dev/null
+++ b/results/qwen35-2b/code_checksums.txt
@@ -0,0 +1,10 @@
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diff --git a/results/qwen35-2b/download-progress.txt b/results/qwen35-2b/download-progress.txt
new file mode 100644
index 0000000..80e6b96
--- /dev/null
+++ b/results/qwen35-2b/download-progress.txt
@@ -0,0 +1,67 @@
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diff --git a/results/qwen35-2b/environment.txt b/results/qwen35-2b/environment.txt
new file mode 100644
index 0000000..eccd5d5
--- /dev/null
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diff --git a/results/qwen35-2b/execution-environment.json b/results/qwen35-2b/execution-environment.json
new file mode 100644
index 0000000..f64c6bb
--- /dev/null
+++ b/results/qwen35-2b/execution-environment.json
@@ -0,0 +1,18 @@
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diff --git a/results/qwen35-2b/figures/block_structure.pdf b/results/qwen35-2b/figures/block_structure.pdf
new file mode 100644
index 0000000..4a12de2
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diff --git a/results/qwen35-2b/figures/block_structure.png b/results/qwen35-2b/figures/block_structure.png
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diff --git a/results/qwen35-2b/figures/block_structure.svg b/results/qwen35-2b/figures/block_structure.svg
new file mode 100644
index 0000000..a4e7ff8
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diff --git a/results/qwen35-2b/manifest.json b/results/qwen35-2b/manifest.json
new file mode 100644
index 0000000..50f60b1
--- /dev/null
+++ b/results/qwen35-2b/manifest.json
@@ -0,0 +1,34 @@
+{
+  "repo": "Qwen/Qwen3.5-2B",
+  "revision": "15852e8c16360a2fea060d615a32b45270f8a8fc",
+  "expected_bytes": 4548351670,
+  "started_unix": 1794156349.8216345,
+  "files": [
+    {
+      "name": "README.md",
+      "bytes": 62814,
+      "sha256": "c0e83a849c776e6fa843d011f023132d942a0f0140d903205bf1c363adad2275",
+      "upstream_sha256": null
+    },
+    {
+      "name": "config.json",
+      "bytes": 2908,
+      "sha256": "ed1c1723241f23f7f4e23430759cbd7dcfb4103cbdfe052bfe7626b57c2615b4",
+      "upstream_sha256": null
+    },
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+      "name": "model.safetensors-00001-of-00001.safetensors",
+      "bytes": 4548221488,
+      "sha256": "aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1",
+      "upstream_sha256": "aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1"
+    },
+    {
+      "name": "model.safetensors.index.json",
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+      "sha256": "aca8afed9da75b0f050b408d270766fd77627f1af401e240f61c3b47d0db02f9",
+      "upstream_sha256": null
+    }
+  ],
+  "license": "apache-2.0",
+  "completed_unix": 1794159787.5429547
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/model_config.json b/results/qwen35-2b/model_config.json
new file mode 100644
index 0000000..d30a15b
--- /dev/null
+++ b/results/qwen35-2b/model_config.json
@@ -0,0 +1,96 @@
+{
+    "architectures": [
+        "Qwen3_5ForConditionalGeneration"
+    ],
+    "image_token_id": 248056,
+    "model_type": "qwen3_5",
+    "text_config": {
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "attn_output_gate": true,
+        "dtype": "bfloat16",
+        "eos_token_id": 248044,
+        "full_attention_interval": 4,
+        "head_dim": 256,
+        "hidden_act": "silu",
+        "hidden_size": 2048,
+        "initializer_range": 0.02,
+        "intermediate_size": 6144,
+        "layer_types": [
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention"
+        ],
+        "linear_conv_kernel_dim": 4,
+        "linear_key_head_dim": 128,
+        "linear_num_key_heads": 16,
+        "linear_num_value_heads": 16,
+        "linear_value_head_dim": 128,
+        "max_position_embeddings": 262144,
+        "mlp_only_layers": [],
+        "model_type": "qwen3_5_text",
+        "mtp_num_hidden_layers": 1,
+        "mtp_use_dedicated_embeddings": false,
