Add full experimental v3/v4 embedding tables

metadata/capacitance-operator-v3.parquet

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+size 12304175

metadata/planar-terminal-graph-v4.parquet

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+size 13672718

models/capacitance-operator-v3/release-manifest.json

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+{
+  "design_options_used": false,
+  "dimensions": 256,
+  "duplicate_layout_ids": 1,
+  "elapsed_seconds": 651.6458279590006,
+  "embedding_model": "capacitance-operator-v3",
+  "embedding_schema_version": "3.0.0-experimental",
+  "encoding_failures": 0,
+  "environment": {
+    "metal_thickness_um": 0.25,
+    "relative_permittivity": 11.45,
+    "substrate_thickness_um": 350.0,
+    "vacuum_height_um": 1000.0
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+  "families": {
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+    "GeneralizedCapNInterdigital": 13683,
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+  "schema_sha256": "36eae36ab20e2eda8d0b62f37d05aa2bc20cb587bab5f016fc734a8a8bce1d25",
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+  "source_code_repository": "LFL-Lab/SQuADDS",
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+  "source_layout_revision": "78f0b913c210f1c49fbb4495db0108efb4f2b9e7",
+  "status": "experimental-review-candidate",
+  "table": "metadata/capacitance-operator-v3.parquet",
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+  "terminal_counts": {
+    "2": 16511
+  }
+}

models/capacitance-operator-v3/schema.json

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+{
+  "blocks": {
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+      "dimensions": 16,
+      "offset": 240,
+      "values": [
+        "relative_permittivity",
+        "effective_relative_permittivity",
+        "log1p_substrate_thickness_um",
+        "substrate_thickness_per_scale",
+        "log1p_metal_thickness_um",
+        "metal_thickness_per_scale",
+        "log1p_vacuum_height_um",
+        "vacuum_height_per_scale",
+        "in_plane_permittivity_x",
+        "in_plane_permittivity_y",
+        "out_of_plane_permittivity",
+        "in_plane_isotropic",
+        "stack_available",
+        "ground_reference_available",
+        "reserved_environment_0",
+        "reserved_environment_1"
+      ]
+    },
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+      "offset": 136,
+      "values_per_pair": 16
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+    "grounded_electrostatic_proxy": {
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+        "signed_log1p_scaled_proxy_eigenvalue_2_fF",
+        "signed_log1p_scaled_proxy_trace_fF",
+        "log1p_scaled_proxy_mutual_01_fF",
+        "log1p_scaled_proxy_terminal_0_ground_fF",
+        "log1p_scaled_proxy_terminal_1_ground_fF",
+        "log1p_green_condition_number",
+        "electrostatic_role_count",
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+        "electrostatic_proxy_available"
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+        "terminal_0_perimeter_fraction",
+        "terminal_1_perimeter_fraction",
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+        "terminal_1_compactness",
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+        "log1p_minimum_terminal_gap_um",
+        "minimum_terminal_gap_per_scale",
+        "log1p_median_boundary_distance_um",
+        "median_boundary_distance_per_scale",
+        "log1p_p90_boundary_distance_um",
+        "p90_boundary_distance_per_scale",
+        "log1p_terminal_0_ground_gap_um",
+        "log1p_terminal_1_ground_gap_um",
+        "terminal_0_ground_gap_per_scale",
+        "terminal_1_ground_gap_per_scale",
+        "terminal_0_boundary_eccentricity",
+        "terminal_1_boundary_eccentricity",
+        "terminal_0_radial_dispersion_per_scale",
+        "terminal_1_radial_dispersion_per_scale",
+        "terminal_union_component_count",
+        "terminal_union_hole_count",
+        "log1p_local_ground_boundary_length_um",
+        "local_ground_boundary_length_per_scale",
+        "ground_available",
+        "absolute_terminal_area_asymmetry",
+        "absolute_terminal_perimeter_asymmetry",
+        "terminal_area_balance"
+      ]
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+      "role_pairs": [
+        "terminal_0-terminal_1",
+        "terminal_0-ground",
+        "terminal_1-ground"
+      ]
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+      "offset": 184,
+      "radii_per_scale": [
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+        0.3981071705534973,
+        0.6309573444801936,
+        1.0
+      ]
+    }
+  },
+  "dimensions": 256,
+  "embedding_schema_version": "3.0.0-experimental",
+  "experimental": true,
+  "fitted_on_catalogue": false,
+  "limitations": [
+    "The grounded BEM block is a scale-lifted 2-D low-fidelity proxy, not a converged 3-D solve.",
+    "GDS quantization and finite boundary quadrature make arbitrary-angle equality numerical rather than bitwise.",
+    "Anisotropic material orientation must be supplied and transformed with the layout."
+  ],
+  "model": "capacitance-operator-v3",
+  "query_conditioned": true,
+  "recommended_views": {
+    "complete": {
+      "dimensions": 256,
+      "slice": [
+        0,
+        256
+      ],
+      "use_when": "stack variables change and stack-held-out validation is available"
+    },
+    "fixed_stack_core": {
+      "dimensions": 240,
+      "slice": [
+        0,
+        240
+      ],
+      "use_when": "the substrate and metal stack are fixed across the study"
+    }
+  },
+  "simulation_results_used": false,
+  "symmetry_contract": {
+    "outer_domain_frame": "invariant; only moat-facing ground rings enter physical features",
+    "reflection": "invariant for parity-symmetric material and boundary conditions",
+    "rotation": "invariant for isotropic in-plane material when geometry and domain transform together",
+    "scale": "shape channels invariant; absolute scale and stack ratios remain explicit",
+    "terminal_relabeling": "equivariant; terminal-specific channels and capacitance rows/columns permute",
+    "translation": "invariant for the complete vector"
+  }
+}

models/planar-terminal-graph-v4/release-manifest.json

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+{
+  "design_options_used": false,
+  "dimensions": 544,
+  "duplicate_layout_ids": 1,
+  "elapsed_seconds": 651.6458279590006,
+  "embedding_model": "planar-terminal-graph-v4",
+  "embedding_schema_version": "4.0.0-experimental",
+  "encoding_failures": 0,
+  "families": {
+    "CapNInterdigitalTee": 894,
+    "GeneralizedCapNInterdigital": 13683,
+    "TransmonCross": 1934
+  },
+  "missing_source_layouts": 6379,
+  "rows": 16511,
+  "schema_sha256": "16659f58c9fa480e06918d20c4d434930a9fef2a92527f5705d36cf89382e197",
+  "simulation_results_used": false,
+  "source_code_commit": "ed205da2ef435fa2232f1161e3625f6c8b13a4e3",
+  "source_code_repository": "LFL-Lab/SQuADDS",
+  "source_code_sha256": {
+    "scripts/stage_v3_v4_embedding_release.py": "342efbc4852cf3240e04f663f9697a33cfea6223907c2b1f37da850897774cdb",
+    "squadds/layouts/geometry_v3.py": "69bfc3a8f12978648ca965f0338c1a1516d6e521bdfa4a222424bb4aaf0c8da6",
+    "squadds/layouts/geometry_v4.py": "2fb482ea5b158a9f58599d456926da21808cc64b10e75fb8e90e56c2f02bc2e1"
+  },
+  "source_layout_manifest_sha256": "5798b30d127c86bc7c400a1854ec978ed5cc6163d7e9e9a39009a877ab743ad0",
+  "source_layout_repository": "SQuADDS/SQuADDS_Layouts",
+  "source_layout_revision": "78f0b913c210f1c49fbb4495db0108efb4f2b9e7",
+  "status": "experimental-review-candidate",
+  "table": "metadata/planar-terminal-graph-v4.parquet",
+  "table_sha256": "f557974cd500851d823802eccae73c83f646c1fa0f32eb93debc2ac45d35b7f5",
+  "terminal_counts": {
+    "2": 16511
+  },
+  "view": "permutation-invariant-layout"
+}

models/planar-terminal-graph-v4/schema.json

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+{
+  "canonical_representation": {
+    "edge_features": [
+      "terminal_count",
+      "terminal_count",
+      96
+    ],
+    "global_features": [
+      64
+    ],
+    "node_features": [
+      "terminal_count",
+      64
+    ],
+    "terminal_count_cap": null
+  },
+  "derived_views": {
+    "hybrid_pair": {
+      "blocks": {
+        "all_other_conductor_context": 192,
+        "v3_fixed_stack_pair_core": 240
+      },
+      "dimensions": 432,
+      "recommended_for": "ordinary regressors and planar capacitance transfer"
+    },
+    "layout": {
+      "dimensions": 544,
+      "permutation_invariant": true
+    },
+    "pair": {
+      "dimensions": 480,
+      "endpoint_swap_invariant": true,
+      "other_terminal_permutation_invariant": true
+    }
+  },
+  "embedding_schema_version": "4.0.0-experimental",
+  "experimental": true,
+  "fitted_on_catalogue": false,
+  "future_layer_stack_contract": {
+    "current_behavior": "reject multiple physical conductor planes rather than emit misleading features",
+    "format": "versioned JSON or XML mapped into a common internal representation",
+    "required_fields": [
+      "GDS layer/datatype",
+      "plane z",
+      "material",
+      "thickness",
+      "terminal net"
+    ]
+  },
+  "implemented_geometry": "single-plane planar",
+  "limitations": [
+    "The pooled layout view is diagnostic and is not mathematically injective over all possible layouts.",
+    "The boundary operator is a deterministic two-dimensional low-fidelity feature, not simulation truth.",
+    "GDS alone cannot determine terminal nets, floating status, materials, or physical layer heights."
+  ],
+  "model": "planar-terminal-graph-v4",
+  "simulation_results_used": false,
+  "symmetry_contract": {
+    "in_plane_rotation": "invariant for isotropic planar materials",
+    "reflection": "invariant for parity-symmetric planar materials",
+    "scale": "normalized shape and absolute physical measurements are both retained",
+    "terminal_relabeling": "graph equivariant; layout and corresponding pair views invariant",
+    "translation": "invariant"
+  }
+}

releases/v3-v4-2026-09-09/README.md

@@ -0,0 +1,30 @@
+# Experimental v3/v4 review candidate — 2026-09-09
+
+This review bundle adds full, label-free embedding tables for every currently
+downloadable GDS file in the three port-complete families used by Tutorials 20–24.
+
+| representation | width | rows | view |
+|---|---:|---:|---|
+| `capacitance-operator-v3` | 256 | 16,511 | ordered terminal pair (0, 1) |
+| `planar-terminal-graph-v4` | 544 | 16,511 | permutation-invariant layout |
+
+Family coverage is 13,683 `GeneralizedCapNInterdigital`, 1,934
+`TransmonCross`, and 894 `CapNInterdigitalTee` layouts. Every encoded row has
+exactly two discovered terminals, the expected vector width, and finite values.
+No simulation result or fitted catalogue statistic is an encoder input.
+
+The layout manifest contains 6,379 additional `GeneralizedCapNInterdigital`
+rows whose GDS bytes are not downloadable from the layout repository. They are
+listed in `unavailable-layouts.parquet` and are not represented by placeholder
+vectors. One known duplicate Transmon layout identity is retained because the
+two source records are both part of the published dataset.
+
+The schema and per-model release manifests record the exact source layout
+revision, source manifest checksum, encoder code commit and checksums, table
+checksums, physical stack inputs for v3, coverage, and failure counts. The
+encoder source is `LFL-Lab/SQuADDS` commit
+`ed205da2ef435fa2232f1161e3625f6c8b13a4e3` in GitHub PR #67.
+
+These schemas remain explicitly experimental. This upload is a Hugging Face
+pull request for review; merging and promotion are intentionally left to the
+maintainer.

releases/v3-v4-2026-09-09/unavailable-layouts.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e496e4df6c11ca7b49bdaad755a1c8e4876032ba0c2feb1fbcbcb5e01c7ee151
+size 263331

releases/v3-v4-2026-09-09/validation-report.json

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+{
+  "all_encoded_layouts_two_terminal": true,
+  "all_vectors_expected_width": true,
+  "all_vectors_finite": true,
+  "available_rows": 16511,
+  "encoded_rows": 16511,
+  "failed_rows": 0,
+  "missing_rows": 6379,
+  "missing_rows_are_unpublished_manifest_entries": true,
+  "passed": true
+}

studies/expanded-capacitance-transfer-v2/EXECUTIVE_SUMMARY.md

@@ -0,0 +1,216 @@
+# Expanded capacitance-transfer study — executive summary
+
+## Decision
+
+The new datasets change the strength and scope of the result, but not the core
+physics conclusion.
+
+- **Strong evidence:** the frozen v4 layout representation transfers across
+  parameter campaigns of the same Generalized IDC topology.
+- **Useful but conditional evidence:** source pretraining reduces the number of
+  target labels needed for some cross-topology branches, especially CapN mutual
+  capacitance at K <= 10.
+- **Not supported:** reliable universal zero-shot transfer to an unseen topology.
+  Transmon zero-shot remains poor, especially for ground capacitance.
+- **Not yet testable:** the confirmatory lockbox claim. The concentric and star
+  datasets contain geometry and proxy features but no joined high-fidelity
+  capacitance labels.
+- **JEPA is not the next experiment.** The limiting issue is topology support,
+  label/campaign consistency, and the physical output parameterization—not the
+  absence of a learned three-number target latent.
+
+This is therefore an **expanded exploratory study**, not the final confirmatory
+lockbox result.
+
+## What was actually used
+
+The earlier 2,682-row balanced analysis is superseded. This run uses every
+currently joinable labeled v4 layout:
+
+| Labeled domain | Unique geometries |
+| --- | ---: |
+| Generalized IDC `exports` | 2,243 |
+| Generalized IDC `exp7_exports` | 1,440 |
+| Generalized IDC `exp8_exports` | 16,379 |
+| CapNInterdigitalTee full release | 894 |
+| TransmonCross full release | 1,933 |
+| **Total** | **22,889** |
+
+It also uses the complete 45,021-row v4 study library for support/OOD analysis:
+15,000 asymmetric IDC, 2,000 concentric, 2,000 star, 1,000 Firas exp1, and
+2,082 Firas exp2 layouts are included as unlabeled evaluation geometry.
+
+Consistency checks were unusually strong:
+
+- All 13,683 GIDC layouts shared by the earlier v4 release and the new custom
+  campaigns have bit-identical 544-D embeddings.
+- The new custom data add 6,379 labeled GIDC designs beyond that release.
+- One duplicated Transmon design was found. Its two historical labels differ by
+  as much as 3.58%; it is represented once, using the mean label, and is not
+  counted as two geometries.
+
+## Main numerical findings
+
+All primary targets are scored in physical log-capacitance and reported here as
+median relative error. The nonlinear probe is a 96-feature random-Fourier ridge
+model on the frozen 544-D v4 layout view. Results average five model seeds; the
+few-shot study also averages 20 nested shot draws on one fixed 50/50 target
+pool/test split.
+
+### 1. Same-domain learning works well
+
+On held-out rows from the same domain, median errors are:
+
+| Domain | Mutual | Ground-branch sum |
+| --- | ---: | ---: |
+| GIDC exports | 3.27% | 1.93% |
+| GIDC exp7 | 4.03% | 2.42% |
+| GIDC exp8 | 4.69% | 2.91% |
+| CapN Tee | 5.77% | 2.10% |
+| TransmonCross | 2.40% | 1.83% |
+
+This confirms that v4 is sensitive to the relevant continuous parameter changes
+inside each family.
+
+### 2. Cross-campaign GIDC transfer is strong
+
+Training on two GIDC campaigns and holding out the third gives:
+
+| Held campaign | Mutual median / P90 | Ground sum median / P90 |
+| --- | ---: | ---: |
+| exports | 4.71% / 12.13% | 2.98% / 7.34% |
+| exp7 | 6.43% / 15.90% | 3.83% / 10.25% |
+| exp8 | 9.73% / 38.43% | 6.62% / 29.82% |
+
+The much broader exp8 sweep is the hardest direction, as expected. Even there,
+the result is materially better than training a small target-only model at low
+K. For mutual capacitance, cross-campaign pretraining beats target-only through
+K=30 on exp8; target-only wins by K=100.
+
+The full-matrix eigen-spectrum tells the same story. Cross-campaign median
+errors are 2.3–6.8% across the three eigenmodes, with exp8 again the hardest
+held campaign.
+
+### 3. Cross-topology zero-shot remains asymmetric
+
+| Held topology | Source protocol | Mutual median | Ground sum median |
+| --- | --- | ---: | ---: |
+| CapN Tee | old balanced source | 36.26% | 30.00% |
+| CapN Tee | expanded, family-balanced | **21.39%** | 21.67% |
+| TransmonCross | old balanced source | **40.11%** | **82.23%** |
+| TransmonCross | expanded, family-balanced | 45.04% | 84.09% |
+
+The new data substantially improve zero-shot CapN mutual prediction, but do not
+improve Transmon zero-shot. Simply adding 20,062 IDC rows is not enough; without
+family balancing, the large IDC campaign can dominate the fit. Even with
+balancing, the Transmon ground branch is far outside the learned response map.
+
+### 4. Transfer helps only in the low-shot regime, and the crossover is branch-specific
+
+For mutual capacitance, the family-balanced expanded source model versus a
+target-only model gives:
+
+| Held topology | K=1 | K=3 | K=10 | K=30 | K=100 |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| CapN transfer | 26.22% | 24.98% | **22.69%** | 22.03% | 20.64% |
+| CapN target-only | 85.20% | 54.97% | 32.13% | **18.31%** | **10.41%** |
+| Transmon transfer | **38.94%** | **34.59%** | 29.67% | 29.08% | 29.10% |
+| Transmon target-only | 77.66% | 45.18% | **21.32%** | **12.90%** | **7.21%** |
+
+Paired bootstrap comparisons confirm the low-K advantage. At K=3, transfer
+reduces median mutual error by 30.0 percentage points for CapN and 10.6 points
+for Transmon versus target-only. Target-only overtakes by K=30 for CapN and K=10
+for Transmon.
+
+For Transmon ground sum, target-only is already better at K=1. A single global
+offset cannot repair the topology mismatch. The adaptation strategy must be
+allowed to change slope and local residual structure, not only the family mean.
+
+### 5. Geometry similarity is informative, not universal
+
+Spearman correlation between v4 distance and joint physical response distance
+is 0.54–0.77 within domains, 0.52–0.65 across GIDC campaigns, and 0.21–0.67
+across topology pairs. The CapN–Transmon pair is only 0.212.
+
+More importantly, differences in the raw proxy branches correlate weakly with
+differences in the high-fidelity branches. Cross-topology proxy correlations are
+near zero or negative in several directions. This explains how a broad geometric
+distance correlation can coexist with bad zero-shot regression: the embedding
+contains predictive information, but ordinary Euclidean distance and the raw
+proxy do not define a topology-independent electrostatic metric.
+
+## Matrix and solver audit
+
+All 20,062 GIDC records contain a full 3x3 database matrix. The 16,379 exp8
+designs additionally retain every solver pass in raw CSV form.
+
+- The raw exp8 final matrices match the database matrices exactly.
+- Reciprocal asymmetry is zero at stored precision.
+- There are no positive off-diagonal entries.
+- The smallest eigenvalue is positive for every audited GIDC matrix.
+- Final-pass maximum branch drift has median 0.0161%, P99 0.0438%, and maximum
+  0.1639%.
+
+However, the matrices do **not** have zero row sums. The median maximum row-sum
+fraction is 0.52–0.57 by GIDC campaign, with P95 about 0.74–0.75. These are SPD
+capacitance matrices referenced to the solver's outer/infinite boundary, not a
+three-node Laplacian containing every electrical reference.
+
+This invalidates one part of the proposed M1 specification: forcing the observed
+3x3 matrix to have zero row sums would train against the labels. The model must
+instead either:
+
+1. predict an SPD matrix directly, for example through a Cholesky parameterization;
+2. add explicit capacitance branches to the omitted outer/infinite reference; or
+3. train only on well-defined branch targets after deriving a physically correct
+   equivalent circuit from the solver convention.
+
+CapN and Transmon currently provide mutual and aggregate ground targets but no
+stored full raw matrices, so the proposed three-branch full-matrix loss remains
+an interim objective outside GIDC.
+
+## OOD status of the new unlabeled datasets
+
+Nearest-support distance in a source-fitted 32-D v4 PCA space is:
+
+| Campaign | Rows | Median nearest distance |
+| --- | ---: | ---: |
+| Firas exp1 | 1,000 | 0.309 |
+| Firas exp2 | 2,082 | 0.319 |
+| asymmetric IDC exp9 | 15,000 | 1.582 |
+| star | 2,000 | 3.062 |
+| concentric | 2,000 | 3.353 |
+
+Firas is geometrically close to labeled IDC support. Exp9 is a meaningful
+extrapolation. Star and concentric are genuine topology OOD sets and are the
+right lockboxes—but without high-fidelity labels they cannot yet validate the
+transfer claim.
+
+## Recommended plan of action
+
+1. **Freeze this run as the expanded exploratory baseline.** Do not tune v4 or
+   the final model using star/concentric labels after they are generated.
+2. **Generate lockbox labels:** at least 300 star and 300 concentric designs,
+   under one reference solver configuration, retaining full matrices, pass
+   histories, and ideally panel/surface charge.
+3. **Resolve campaign conventions:** re-simulate the preregistered 24 designs per
+   family plus bridge geometries. Investigate the duplicated Transmon label and
+   document the outer-boundary convention responsible for nonzero row sums.
+4. **Build the graph-native model on the correct output space.** Start with a
+   conductor-level equivariant residual as an ablation, then move to panel/edge
+   locality. Store ragged v4 graph tensors rather than only the pooled 544-D view.
+5. **Use family-balanced training.** The 20k IDC examples must not contribute
+   twenty times the family weight of CapN or Transmon.
+6. **Replace offset-only adaptation with a graceful ladder:** offset, affine,
+   GP residual on the frozen source representation, readout-only L2-SP, then full
+   L2-SP. The present results show exactly where a global offset is insufficient.
+7. **Evaluate once on the lockboxes** with the frozen protocol: K in
+   {0,1,3,10,30,100}, 20 nested draws, five seeds, physical relative-error
+   quantiles, uncertainty coverage, and downstream device metrics once circuit
+   parameters are supplied.
+
+The scientific headline should be: **GDS-native v4 embeddings deliver strong
+transfer across broad parameter campaigns and measurable low-shot gains across
+some topologies, but true unseen-topology generalization requires topology-diverse
+labels and a local, physics-consistent graph model.**
+