+        "num_attention_heads": 8,
+        "num_hidden_layers": 24,
+        "num_key_value_heads": 2,
+        "rms_norm_eps": 1e-06,
+        "tie_word_embeddings": true,
+        "use_cache": true,
+        "vocab_size": 248320,
+        "mamba_ssm_dtype": "float32",
+        "rope_parameters": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                11,
+                11,
+                10
+            ],
+            "rope_type": "default",
+            "rope_theta": 10000000,
+            "partial_rotary_factor": 0.25
+        }
+    },
+    "tie_word_embeddings": true,
+    "transformers_version": "4.57.0.dev0",
+    "video_token_id": 248057,
+    "vision_config": {
+        "deepstack_visual_indexes": [],
+        "depth": 24,
+        "hidden_act": "gelu_pytorch_tanh",
+        "hidden_size": 1024,
+        "in_channels": 3,
+        "initializer_range": 0.02,
+        "intermediate_size": 4096,
+        "model_type": "qwen3_5",
+        "num_heads": 16,
+        "num_position_embeddings": 2304,
+        "out_hidden_size": 2048,
+        "patch_size": 16,
+        "spatial_merge_size": 2,
+        "temporal_patch_size": 2
+    },
+    "vision_end_token_id": 248054,
+    "vision_start_token_id": 248053
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/numerical-qc.json b/results/qwen35-2b/numerical-qc.json
new file mode 100644
index 0000000..63939e7
--- /dev/null
+++ b/results/qwen35-2b/numerical-qc.json
@@ -0,0 +1,19 @@
+{
+  "passed": true,
+  "checked_unix": 1794167980.8863053,
+  "numerical_tables": {
+    "tensor": {
+      "rows": 2000,
+      "finite_metrics": true,
+      "coverage_matches": true
+    },
+    "block": {
+      "rows": 14778,
+      "finite_metrics": true,
+      "coverage_matches": true
+    }
+  },
+  "final_tests": 6,
+  "final_test_seconds": 77.193,
+  "earlier_parallel_test_timeout_seconds": 300
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/plot-run.txt b/results/qwen35-2b/plot-run.txt
new file mode 100644
index 0000000..e69de29
diff --git a/results/qwen35-2b/resource-observation.txt b/results/qwen35-2b/resource-observation.txt
new file mode 100644
index 0000000..3cf10db
--- /dev/null
+++ b/results/qwen35-2b/resource-observation.txt
@@ -0,0 +1,5 @@
+procs -----------memory---------- ---swap-- -----io---- -system-- -------cpu------- -----timestamp-----
+ r  b   swpd   free   buff  cache   si   so    bi    bo   in   cs us sy id wa st gu                 CST
+ 0 18 2802460 145460   6576 1115112  143  162  2885   442 2946   19  7  4 86  2  0  0 2026-11-09 02:21:08
+ 0 18 2799748 101732   6080 1160132 4920 2256 104468  2468 6759 7057 10 15  0 75  0  0 2026-11-09 02:21:09
+ 0 22 2803528  89596   5284 1169020 1768 5308 114616  5364 6557 6835  3 12  0 85  0  0 2026-11-09 02:21:10
diff --git a/results/qwen35-2b/source_metadata.json b/results/qwen35-2b/source_metadata.json
new file mode 100644
index 0000000..c5722ba
--- /dev/null
+++ b/results/qwen35-2b/source_metadata.json
@@ -0,0 +1,261 @@
+{
+  "_id": "69a380a0ae63980df575326b",
+  "id": "Qwen/Qwen3.5-2B",
+  "private": false,
+  "pipeline_tag": "image-text-to-text",
+  "library_name": "transformers",
+  "tags": [
+    "transformers",
+    "safetensors",
+    "qwen3_5",
+    "image-text-to-text",
+    "conversational",
+    "base_model:Qwen/Qwen3.5-2B-Base",
+    "base_model:finetune:Qwen/Qwen3.5-2B-Base",
+    "license:apache-2.0",
+    "eval-results",
+    "endpoints_compatible",
+    "deploy:sagemaker",
+    "deploy:azure",
+    "region:us"
+  ],
+  "downloads": 3648202,
+  "likes": 473,
+  "modelId": "Qwen/Qwen3.5-2B",
+  "author": "Qwen",
+  "sha": "15852e8c16360a2fea060d615a32b45270f8a8fc",
+  "lastModified": "2026-03-02T11:26:29.000Z",
+  "gated": false,
+  "disabled": false,
+  "widgetData": [
+    {
+      "text": "Hi, what can you help me with?"
+    },
+    {
+      "text": "What is 84 * 3 / 2?"
+    },
+    {
+      "text": "Tell me an interesting fact about the universe!"
+    },
+    {
+      "text": "Explain quantum computing in simple terms."
+    }
+  ],
+  "model-index": null,
+  "config": {
+    "architectures": [
+      "Qwen3_5ForConditionalGeneration"
+    ],
+    "model_type": "qwen3_5",
+    "tokenizer_config": {
+      "bos_token": null,
+      "eos_token": "<|im_end|>",
+      "unk_token": null,
+      "pad_token": "<|endoftext|>",
+      "chat_template": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n    {%- if content is string %}\n        {{- content }}\n    {%- elif content is iterable and content is not mapping %}\n        {%- for item in content %}\n            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain images.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n            {%- elif 'video' in item or item.type == 'video' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain videos.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n            {%- elif 'text' in item %}\n                {{- item.text }}\n            {%- else %}\n                {{- raise_exception('Unexpected item type in content.') }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif content is none or content is undefined %}\n        {{- '' }}\n    {%- else %}\n        {{- raise_exception('Unexpected content type.') }}\n    {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n    {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n    {{- '<|im_start|>system\\n' }}\n    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '\\n\\n' + content }}\n        {%- endif %}\n    {%- endif %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" %}\n        {%- set content = render_content(message.content, false)|trim %}\n        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n            {%- set ns.multi_step_tool = false %}\n            {%- set ns.last_query_index = index %}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n    {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n    {%- set content = render_content(message.content, true)|trim %}\n    {%- if message.role == \"system\" %}\n        {%- if not loop.first %}\n            {{- raise_exception('System message must be at the beginning.') }}\n        {%- endif %}\n    {%- elif message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- set reasoning_content = reasoning_content|trim %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if tool_call.function is defined %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {%- if loop.first %}\n                    {%- if content|trim %}\n                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- else %}\n                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- endif %}\n                {%- else %}\n                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                {%- endif %}\n                {%- if tool_call.arguments is defined %}\n                    {%- for args_name, args_value in tool_call.arguments|items %}\n                        {{- '<parameter=' + args_name + '>\\n' }}\n                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n                        {{- args_value }}\n                        {{- '\\n</parameter>\\n' }}\n                    {%- endfor %}\n                {%- endif %}\n                {{- '</function>\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- raise_exception('Unexpected message role.') }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is true %}\n        {{- '<think>\\n' }}\n    {%- else %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+    },
+    "chat_template_jinja": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n    {%- if content is string %}\n        {{- content }}\n    {%- elif content is iterable and content is not mapping %}\n        {%- for item in content %}\n            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain images.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n            {%- elif 'video' in item or item.type == 'video' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain videos.