studies/expanded-capacitance-transfer-v2/README.md

@@ -0,0 +1,34 @@
+# Expanded capacitance-transfer study v2
+
+This directory is generated by:
+
+```bash
+MPLCONFIGDIR=/tmp/squadds-mpl .venv/bin/python \
+  scripts/run_expanded_capacitance_transfer_study.py
+```
+
+The run uses 22,889 unique labeled layouts, 20 shot draws, five model seeds,
+5,000 sampled pairs per similarity stratum, and the full 45,021-row v4 study
+library for unlabeled support analysis. See `manifest.json` for input and
+implementation hashes.
+
+Key files:
+
+- `EXECUTIVE_SUMMARY.md`: interpretation, conclusions, and next actions.
+- `dataset_summary.csv`, `raw_database_summary.csv`, and
+  `representation_coverage.csv`: corpus accounting and overlap checks.
+- `within_domain_predictive.csv` and `cross_campaign_zero_shot.csv`: specialist
+  and GIDC campaign-transfer results.
+- `topology_zero_shot.csv`, `topology_low_shot_summary.csv`, and
+  `topology_paired_comparisons.csv`: cross-topology results.
+- `database_matrix_audit_summary.csv` and `exp8_matrix_audit_summary.csv`: matrix
+  integrity, solver convergence, and database/raw-output agreement.
+- `gidc_matrix_spectrum_within.csv` and
+  `gidc_matrix_spectrum_cross_campaign.csv`: full-matrix spectrum prediction.
+- `similarity_correlations.csv`: physical-distance diagnostics.
+- `unlabeled_ood_support_summary.csv`: support distances for exp9, concentric,
+  star, and Firas campaigns.
+
+All learned-model results are exploratory probes on a frozen pooled v4 view.
+They are not a substitute for the proposed ragged graph model or for a labeled
+star/concentric lockbox.

studies/expanded-capacitance-transfer-v2/campaign_low_shot_summary.csv

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studies/expanded-capacitance-transfer-v2/cross_campaign_zero_shot.csv

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studies/expanded-capacitance-transfer-v2/database_matrix_audit_summary.csv

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studies/expanded-capacitance-transfer-v2/dataset_summary.csv

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studies/expanded-capacitance-transfer-v2/gidc_matrix_spectrum_cross_campaign.csv

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studies/expanded-capacitance-transfer-v2/gidc_matrix_spectrum_within.csv

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studies/expanded-capacitance-transfer-v2/manifest.json

@@ -0,0 +1,79 @@
+{
+  "study": "expanded-capacitance-transfer-v2",
+  "seed": 20261001,
+  "labeled_rows": 22889,
+  "labeled_rows_by_domain": {
+    "exp7_exports": 1440,
+    "exp8_exports": 16379,
+    "exports": 2243,
+    "release_CapNInterdigitalTee": 894,
+    "release_TransmonCross": 1933
+  },
+  "historical_comparator_rows": 2682,
+  "study_library_rows": 45021,
+  "exp8_raw_matrices": 16379,
+  "exp8_raw_matrix_set_sha256": "9ef4e84a382a979dee641e18d34ecc98c02fd276e7c8e62e82ef70766286f1bc",
+  "protocol": {
+    "targets": [
+      "ln(mutual_fF)",
+      "ln(ground_a_fF + ground_b_fF)"
+    ],
+    "representation": "planar-terminal-graph-v4 permutation-invariant 544-D layout view",
+    "canonical_proxy": "v4 graph proxy matrix stored per layout",
+    "topology_targets": [
+      "CapNInterdigitalTee",
+      "TransmonCross"
+    ],
+    "labeled_gidc_campaigns": [
+      "exports",
+      "exp7_exports",
+      "exp8_exports"
+    ],
+    "unlabeled_ood_campaigns": [
+      "exp9_exports",
+      "exp10_exports",
+      "exp11_exports",
+      "firas_sims_exp1",
+      "firas_sims_exp2"
+    ],
+    "budgets": [
+      0,
+      1,
+      3,
+      10,
+      30,
+      100
+    ],
+    "draws": 20,
+    "model_seeds": [
+      20261001,
+      20261101,
+      20261201,
+      20261301,
+      20261401
+    ],
+    "held_target_test_fraction": 0.5,
+    "within_domain_test_fraction": 0.2
+  },
+  "input_sha256": {
+    "tutorials/runtime/study-library/study-library-v4.parquet": "879e4e0395f58a10092d67a3d9cc3dcd3b87619a5508ba2690bb33223d603009",
+    "tutorials/runtime/tutorial24/planar-terminal-graph-v4-balanced.parquet": "873cd70c9be719459f58ad8183c0b70ab43095363a12e0c7f229cb43c09a1a46",
+    "../SQuADDS-hf-v34-release/metadata/planar-terminal-graph-v4.parquet": "f557974cd500851d823802eccae73c83f646c1fa0f32eb93debc2ac45d35b7f5",
+    "/Users/shanto/.cache/huggingface/hub/datasets--SQuADDS--SQuADDS_DB/snapshots/0e25705f54c343fb96571ff15b6fd8375ca899aa/coupler-GeneralizedCapNInterdigital-cap_matrix.json": "9a13d18fe6c582ebba39e490dddccfbd87d32c68696fd35401098ea7fd6dddb8",
+    "/Users/shanto/.cache/huggingface/hub/datasets--SQuADDS--SQuADDS_DB/snapshots/0e25705f54c343fb96571ff15b6fd8375ca899aa/coupler-CapNInterdigitalTee-cap_matrix.json": "a35365dff70433c7d12404047ec11f48bace1f4962e5c528cdea38bd5ab804d2",
+    "/Users/shanto/.cache/huggingface/hub/datasets--SQuADDS--SQuADDS_DB/snapshots/0e25705f54c343fb96571ff15b6fd8375ca899aa/qubit-TransmonCross-cap_matrix.json": "2f6cd1697099db4229eec52c3ee96d65f184bb7ec1e8124c54744a4b1aa5152f"
+  },
+  "implementation_sha256": "8e776658e8ccc9c383129e0e09b2fd1070322cc5c051bffb2966929f11e292d3",
+  "runtime": {
+    "python": "3.11.14 (main, Dec 17 2025, 21:09:14) [Clang 21.1.4 ]",
+    "platform": "macOS-26.6.2-arm64-arm-64bit",
+    "numpy": "1.26.4",
+    "pandas": "2.3.3"
+  },
+  "limitations": [
+    "Concentric, star, exp9, and Firas campaigns have v4 geometry/proxy data but no joined high-fidelity labels.",
+    "Only two non-GIDC labeled topology families are available for strict topology transfer.",
+    "The three historical topology families influenced representation development; topology LOFO remains exploratory.",
+    "RFF ridge is a controlled nonlinear probe, not the proposed final graph neural network."
+  ]
+}

studies/expanded-capacitance-transfer-v2/raw_database_summary.csv

@@ -0,0 +1,4 @@
+family,database_rows,unique_designs,duplicate_rows,maximum_duplicate_relative_spread,full_matrix_designs
+GeneralizedCapNInterdigital,20062,20062,0,0.0,20062
+CapNInterdigitalTee,894,894,0,0.0,0
+TransmonCross,1934,1933,1,0.035837031312191424,0

studies/expanded-capacitance-transfer-v2/representation_coverage.csv

@@ -0,0 +1,4 @@
+family,release_rows,release_unique_layouts,selected_labeled_rows,overlap_designs,maximum_overlap_embedding_difference
+GeneralizedCapNInterdigital,13683,13683,20062,13683,0.0
+CapNInterdigitalTee,894,894,894,894,0.0
+TransmonCross,1934,1933,1933,1933,0.0

studies/expanded-capacitance-transfer-v2/run_expanded_capacitance_transfer_study.py