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n            {%- elif 'text' in item %}\n                {{- item.text }}\n            {%- else %}\n                {{- raise_exception('Unexpected item type in content.') }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif content is none or content is undefined %}\n        {{- '' }}\n    {%- else %}\n        {{- raise_exception('Unexpected content type.') }}\n    {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n    {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n    {{- '<|im_start|>system\\n' }}\n    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '\\n\\n' + content }}\n        {%- endif %}\n    {%- endif %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" %}\n        {%- set content = render_content(message.content, false)|trim %}\n        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n            {%- set ns.multi_step_tool = false %}\n            {%- set ns.last_query_index = index %}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n    {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n    {%- set content = render_content(message.content, true)|trim %}\n    {%- if message.role == \"system\" %}\n        {%- if not loop.first %}\n            {{- raise_exception('System message must be at the beginning.') }}\n        {%- endif %}\n    {%- elif message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- set reasoning_content = reasoning_content|trim %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if tool_call.function is defined %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {%- if loop.first %}\n                    {%- if content|trim %}\n                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- else %}\n                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- endif %}\n                {%- else %}\n                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                {%- endif %}\n                {%- if tool_call.arguments is defined %}\n                    {%- for args_name, args_value in tool_call.arguments|items %}\n                        {{- '<parameter=' + args_name + '>\\n' }}\n                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n                        {{- args_value }}\n                        {{- '\\n</parameter>\\n' }}\n                    {%- endfor %}\n                {%- endif %}\n                {{- '</function>\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- raise_exception('Unexpected message role.') }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is true %}\n        {{- '<think>\\n' }}\n    {%- else %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+  },
+  "cardData": {
+    "library_name": "transformers",
+    "license": "apache-2.0",
+    "license_link": "https://huggingface.co/Qwen/Qwen3.5-2B/blob/main/LICENSE",
+    "pipeline_tag": "image-text-to-text",
+    "base_model": [
+      "Qwen/Qwen3.5-2B-Base"
+    ]
+  },
+  "transformersInfo": {
+    "auto_model": "AutoModelForMultimodalLM",
+    "pipeline_tag": "image-text-to-text",
+    "processor": "AutoProcessor"
+  },
+  "siblings": [
+    {
+      "rfilename": ".gitattributes",
+      "blobId": "52373fe24473b1aa44333d318f578ae6bf04b49b",
+      "size": 1570
+    },
+    {
+      "rfilename": "LICENSE",
+      "blobId": "f938136e3adacfd92be087f6e113b5d6d97f678f",
+      "size": 11544
+    },
+    {
+      "rfilename": "README.md",
+      "blobId": "2efa49d346c547ea81cd3d1310f414df25934f45",
+      "size": 62814
+    },
+    {
+      "rfilename": "chat_template.jinja",
+      "blobId": "0ef09f214eaa6d9bca297988afc1454b5827b2c7",
+      "size": 7755
+    },
+    {
+      "rfilename": "config.json",
+      "blobId": "d30a15be1bbfd610f58ad80f1fbeb6778f58a80f",
+      "size": 2908
+    },
+    {
+      "rfilename": "merges.txt",
+      "blobId": "a494e019ca1502219fd0128658b979e5f05ae8e8",
+      "size": 3353259
+    },
+    {
+      "rfilename": "model.safetensors-00001-of-00001.safetensors",
+      "blobId": "3868ead7f1658210f24643fa927afa7d9717083c",
+      "size": 4548221488,
+      "lfs": {
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+      }
+    },
+    {
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+      "blobId": "69a41758e1138259916d08b99b56fada441fd502",
+      "size": 64460
+    },
+    {
+      "rfilename": "preprocessor_config.json",
+      "blobId": "2ea84a437d448ff71b08df68fdd949d5cc4ebb64",
+      "size": 390
+    },
+    {
+      "rfilename": "tokenizer.json",
+      "blobId": "a73a846725794819aa6e1c9e97d8dc9671c2006d",
+      "size": 12807982,
+      "lfs": {
+        "sha256": "5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42",
+        "size": 12807982,
+        "pointerSize": 133
+      }
+    },
+    {
+      "rfilename": "tokenizer_config.json",
+      "blobId": "fae3ce993e07c092ad024dde45e592379fde91bb",
+      "size": 16709
+    },
+    {
+      "rfilename": "video_preprocessor_config.json",
+      "blobId": "3ba673a5ad7d4d13f54155ecd38b2a94a6dac8fe",
+      "size": 385
+    },
+    {
+      "rfilename": "vocab.json",