@@ -0,0 +1,1353 @@
+#!/usr/bin/env python
+"""Expanded capacitance-transfer study using the Tutorial 24/25 corpora.
+
+The study joins the 45k-layout v4 study library to all available capacitance
+labels: 20,062 Generalized IDC records across exports/exp7/exp8, 894 CapN Tee
+records, and 1,933 unique Transmon records from the full v3/v4 release.  It also
+audits the 16,379 raw exp8 per-pass matrices and measures support for the
+unlabeled asymmetric IDC, concentric, star, and Firas campaigns.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import hashlib
+import json
+import math
+import platform
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Iterable
+
+import numpy as np
+import pandas as pd
+from huggingface_hub import hf_hub_download
+from scipy.optimize import minimize_scalar
+from scipy.spatial import cKDTree
+from scipy.stats import spearmanr
+
+from squadds.layouts import canonical_design_id
+
+
+SEED = 20261001
+CAP_FLOOR_FF = 0.01
+HUBER_DELTA = 0.05
+TAU_LOG = 0.10
+GIDC = "GeneralizedCapNInterdigital"
+CAPN = "CapNInterdigitalTee"
+TRANSMON = "TransmonCross"
+TOPOLOGY_FAMILIES = (GIDC, CAPN, TRANSMON)
+LABELED_GIDC_CAMPAIGNS = ("exports", "exp7_exports", "exp8_exports")
+UNLABELED_CAMPAIGNS = (
+    "exp9_exports",
+    "exp10_exports",
+    "exp11_exports",
+    "firas_sims_exp1",
+    "firas_sims_exp2",
+)
+RAW_SPECS = {
+    GIDC: {
+        "file": "coupler-GeneralizedCapNInterdigital-cap_matrix.json",
+        "a": "north",
+        "b": "south",
+    },
+    CAPN: {
+        "file": "coupler-CapNInterdigitalTee-cap_matrix.json",
+        "a": "top",
+        "b": "bottom",
+    },
+    TRANSMON: {
+        "file": "qubit-TransmonCross-cap_matrix.json",
+        "a": "cross",
+        "b": "claw",
+    },
+}
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def stack(series: pd.Series) -> np.ndarray:
+    matrix = np.vstack(series.map(np.asarray).to_numpy()).astype(np.float64)
+    if not np.isfinite(matrix).all():
+        raise ValueError("Non-finite representation values found.")
+    return matrix
+
+
+def huber(error: np.ndarray, delta: float = HUBER_DELTA) -> np.ndarray:
+    absolute = np.abs(error)
+    return np.where(absolute <= delta, 0.5 * error**2, delta * (absolute - 0.5 * delta))
+
+
+def group_balanced_weights(groups: np.ndarray) -> np.ndarray:
+    groups = np.asarray(groups)
+    result = np.zeros(len(groups), dtype=np.float64)
+    unique = np.unique(groups)
+    for group in unique:
+        mask = groups == group
+        result[mask] = 1.0 / (len(unique) * mask.sum())
+    return result * len(result)
+
+
+def fit_offset(
+    observed: np.ndarray,
+    base: np.ndarray,
+    groups: np.ndarray | None = None,
+) -> float:
+    weights = np.ones(len(observed)) if groups is None else group_balanced_weights(groups)
+    weights = weights / weights.sum()
+    objective = lambda value: float(
+        np.sum(weights * huber(observed - (base + float(value))))
+    )
+    return float(minimize_scalar(objective, bounds=(-10.0, 10.0), method="bounded").x)
+
+
+@dataclass
+class RandomFeatureRidge:
+    seed: int
+    features: int = 96
+    alpha: float = 1.0
+
+    def fit(
+        self,
+        matrix: np.ndarray,
+        target: np.ndarray,
+        sample_weight: np.ndarray | None = None,
+    ) -> "RandomFeatureRidge":
+        matrix = np.asarray(matrix, dtype=np.float64)
+        target = np.asarray(target, dtype=np.float64)
+        weights = np.ones(len(matrix), dtype=np.float64) if sample_weight is None else np.asarray(sample_weight)
+        weights = weights * (len(weights) / weights.sum())
+        self.mean_ = np.average(matrix, axis=0, weights=weights)
+        centered = matrix - self.mean_
+        self.scale_ = np.sqrt(np.average(centered**2, axis=0, weights=weights))
+        self.scale_ = np.where(self.scale_ > 1e-8, self.scale_, 1.0)
+        standardized = np.clip(centered / self.scale_, -8.0, 8.0)
+        rng = np.random.default_rng(self.seed)
+        self.weights_ = rng.normal(
+            0.0,
+            1.0 / math.sqrt(max(matrix.shape[1], 1)),
+            (matrix.shape[1], self.features),
+        )
+        self.phase_ = rng.uniform(0.0, 2.0 * np.pi, self.features)
+        design = self._design(standardized)
+        weighted_design = design * np.sqrt(weights)[:, None]
+        weighted_target = target * np.sqrt(weights)[:, None] if target.ndim == 2 else target * np.sqrt(weights)
+        penalty = np.eye(design.shape[1]) * self.alpha
+        penalty[0, 0] = 0.0
+        self.coefficients_ = np.linalg.solve(
+            weighted_design.T @ weighted_design + penalty,
+            weighted_design.T @ weighted_target,
+        )
+        return self
+
+    def _design(self, standardized: np.ndarray) -> np.ndarray:
+        nonlinear = np.sqrt(2.0 / self.features) * np.cos(
+            standardized @ self.weights_ + self.phase_
+        )
+        return np.column_stack([np.ones(len(standardized)), nonlinear])
+
+    def predict(self, matrix: np.ndarray) -> np.ndarray:
+        standardized = np.clip((np.asarray(matrix) - self.mean_) / self.scale_, -8.0, 8.0)
+        return self._design(standardized) @ self.coefficients_
+
+
+def load_raw_databases(local_only: bool):
+    labels: dict[str, dict[str, dict[str, Any]]] = {}
+    paths: dict[str, Path] = {}
+    summaries: list[dict[str, Any]] = []
+    for family, spec in RAW_SPECS.items():
+        path = Path(
+            hf_hub_download(
+                "SQuADDS/SQuADDS_DB",
+                spec["file"],
+                repo_type="dataset",
+                local_files_only=local_only,
+            )
+        )
+        paths[family] = path
+        a, b = spec["a"], spec["b"]
+        buckets: dict[str, list[dict[str, Any]]] = {}
+        for row in json.loads(path.read_text(encoding="utf-8")):
+            result = row["sim_results"]
+            design_id = canonical_design_id(family, row["design"]["design_options"])
+            record: dict[str, Any] = {
+                "design_id": design_id,
+                "mutual_fF": abs(float(result[f"{a}_to_{b}"])),
+                "ground_a_fF": abs(float(result[f"{a}_to_ground"])),
+                "ground_b_fF": abs(float(result[f"{b}_to_ground"])),
+                "diag_a_fF": abs(float(result[f"{a}_to_{a}"])),
+                "diag_b_fF": abs(float(result[f"{b}_to_{b}"])),
+                "diag_ground_fF": abs(float(result["ground_to_ground"])),
+            }
+            record["ground_sum_fF"] = record["ground_a_fF"] + record["ground_b_fF"]
+            matrix = result.get("maxwell_matrix")
+            if matrix:
+                letters = (a[0].upper(), b[0].upper(), "G")
+                value = np.asarray(
+                    [[float(matrix[f"C_{first}_{second}"]) for second in letters] for first in letters],
+                    dtype=np.float64,
+                )
+                eigenvalues = np.linalg.eigvalsh((value + value.T) / 2.0)
+                record["matrix_json"] = json.dumps(value.tolist())
+                for index, eigenvalue in enumerate(eigenvalues):
+                    record[f"matrix_eigen_{index}_fF"] = float(eigenvalue)
+            buckets.setdefault(design_id, []).append(record)
+        family_labels: dict[str, dict[str, Any]] = {}
+        duplicate_spreads = []
+        for design_id, records in buckets.items():
+            record = dict(records[0])
+            numeric = [
+                key
+                for key in record
+                if key != "design_id" and isinstance(record[key], (float, int))
+            ]
+            for key in numeric:
+                values = np.asarray([item[key] for item in records], dtype=np.float64)
+                record[key] = float(values.mean())
+                if len(values) > 1:
+                    duplicate_spreads.append(
+                        float((values.max() - values.min()) / max(abs(values.mean()), CAP_FLOOR_FF))
+                    )
+            if len(records) > 1 and all("matrix_json" in item for item in records):
+                matrices = np.stack([np.asarray(json.loads(item["matrix_json"])) for item in records])
+                matrix = matrices.mean(axis=0)
+                record["matrix_json"] = json.dumps(matrix.tolist())
+                for index, eigenvalue in enumerate(np.linalg.eigvalsh((matrix + matrix.T) / 2.0)):
+                    record[f"matrix_eigen_{index}_fF"] = float(eigenvalue)
+            record["duplicate_label_count"] = len(records)
+            family_labels[design_id] = record
+        summaries.append(
+            {
+                "family": family,
+                "database_rows": sum(len(items) for items in buckets.values()),
+                "unique_designs": len(buckets),
+                "duplicate_rows": sum(len(items) - 1 for items in buckets.values()),
+                "maximum_duplicate_relative_spread": max(duplicate_spreads, default=0.0),
+                "full_matrix_designs": sum("matrix_json" in item for item in family_labels.values()),
+            }
+        )
+        labels[family] = family_labels
+    return labels, paths, pd.DataFrame(summaries)
+
+
+def proxy_values(value: str) -> tuple[float, float, float, list[float]]:
+    matrix = np.asarray(json.loads(value), dtype=np.float64)
+    if matrix.shape != (3, 3):
+        raise ValueError(f"Expected a 3x3 proxy matrix, found {matrix.shape}.")
+    mutual = abs(float(matrix[0, 1]))
+    ground_a = abs(float(matrix[0, 2]))
+    ground_b = abs(float(matrix[1, 2]))
+    eigenvalues = np.maximum(np.linalg.eigvalsh((matrix + matrix.T) / 2.0), 0.0)
+    return mutual, ground_a + ground_b, abs(ground_a - ground_b), eigenvalues.tolist()
+
+
+def proxy_values_from_embedding(value: Any) -> tuple[float, float, float, list[float]]:
+    """Recover the v4 two-terminal proxy branches from the pooled layout view."""
+    embedding = np.asarray(value, dtype=np.float64)
+    if embedding.shape != (544,):
+        raise ValueError(f"Expected a 544-D v4 layout view, found {embedding.shape}.")
+    # Indices 82 and 146 pool log1p(node-to-ground proxy) over the two nodes.
+    mean_ground_log = float(embedding[82])
+    std_ground_log = abs(float(embedding[146]))
+    ground_a, ground_b = np.expm1(
+        [mean_ground_log + std_ground_log, mean_ground_log - std_ground_log]
+    )
+    ground_a, ground_b = max(float(ground_a), 0.0), max(float(ground_b), 0.0)