+      "blobId": "0aa0ce0658d60ac4a5d609f4eadb0e8e43514176",
+      "size": 6722759
+    }
+  ],
+  "spaces": [
+    "pliny-the-prompter/obliteratus",
+    "webml-community/Qwen3.5-WebGPU",
+    "prithivMLmods/SAM3-Plus-Qwen3.5",
+    "artificialguybr/Video-Transcription-Smart-Summary",
+    "embedl/hfviewer",
+    "artificialguybr/qwen-chat-demo",
+    "Qwen/QwenScope",
+    "build-small-hackathon/LocalDuo",
+    "dqy08/InfoLens1",
+    "openenv/coding_env",
+    "remyxai/Feature-Finder",
+    "amaljoe88/vision-coder-openenv",
+    "openenv/echo_env",
+    "prithivMLmods/Multimodal-Edge-Node",
+    "yuhangzang/Valen-Preview-0923",
+    "emanforhelp/model-pulse",
+    "openenv/atari_env",
+    "openenv/openspiel_env",
+    "dumbordumber/obliteratus",
+    "Kodacoda/obliteratus",
+    "isahsn/MultyModelsChatbot",
+    "openenv/sudoku",
+    "rbinrs/obliteratus",
+    "kicfk/obliteratus",
+    "Chama29929/flan-t5-small-chat",
+    "Ev3Dev/Bio-EnvRL",
+    "aaahmet/obliteratus",
+    "Deadmon/obliteratus",
+    "Ngixdev/obliteratus",
+    "Silavon/Qwen3.5-WebGPU",
+    "z4iXsECHS6ND-heighTSAI-INSEGCSyX6T-SYS-ROD/Qwen3.5-WebGPU",
+    "Dexifried/dex-swarm-engine",
+    "Carnot-EBM/hallucination-detector",
+    "tacomilezdeeepdale/QwenScope",
+    "jokernifty/IND-blogger",
+    "Wstanislawek/RxAI-Micro-2B-Chat-Demo",
+    "build-small-hackathon/blind-quill",
+    "Girlz/TokenTrace",
+    "hugging-apps/internvla-a1-5-vla-explorer",
+    "RavichandranJ/obliteratus",
+    "S-Dreamer/obliteratus",
+    "AlanaSky/asgaard",
+    "marchkamal51/obliteratus",
+    "AdithyaSK/NayanaOCRBench",
+    "nolancassidy/s1v-decision-explorer",
+    "Steveeeeeeen/smallest-s2s",
+    "hugging-apps/locus-2b-grounding",
+    "openenv/chat_env",
+    "openenv/sumo_rl_env",
+    "openenv/tbench2",
+    "openenv/browsergym_env",
+    "openenv/wordle",
+    "Nt-animes/1.7b",
+    "cw4444/obliteratus",
+    "kalmuahlaunda/obliteratus",
+    "ankushthehacker5/Qwen3.5-WebGPU",
+    "sub314xxl/obliteratus",
+    "mastodonitis/obliteratus",
+    "GrozniMilitq/obliteratus",
+    "liminal4423/obliteratus",
+    "aiqtech/Qwen3.5-WebGPU",
+    "Marcop4/Qwen3.5-WebGPU",
+    "duarffodog/obliteratus",
+    "Joaco2802/ProductDescription",
+    "Joaco2802/Product_Clasification",
+    "cypher109/obliteratus",
+    "Xero1/Qwen-3.5-HF-Demo",
+    "Ev3Dev/hackathon",
+    "nagose/qw2b",
+    "milkygingers/obliteratus",
+    "TOpNeT7/obliteratus",
+    "kirikir13/obliteratus",
+    "flwa/ohmy2",
+    "jellewas/pii-nl-bench",
+    "RaccoonStampede/obliteratus",
+    "ntcuong2103/Qwen-3.5-Multimodal-Demo",
+    "bofbofai/Qwen3.5-WebGPU",
+    "highkay/ohmycaptcha",
+    "jellewas/gdpr-lora-v2train",
+    "zbq111/flow",
+    "dragg2/mycaptcha",
+    "Hk4crprasad/email-triage-env",
+    "impresionist/OBLITERATUS",
+    "dragggl25/ohmycaptchaa",
+    "zzdccww/zzdccww-ohmycaptcha",
+    "bresseporix22/obliteratus",
+    "Sendpns/obliteratus",
+    "oceanicdayi/Qwen3.5-WebGPU",
+    "digifreely/chatmessenger",
+    "huezcy/ohmycaptcha",
+    "mehraz203/zzdccww-ohmycaptcha",
+    "Valtry/Qwen",
+    "jessy38472/obliteratus",
+    "error5920/ohmycaptcha",
+    "eyalnof123/football-fan-space",
+    "epixdonkey/obliteratus",
+    "ymonoce/obliteratus",
+    "sidmaz666/qwen3.5api",
+    "Mattral/Qwen3.5-WebGPU",
+    "ehsaaniqbal/circuit-detective"
+  ],
+  "createdAt": "2026-02-28T23:56:16.000Z",
+  "safetensors": {
+    "parameters": {
+      "F32": 2592,
+      "BF16": 2274067232
+    },
+    "total": 2274069824
+  },
+  "usedStorage": 4561029470
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/status.json b/results/qwen35-2b/status.json
new file mode 100644
index 0000000..90ec011
--- /dev/null
+++ b/results/qwen35-2b/status.json
@@ -0,0 +1,6 @@
+{
+  "phase": "archived_weights_deleted",
+  "updated_unix": 1794167040.9455411,
+  "supervisor_started_unix": 1794156953.8715413,
+  "weights_present": false
+}
\ No newline at end of file
diff --git a/results/qwen35-2b/supervisor-run.txt b/results/qwen35-2b/supervisor-run.txt
new file mode 100644
index 0000000..b277b82
--- /dev/null
+++ b/results/qwen35-2b/supervisor-run.txt
@@ -0,0 +1,5 @@
+waiting_for_download
+analyzing
+plotting
+validating
+results_ready_weights_retained_until_archive_copy
diff --git a/results/qwen35-2b/test-results.txt b/results/qwen35-2b/test-results.txt
new file mode 100644
index 0000000..98c1a21
--- /dev/null
+++ b/results/qwen35-2b/test-results.txt
@@ -0,0 +1,17 @@
+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": 0.33063589688390493, "analysis_budget_seconds": 60, "peak_rss_kib": 50188, "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 6 tests in 77.193s