+    # Global index 55 is log1p of the sum of all unordered proxy branches.
+    total_branches = max(float(np.expm1(embedding[55])), 0.0)
+    mutual = max(total_branches - ground_a - ground_b, 0.0)
+    matrix = np.asarray(
+        [
+            [mutual + ground_a, -mutual, -ground_a],
+            [-mutual, mutual + ground_b, -ground_b],
+            [-ground_a, -ground_b, ground_a + ground_b],
+        ]
+    )
+    eigenvalues = np.maximum(np.linalg.eigvalsh(matrix), 0.0)
+    return mutual, ground_a + ground_b, abs(ground_a - ground_b), eigenvalues.tolist()
+
+
+def configuration_path(source_path: str) -> Path:
+    path = Path(source_path)
+    return path.parent.parent / "json" / f"{path.stem}.json"
+
+
+def frame_record(
+    row: Any,
+    label: dict[str, Any],
+    domain: str,
+    source_id: str,
+) -> dict[str, Any]:
+    proxy_mutual, proxy_ground, proxy_imbalance, proxy_eigen = proxy_values_from_embedding(
+        row.embedding_v4_layout
+    )
+    return {
+        "row_id": f"{domain}:{source_id}",
+        "family": row.component_name,
+        "domain": domain,
+        "source_id": source_id,
+        "design_id": label.get("design_id", ""),
+        "physical_geometry_sha256": row.physical_geometry_sha256,
+        "mutual_fF": label["mutual_fF"],
+        "ground_a_fF": label.get("ground_a_fF", np.nan),
+        "ground_b_fF": label.get("ground_b_fF", np.nan),
+        "ground_sum_fF": label["ground_sum_fF"],
+        "proxy_mutual_fF": proxy_mutual,
+        "proxy_ground_sum_fF": proxy_ground,
+        "proxy_ground_imbalance_fF": proxy_imbalance,
+        "proxy_eigen_0_fF": proxy_eigen[0],
+        "proxy_eigen_1_fF": proxy_eigen[1],
+        "proxy_eigen_2_fF": proxy_eigen[2],
+        "matrix_eigen_0_fF": label.get("matrix_eigen_0_fF", np.nan),
+        "matrix_eigen_1_fF": label.get("matrix_eigen_1_fF", np.nan),
+        "matrix_eigen_2_fF": label.get("matrix_eigen_2_fF", np.nan),
+        "matrix_json": label.get("matrix_json"),
+        "embedding": row.embedding_v4_layout,
+    }
+
+
+def frame_release_record(row: Any, label: dict[str, Any]) -> dict[str, Any]:
+    proxy_mutual, proxy_ground, proxy_imbalance, proxy_eigen = proxy_values_from_embedding(
+        row.embedding
+    )
+    source_id = str(row.source_id)
+    return {
+        "row_id": f"release:{source_id}",
+        "family": row.component_name,
+        "domain": f"release_{row.component_name}",
+        "source_id": source_id,
+        "design_id": row.design_id,
+        "physical_geometry_sha256": row.gds_sha256,
+        "mutual_fF": label["mutual_fF"],
+        "ground_a_fF": label["ground_a_fF"],
+        "ground_b_fF": label["ground_b_fF"],
+        "ground_sum_fF": label["ground_sum_fF"],
+        "proxy_mutual_fF": proxy_mutual,
+        "proxy_ground_sum_fF": proxy_ground,
+        "proxy_ground_imbalance_fF": proxy_imbalance,
+        "proxy_eigen_0_fF": proxy_eigen[0],
+        "proxy_eigen_1_fF": proxy_eigen[1],
+        "proxy_eigen_2_fF": proxy_eigen[2],
+        "matrix_eigen_0_fF": label.get("matrix_eigen_0_fF", np.nan),
+        "matrix_eigen_1_fF": label.get("matrix_eigen_1_fF", np.nan),
+        "matrix_eigen_2_fF": label.get("matrix_eigen_2_fF", np.nan),
+        "matrix_json": label.get("matrix_json"),
+        "embedding": row.embedding,
+    }
+
+
+def load_expanded_corpus(
+    library_path: Path,
+    old_balanced_path: Path,
+    release_v4_path: Path,
+    local_only: bool,
+):
+    library = pd.read_parquet(library_path)
+    release = pd.read_parquet(release_v4_path)
+    raw, raw_paths, raw_summary = load_raw_databases(local_only)
+    coverage: list[dict[str, Any]] = []
+    expanded_records: list[dict[str, Any]] = []
+
+    custom = library[
+        (library.component_name == GIDC)
+        & (library.collection == "custom_sweeps")
+        & library.campaign.isin(LABELED_GIDC_CAMPAIGNS)
+    ]
+    for row in custom.itertuples():
+        config = configuration_path(row.source_path)
+        options = json.loads(config.read_text(encoding="utf-8"))["cap_options"]
+        design_id = canonical_design_id(GIDC, options)
+        label = raw[GIDC].get(design_id)
+        short_source = str(row.source_id).split("/")[-1]
+        coverage.append(
+            {
+                "family": GIDC,
+                "domain": row.campaign,
+                "source_id": short_source,
+                "design_id": design_id,
+                "label_available": label is not None,
+                "matrix_available": bool(label and "matrix_json" in label),
+            }
+        )
+        if label:
+            expanded_records.append(frame_record(row, label, row.campaign, short_source))
+
+    representation_rows = []
+    gidc_release = release[release.component_name == GIDC]
+    gidc_expanded = pd.DataFrame(expanded_records)
+    release_by_design = gidc_release.set_index("design_id")
+    common = gidc_expanded[gidc_expanded.design_id.isin(release_by_design.index)]
+    differences = []
+    for row in common.itertuples():
+        released = np.asarray(release_by_design.loc[row.design_id].embedding, dtype=np.float64)
+        differences.append(float(np.max(np.abs(np.asarray(row.embedding) - released))))
+    representation_rows.append(
+        {
+            "family": GIDC,
+            "release_rows": len(gidc_release),
+            "release_unique_layouts": gidc_release.layout_id.nunique(),
+            "selected_labeled_rows": len(gidc_expanded),
+            "overlap_designs": len(common),
+            "maximum_overlap_embedding_difference": max(differences, default=np.nan),
+        }
+    )
+
+    for family in (CAPN, TRANSMON):
+        family_release = (
+            release[release.component_name == family]
+            .sort_values("source_id", kind="stable")
+            .drop_duplicates("layout_id", keep="first")
+        )
+        missing = family_release[~family_release.design_id.isin(raw[family])]
+        if len(missing):
+            raise RuntimeError(f"{family} release has {len(missing)} layouts without labels.")
+        for row in family_release.itertuples():
+            label = raw[family][row.design_id]
+            expanded_records.append(frame_release_record(row, label))
+            coverage.append(
+                {
+                    "family": family,
+                    "domain": f"release_{family}",
+                    "source_id": row.source_id,
+                    "design_id": row.design_id,
+                    "label_available": True,
+                    "matrix_available": "matrix_json" in label,
+                }
+            )
+        representation_rows.append(
+            {
+                "family": family,
+                "release_rows": int((release.component_name == family).sum()),
+                "release_unique_layouts": family_release.layout_id.nunique(),
+                "selected_labeled_rows": len(family_release),
+                "overlap_designs": len(family_release),
+                "maximum_overlap_embedding_difference": 0.0,
+            }
+        )
+
+    old = pd.read_parquet(old_balanced_path)
+    old_lookup = old.set_index(["component_name", "source_id"])
+    historical_records: list[dict[str, Any]] = []
+    historical = library[
+        (library.campaign == "tutorial24_balanced")
+        & library.component_name.isin(TOPOLOGY_FAMILIES)
+    ]
+    for row in historical.itertuples():
+        matched = old_lookup.loc[(row.component_name, row.source_id)]
+        label = {
+            "mutual_fF": float(matched.mutual_fF),
+            "ground_sum_fF": float(matched.ground_sum_fF),
+        }
+        record = frame_record(
+            row,
+            label,
+            f"historical_{row.component_name}",
+            str(row.source_id),
+        )
+        historical_records.append(record)
+
+    expanded = pd.DataFrame(expanded_records).sort_values(
+        ["family", "domain", "source_id"], kind="stable"
+    ).reset_index(drop=True)
+    historical_frame = pd.DataFrame(historical_records).sort_values(
+        ["family", "source_id"], kind="stable"
+    ).reset_index(drop=True)
+    if expanded.physical_geometry_sha256.duplicated().any():
+        raise RuntimeError("Expanded labeled corpus contains duplicate physical geometries.")
+    return (
+        expanded,
+        historical_frame,
+        library,
+        pd.DataFrame(coverage),
+        raw_paths,
+        raw_summary,
+        pd.DataFrame(representation_rows),
+    )
+
+
+def audit_exp8_matrices(
+    results_root: Path,
+    expanded: pd.DataFrame,
+) -> pd.DataFrame:
+    expected = expanded[expanded.domain == "exp8_exports"].set_index("source_id")
+    rows = []
+    matrix_paths = sorted(results_root.glob("q3d_cap_*/cap_matrix.csv"))
+    for path in matrix_paths:
+        source_id = path.parent.name.removeprefix("q3d_")
+        with path.open(newline="", encoding="utf-8") as stream:
+            passes = list(csv.DictReader(stream))
+        final = passes[-1]
+        previous = passes[-2] if len(passes) > 1 else final
+        keys = {
+            "mutual": "C(N,S) [pF]",
+            "ground_a": "C(N,G) [pF]",
+            "ground_b": "C(S,G) [pF]",
+        }
+        values = {name: abs(float(final[key])) * 1000.0 for name, key in keys.items()}
+        previous_values = {name: abs(float(previous[key])) * 1000.0 for name, key in keys.items()}
+        reciprocal = (
+            ("C(N,S) [pF]", "C(S,N) [pF]"),
+            ("C(N,G) [pF]", "C(G,N) [pF]"),
+            ("C(S,G) [pF]", "C(G,S) [pF]"),
+        )
+        asymmetry = max(abs(float(final[left]) - float(final[right])) for left, right in reciprocal) * 1000.0
+        relative_asymmetry = max(
+            abs(float(final[left]) - float(final[right]))
+            / max(0.5 * (abs(float(final[left])) + abs(float(final[right]))), CAP_FLOOR_FF / 1000.0)
+            for left, right in reciprocal
+        )
+        matrix = np.asarray(
+            [
+                [float(final["C(N,N) [pF]"]), float(final["C(N,S) [pF]"]), float(final["C(N,G) [pF]"])],
+                [float(final["C(S,N) [pF]"]), float(final["C(S,S) [pF]"]), float(final["C(S,G) [pF]"])],
+                [float(final["C(G,N) [pF]"]), float(final["C(G,S) [pF]"]), float(final["C(G,G) [pF]"])],
+            ]
+        ) * 1000.0
+        row_sum_fraction = np.max(np.abs(matrix.sum(axis=1)) / np.maximum(np.abs(np.diag(matrix)), CAP_FLOOR_FF))
+        off_diagonal = matrix[~np.eye(3, dtype=bool)]
+        eigenvalues = np.linalg.eigvalsh((matrix + matrix.T) / 2.0)
+        reference = expected.loc[source_id]
+        reference_matrix = np.asarray(json.loads(reference.matrix_json), dtype=np.float64)
+        database_difference = float(np.max(np.abs(matrix - reference_matrix)))
+        rows.append(
+            {
+                "source_id": source_id,
+                "csv_sha256": sha256(path),
+                "passes": len(passes),
+                "final_pass": int(float(final["Pass []"])),
+                "mutual_fF": values["mutual"],
+                "ground_a_fF": values["ground_a"],
+                "ground_b_fF": values["ground_b"],
+                "maximum_reciprocal_asymmetry_fF": asymmetry,
+                "maximum_reciprocal_relative_asymmetry": relative_asymmetry,
+                "positive_off_diagonal_entries": int(np.sum(off_diagonal > 1e-12)),
+                "minimum_eigenvalue_fF": float(eigenvalues.min()),
+                "maximum_final_pass_relative_drift": max(
+                    abs(values[name] - previous_values[name]) / max(values[name], CAP_FLOOR_FF)
+                    for name in keys
+                ),
+                "maximum_row_sum_fraction": row_sum_fraction,
+                "maximum_database_difference_fF": database_difference,
+            }
+        )
+    return pd.DataFrame(rows).sort_values("source_id")
+
+
+def database_matrix_audit(expanded: pd.DataFrame) -> pd.DataFrame:
+    rows = []
+    for row in expanded[expanded.matrix_json.notna()].itertuples():
+        matrix = np.asarray(json.loads(row.matrix_json), dtype=np.float64)
+        reciprocal = np.abs(matrix - matrix.T)
+        scale = np.maximum(0.5 * (np.abs(matrix) + np.abs(matrix.T)), CAP_FLOOR_FF)
+        off_diagonal = matrix[~np.eye(3, dtype=bool)]
+        eigenvalues = np.linalg.eigvalsh((matrix + matrix.T) / 2.0)
+        rows.append(
+            {
+                "domain": row.domain,
+                "source_id": row.source_id,
+                "maximum_reciprocal_relative_asymmetry": float(np.max(reciprocal / scale)),
+                "positive_off_diagonal_entries": int(np.sum(off_diagonal > 1e-12)),
+                "maximum_row_sum_fraction": float(
+                    np.max(
+                        np.abs(matrix.sum(axis=1))
+                        / np.maximum(np.abs(np.diag(matrix)), CAP_FLOOR_FF)
+                    )
+                ),
+                "minimum_eigenvalue_fF": float(eigenvalues.min()),
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def target_arrays(frame: pd.DataFrame, target: str) -> tuple[np.ndarray, np.ndarray]:
+    if target == "mutual":
+        observed = frame.mutual_fF.to_numpy(float)
+        proxy = frame.proxy_mutual_fF.to_numpy(float)
+    elif target == "ground_sum":
+        observed = frame.ground_sum_fF.to_numpy(float)
+        proxy = frame.proxy_ground_sum_fF.to_numpy(float)
+    else:
+        raise ValueError(target)
+    return np.log(np.maximum(observed, CAP_FLOOR_FF)), np.log(np.maximum(proxy, CAP_FLOOR_FF))
+
+
+def regression_metrics(observed_log: np.ndarray, predicted_log: np.ndarray) -> dict[str, float]:
+    error = np.asarray(predicted_log) - np.asarray(observed_log)
+    relative = np.abs(np.exp(np.clip(error, -20.0, 20.0)) - 1.0)
+    observed = np.exp(observed_log)
+    predicted = np.exp(np.clip(predicted_log, -20.0, 20.0))
+    denominator = float(np.sum((observed_log - observed_log.mean()) ** 2))
+    return {
+        "n_test": len(observed_log),
+        "median_relative_error": float(np.median(relative)),
+        "p90_relative_error": float(np.quantile(relative, 0.90)),
+        "p95_relative_error": float(np.quantile(relative, 0.95)),
+        "bias_log": float(np.mean(error)),
+        "slope_log": float(np.polyfit(observed_log, predicted_log, 1)[0]),
+        "r2_log": float(1.0 - np.sum(error**2) / denominator) if denominator > 0 else float("nan"),
+        "medae_fF": float(np.median(np.abs(predicted - observed))),
+        "rmse_fF": float(np.sqrt(np.mean((predicted - observed) ** 2))),
+    }
+
+
+def exact_stratified_split(proxy_log: np.ndarray, test_fraction: float, seed: int):
+    ranks = pd.Series(proxy_log).rank(method="first")
+    bins = pd.qcut(ranks, 10, labels=False, duplicates="drop").to_numpy()
+    rng = np.random.default_rng(seed)
+    desired = int(round(len(proxy_log) * test_fraction))
+    members = {int(value): rng.permutation(np.flatnonzero(bins == value)) for value in np.unique(bins)}
+    raw = {key: len(value) * test_fraction for key, value in members.items()}
+    counts = {key: int(math.floor(value)) for key, value in raw.items()}
+    remainder = desired - sum(counts.values())
+    order = sorted(members, key=lambda key: (raw[key] - counts[key], -key), reverse=True)
+    for key in order[:remainder]:
+        counts[key] += 1
+    test, train = [], []
+    for key, values in members.items():
+        test.extend(values[: counts[key]])
+        train.extend(values[counts[key] :])
+    return np.asarray(sorted(train)), np.asarray(sorted(test))
+
+
+def fit_predict_methods(
+    train: pd.DataFrame,
+    test: pd.DataFrame,
+    target: str,
+    seeds: Iterable[int],
+    weight_groups: bool = False,
+):
+    train_x, test_x = stack(train.embedding), stack(test.embedding)
+    train_y, train_proxy = target_arrays(train, target)
+    test_y, test_proxy = target_arrays(test, target)
+    groups = train.family.to_numpy(str) if weight_groups else None
+    sample_weight = group_balanced_weights(groups) if groups is not None else None
+    offset = fit_offset(train_y, train_proxy, groups)
+    output = [
+        ("P0_raw_proxy", -1, test_proxy, float("nan")),
+        ("P1_source_offset", -1, test_proxy + offset, offset),
+    ]
+    for seed in seeds:
+        direct = RandomFeatureRidge(seed).fit(train_x, train_y, sample_weight)
+        residual = RandomFeatureRidge(seed).fit(
+            train_x, train_y - (train_proxy + offset), sample_weight
+        )
+        output.extend(
+            [
+                ("RFF_v4_layout_direct", seed, direct.predict(test_x), offset),
+                (
+                    "RFF_v4_layout_proxy_residual",
+                    seed,
+                    test_proxy + offset + residual.predict(test_x),
+                    offset,
+                ),
+            ]
+        )
+    return test_y, output
+
+
+def within_domain_study(expanded: pd.DataFrame, seeds: Iterable[int]) -> pd.DataFrame:
+    rows = []
+    for domain, frame in expanded.groupby("domain", sort=True):
+        frame = frame.reset_index(drop=True)
+        _, proxy = target_arrays(frame, "mutual")
+        train_index, test_index = exact_stratified_split(proxy, 0.2, SEED + len(rows))
+        train, test = frame.iloc[train_index], frame.iloc[test_index]
+        for target in ("mutual", "ground_sum"):
+            observed, predictions = fit_predict_methods(train, test, target, seeds)
+            for method, seed, predicted, offset in predictions:
+                rows.append(
+                    {
+                        "domain": domain,
+                        "family": frame.family.iloc[0],
+                        "target": target,
+                        "method": method,
+                        "seed": seed,
+                        "source_offset": offset,
+                        **regression_metrics(observed, predicted),
+                    }
+                )
+    return pd.DataFrame(rows)
+
+
+def cross_campaign_study(expanded: pd.DataFrame, seeds: Iterable[int]) -> pd.DataFrame:
+    gidc = expanded[expanded.family == GIDC]
+    rows = []
+    for held in LABELED_GIDC_CAMPAIGNS:
+        train = gidc[gidc.domain != held]
+        test = gidc[gidc.domain == held]
+        for target in ("mutual", "ground_sum"):
+            observed, predictions = fit_predict_methods(train, test, target, seeds)
+            for method, seed, predicted, offset in predictions:
+                rows.append(
+                    {
+                        "held_campaign": held,
+                        "target": target,
+                        "method": method,
+                        "seed": seed,
+                        "source_offset": offset,
+                        **regression_metrics(observed, predicted),
+                    }
+                )
+    return pd.DataFrame(rows)
+
+
+def topology_sources(
+    expanded: pd.DataFrame,
+    historical: pd.DataFrame,
+    held_family: str,
+):
+    old = historical[historical.family != held_family].copy()
+    expanded_source = expanded[expanded.family != held_family].copy()
+    return {
+        "old_balanced_2682": (old, False),
+        "expanded_unweighted": (expanded_source, False),
+        "expanded_family_balanced": (expanded_source, True),
+    }
+
+
+def topology_zero_shot(
+    expanded: pd.DataFrame,
+    historical: pd.DataFrame,
+    seeds: Iterable[int],
+) -> pd.DataFrame:
+    rows = []
+    for held in (CAPN, TRANSMON):
+        test = expanded[expanded.family == held]
+        for protocol, (train, weighted) in topology_sources(expanded, historical, held).items():
+            for target in ("mutual", "ground_sum"):
+                observed, predictions = fit_predict_methods(
+                    train, test, target, seeds, weight_groups=weighted
+                )
+                for method, seed, predicted, offset in predictions:
+                    rows.append(
+                        {
+                            "held_family": held,
+                            "source_protocol": protocol,
+                            "target": target,
+                            "method": method,
+                            "seed": seed,
+                            "source_rows": len(train),
+                            "source_offset": offset,
+                            **regression_metrics(observed, predicted),
+                        }
+                    )
+    return pd.DataFrame(rows)
+
+
+def low_shot_topology(
+    expanded: pd.DataFrame,
+    historical: pd.DataFrame,
+    seeds: Iterable[int],
+    draws: int,
+    budgets: Iterable[int],
+):
+    rows, assignments = [], []
+    budgets = tuple(sorted(budgets))
+    for held_index, held in enumerate((CAPN, TRANSMON)):
+        target_frame = expanded[expanded.family == held].reset_index(drop=True)