+
+OK
diff --git a/results/qwen35-2b/tests-during-io-timeout.txt b/results/qwen35-2b/tests-during-io-timeout.txt
new file mode 100644
index 0000000..f84ab20
--- /dev/null
+++ b/results/qwen35-2b/tests-during-io-timeout.txt
@@ -0,0 +1,8 @@
+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": 0.16227321792393923, "analysis_budget_seconds": 60, "peak_rss_kib": 49700, "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}
diff --git a/results/qwen35-2b/validation.json b/results/qwen35-2b/validation.json
new file mode 100644
index 0000000..74867f3
--- /dev/null
+++ b/results/qwen35-2b/validation.json
@@ -0,0 +1,6 @@
+{
+  "validated_unix": 1794167982.261223,
+  "files_checksummed": 81,
+  "group_edges_checked": true,
+  "passed": true
+}
\ No newline at end of file
diff --git a/tests/test_pilot.py b/tests/test_pilot.py
new file mode 100644
index 0000000..0bb7419
--- /dev/null
+++ b/tests/test_pilot.py
@@ -0,0 +1,132 @@
+import json
+import io
+import pathlib
+import struct
+import tempfile
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+
+from pilot.analyze import (build_blocks, exact_pair, object_record, read_object,
+                           sample_object, sharing_groups)
+from pilot.tensors import decode, load_tensors, metrics
+
+
+class PilotTests(unittest.TestCase):
+    def test_directional_replacement_error_and_scaling(self):
+        x = np.array([1., 2., 3.])
+        m = metrics(x, 2 * x)
+        self.assertAlmostEqual(m['cosine'], 1.)
+        self.assertAlmostEqual(m['error_member_to_rep'], 1.)
+        self.assertAlmostEqual(m['error_rep_to_member'], .5)
+        self.assertAlmostEqual(m['scaled_error'], 0.)
+        self.assertAlmostEqual(m['scale'], .5)
+
+    def test_bfloat16(self):
+        raw = np.array([0x3f80, 0xc000, 0x0000], dtype='<u2')
+        np.testing.assert_array_equal(decode(raw, 'BF16'), [1., -2., 0.])
+
+    def test_ranged_download_preserves_prefix(self):
+        from pilot.download import ranged_download
+        data = b'the entire known checkpoint contents'
+        class Response(io.BytesIO):
+            status = 206
+        def request(req, timeout):
+            span = req.headers['Range'].split('=')[1]
+            a, b = map(int, span.split('-'))
+            response = Response(data[a:b + 1])
+            response.headers = {'Content-Range': f'bytes {a}-{b}/{len(data)}'}
+            return response
+        with tempfile.TemporaryDirectory() as td:
+            part = pathlib.Path(td) / 'file.partial'
+            part.write_bytes(data[:7])
+            with patch('pilot.download.urllib.request.urlopen', side_effect=request):
+                ranged_download('https://example.invalid/file', part, len(data), workers=2)
+            self.assertEqual(part.read_bytes(), data)
+            self.assertFalse(part.with_suffix('.partial.ranges.json').exists())
+
+    def test_no_transitive_chaining(self):
+        objects = [{'id': str(i)} for i in range(3)]
+        pairs = [{'a': 0, 'b': 1, 'error_member_to_rep': .01, 'error_rep_to_member': .5},
+                 {'a': 1, 'b': 2, 'error_member_to_rep': .01, 'error_rep_to_member': .5}]
+        groups = sharing_groups(objects, pairs, .02)
+        self.assertFalse(any(set(g['members']) == {0, 1, 2} for g in groups))
+        self.assertTrue(all(g['max_error'] <= .02 for g in groups))
+
+    def test_3d_reader_and_exact_block(self):
+        x = np.arange(8 * 4 * 4, dtype='<f4').reshape(8, 4, 4)
+        names = ['model.layers.0.conv.weight', 'model.layers.1.conv.weight']
+        header = {name: {'dtype': 'F32', 'shape': list(x.shape),
+                         'data_offsets': [i * x.nbytes, (i + 1) * x.nbytes]}
+                  for i, name in enumerate(names)}
+        encoded = json.dumps(header).encode()
+        with tempfile.TemporaryDirectory() as td:
+            path = pathlib.Path(td)
+            (path / 'model.safetensors').write_bytes(struct.pack('<Q', len(encoded)) + encoded + x.tobytes() * 2)
+            tensors = load_tensors(path)
+            a = object_record(tensors[names[0]], 'block', (4, 2, 2), (4, 2, 2), '3d')
+            b = object_record(tensors[names[1]], 'block', (4, 2, 2), (4, 2, 2), '3d')
+            np.testing.assert_array_equal(read_object(a, tensors), x[4:8, 2:4, 2:4].ravel())
+            np.testing.assert_array_equal(sample_object(a, tensors), read_object(a, tensors))
+            self.assertEqual(exact_pair(a, b, tensors, float('inf'))['error_member_to_rep'], 0.)
+            blocks, coverage, totals = build_blocks(tensors, 1, float('inf'), 2)
+            self.assertTrue(blocks)
+            self.assertTrue(all(r['sampled_blocks'] <= 2 for r in coverage))
+            self.assertTrue(all(r['bytes'] + r['edge_bytes'] == x.nbytes * 2 for r in totals.values()))
+
+    def test_end_to_end_synthetic_groups_and_savings(self):
+        from pilot.analyze import main
+        arrays = {}
+        for layer in (0, 1):
+            factor = 1 + layer * .005
+            arrays[f'model.layers.{layer}.proj.weight'] = (np.arange(64 * 64, dtype='<f4').reshape(64, 64) + 1) * factor
+            arrays[f'model.layers.{layer}.conv.weight'] = (np.arange(8 * 4 * 4, dtype='<f4').reshape(8, 4, 4) + 1) * factor
+        header, payload, offset = {}, [], 0
+        for name, x in arrays.items():
+            header[name] = {'dtype': 'F32', 'shape': list(x.shape), 'data_offsets': [offset, offset + x.nbytes]}
+            payload.append(x.tobytes())
+            offset += x.nbytes
+        encoded = json.dumps(header).encode()
+        with tempfile.TemporaryDirectory() as td:
+            root = pathlib.Path(td)
+            (root / 'weights').mkdir()
+            (root / 'weights/model.safetensors').write_bytes(struct.pack('<Q', len(encoded)) + encoded + b''.join(payload))
+            (root / 'manifest.json').write_text(json.dumps({'repo': 'synthetic', 'revision': 'test'}))
+            with patch('sys.argv', ['analyze', '--root', str(root), '--seconds', '60']):
+                main()
+            summary = json.loads((root / 'analysis/summary.json').read_text())
+            self.assertTrue(summary['completed'])
+            groups = json.loads((root / 'analysis/groups_tensor_whole_0.01.json').read_text())
+            self.assertEqual(len(groups), 2)
+            self.assertTrue(all(len(g['members']) == 2 for g in groups))
+            self.assertTrue(all(g['max_error'] < .01 for g in groups))
+            from pilot.plot import main as plot_main
+            from pilot.validate import validate
+            with patch('sys.argv', ['plot', '--root', str(root)]):
+                plot_main()
+            self.assertTrue(validate(root)['passed'])
+            self.assertEqual(len(list((root / 'figures').glob('*.pdf'))), 6)
+            import csv
+            from collections import defaultdict
+            with (root / 'analysis/null_histograms.csv').open() as f:
+                bins = list(csv.DictReader(f))
+            areas = defaultdict(float)
+            counts = defaultdict(int)
+            sizes = {}
+            for row in bins:
+                key = (row['mode'], row['ndim'], row['method'])
+                areas[key] += float(row['density']) * (float(row['bin_right']) - float(row['bin_left']))
+                counts[key] += int(row['count'])
+                sizes[key] = int(row['samples'])
+            self.assertTrue(areas)
+            for key in areas:
+                self.assertAlmostEqual(areas[key], 1.)
+                self.assertEqual(counts[key], sizes[key])
+            with (root / 'analysis/heatmap_pairs.csv').open() as f:
+                cells = list(csv.DictReader(f))
+            self.assertTrue(all(row['verified'] == 'False' for row in cells if row['identity'] == 'True'))
+
+
+if __name__ == '__main__':
+    unittest.main()