+        _, proxy = target_arrays(target_frame, "mutual")
+        pool_index, test_index = exact_stratified_split(proxy, 0.5, SEED + held_index)
+        pool, test = target_frame.iloc[pool_index], target_frame.iloc[test_index]
+        for partition, part in (("shot_pool", pool), ("test", test)):
+            for row_id in part.row_id:
+                assignments.append(
+                    {"held_family": held, "row_id": row_id, "partition": partition}
+                )
+        orders = [
+            np.random.default_rng(SEED + 1000 * held_index + draw).permutation(len(pool))
+            for draw in range(draws)
+        ]
+        for target in ("mutual", "ground_sum"):
+            test_y, _ = target_arrays(test, target)
+            pool_y, _ = target_arrays(pool, target)
+            test_x, pool_x = stack(test.embedding), stack(pool.embedding)
+            for protocol, (source, weighted) in topology_sources(expanded, historical, held).items():
+                source_x = stack(source.embedding)
+                source_y, _ = target_arrays(source, target)
+                weights = group_balanced_weights(source.family.to_numpy(str)) if weighted else None
+                for seed in seeds:
+                    model = RandomFeatureRidge(seed).fit(source_x, source_y, weights)
+                    base_test = model.predict(test_x)
+                    base_pool = model.predict(pool_x)
+                    rows.append(
+                        {
+                            "held_family": held,
+                            "target": target,
+                            "source_protocol": protocol,
+                            "budget": 0,
+                            "draw": 0,
+                            "seed": seed,
+                            "method": "source_zero_shot",
+                            **regression_metrics(test_y, base_test),
+                        }
+                    )
+                    residual_scale = max(float(np.std(source_y - model.predict(source_x), ddof=1)), 1e-3)
+                    for draw, order in enumerate(orders):
+                        for budget in budgets:
+                            selected = order[:budget]
+                            residual = pool_y[selected] - base_pool[selected]
+                            shift = float(
+                                residual.sum() / (budget + (residual_scale / TAU_LOG) ** 2)
+                            )
+                            rows.append(
+                                {
+                                    "held_family": held,
+                                    "target": target,
+                                    "source_protocol": protocol,
+                                    "budget": budget,
+                                    "draw": draw,
+                                    "seed": seed,
+                                    "method": "source_K_offset",
+                                    **regression_metrics(test_y, base_test + shift),
+                                }
+                            )
+            for seed in seeds:
+                for draw, order in enumerate(orders):
+                    for budget in budgets:
+                        selected = order[:budget]
+                        model = RandomFeatureRidge(seed).fit(pool_x[selected], pool_y[selected])
+                        rows.append(
+                            {
+                                "held_family": held,
+                                "target": target,
+                                "source_protocol": "target_only",
+                                "budget": budget,
+                                "draw": draw,
+                                "seed": seed,
+                                "method": "target_only",
+                                **regression_metrics(test_y, model.predict(test_x)),
+                            }
+                        )
+    return pd.DataFrame(rows), pd.DataFrame(assignments)
+
+
+def low_shot_campaign(
+    expanded: pd.DataFrame,
+    seeds: Iterable[int],
+    draws: int,
+    budgets: Iterable[int],
+) -> pd.DataFrame:
+    rows = []
+    gidc = expanded[expanded.family == GIDC]
+    budgets = tuple(sorted(budgets))
+    for held_index, held in enumerate(LABELED_GIDC_CAMPAIGNS):
+        target = gidc[gidc.domain == held].reset_index(drop=True)
+        source = gidc[gidc.domain != held]
+        target_y, proxy = target_arrays(target, "mutual")
+        pool_index, test_index = exact_stratified_split(proxy, 0.5, SEED + 200 + held_index)
+        pool_x, test_x = stack(target.embedding.iloc[pool_index]), stack(target.embedding.iloc[test_index])
+        pool_y, test_y = target_y[pool_index], target_y[test_index]
+        source_x, source_y = stack(source.embedding), target_arrays(source, "mutual")[0]
+        orders = [
+            np.random.default_rng(SEED + 3000 * held_index + draw).permutation(len(pool_index))
+            for draw in range(draws)
+        ]
+        for seed in seeds:
+            model = RandomFeatureRidge(seed).fit(source_x, source_y)
+            base_test, base_pool = model.predict(test_x), model.predict(pool_x)
+            residual_scale = max(float(np.std(source_y - model.predict(source_x), ddof=1)), 1e-3)
+            rows.append(
+                {
+                    "held_campaign": held,
+                    "budget": 0,
+                    "draw": 0,
+                    "seed": seed,
+                    "method": "cross_campaign_zero_shot",
+                    **regression_metrics(test_y, base_test),
+                }
+            )
+            for draw, order in enumerate(orders):
+                for budget in budgets:
+                    selected = order[:budget]
+                    residual = pool_y[selected] - base_pool[selected]
+                    shift = float(
+                        residual.sum() / (budget + (residual_scale / TAU_LOG) ** 2)
+                    )
+                    rows.append(
+                        {
+                            "held_campaign": held,
+                            "budget": budget,
+                            "draw": draw,
+                            "seed": seed,
+                            "method": "cross_campaign_K_offset",
+                            **regression_metrics(test_y, base_test + shift),
+                        }
+                    )
+                    target_model = RandomFeatureRidge(seed).fit(pool_x[selected], pool_y[selected])
+                    rows.append(
+                        {
+                            "held_campaign": held,
+                            "budget": budget,
+                            "draw": draw,
+                            "seed": seed,
+                            "method": "target_only",
+                            **regression_metrics(test_y, target_model.predict(test_x)),
+                        }
+                    )
+    return pd.DataFrame(rows)
+
+
+def summarize_metrics(
+    rows: pd.DataFrame,
+    keys: list[str],
+) -> pd.DataFrame:
+    metrics = (
+        "median_relative_error",
+        "p90_relative_error",
+        "bias_log",
+        "slope_log",
+        "r2_log",
+        "medae_fF",
+        "rmse_fF",
+    )
+    aggregations: dict[str, tuple[str, str]] = {}
+    for metric in metrics:
+        aggregations[f"{metric}_mean"] = (metric, "mean")
+        aggregations[f"{metric}_std"] = (metric, "std")
+    return rows.groupby(keys, as_index=False).agg(**aggregations).sort_values(keys)
+
+
+def paired_topology_comparisons(low: pd.DataFrame, bootstrap: int = 10000) -> pd.DataFrame:
+    nonzero = low[low.budget > 0].copy()
+    nonzero["label"] = nonzero.source_protocol + ":" + nonzero.method
+    index = ["held_family", "target", "budget", "draw", "seed"]
+    wide = nonzero.pivot_table(
+        index=index,
+        columns="label",
+        values="median_relative_error",
+        aggfunc="first",
+    ).reset_index()
+    comparisons = {
+        "expanded_balanced_vs_old": (
+            "expanded_family_balanced:source_K_offset",
+            "old_balanced_2682:source_K_offset",
+        ),
+        "expanded_unweighted_vs_old": (
+            "expanded_unweighted:source_K_offset",
+            "old_balanced_2682:source_K_offset",
+        ),
+        "expanded_balanced_vs_target_only": (
+            "expanded_family_balanced:source_K_offset",
+            "target_only:target_only",
+        ),
+    }
+    rng = np.random.default_rng(SEED + 909)
+    output = []
+    for keys, group in wide.groupby(["held_family", "target", "budget"]):
+        for name, (left, right) in comparisons.items():
+            paired = group[["draw", left, right]].copy()
+            paired["difference"] = paired[left] - paired[right]
+            draw_values = paired.groupby("draw").difference.mean().to_numpy(float)
+            samples = rng.choice(draw_values, (bootstrap, len(draw_values)), replace=True).mean(axis=1)
+            output.append(
+                {
+                    "held_family": keys[0],
+                    "target": keys[1],
+                    "budget": keys[2],
+                    "comparison": name,
+                    "mean_paired_difference": float(draw_values.mean()),
+                    "ci95_low": float(np.quantile(samples, 0.025)),
+                    "ci95_high": float(np.quantile(samples, 0.975)),
+                    "fraction_draws_left_better": float(np.mean(draw_values < 0)),
+                }
+            )
+    return pd.DataFrame(output)
+
+
+def robust_scale(matrix: np.ndarray):
+    center = np.median(matrix, axis=0)
+    spread = np.quantile(matrix, 0.75, axis=0) - np.quantile(matrix, 0.25, axis=0)
+    active = spread > 1e-9
+    return center, spread, active
+
+
+def apply_scale(matrix: np.ndarray, center: np.ndarray, spread: np.ndarray, active: np.ndarray):
+    return np.clip((matrix[:, active] - center[active]) / spread[active], -8.0, 8.0)
+
+
+def sampled_pairs(left: np.ndarray, right: np.ndarray, count: int, seed: int, same: bool):
+    rng = np.random.default_rng(seed)
+    first = rng.choice(left, count, replace=True)
+    second = rng.choice(right, count, replace=True)
+    if same:
+        collision = first == second
+        while collision.any():
+            second[collision] = rng.choice(right, int(collision.sum()), replace=True)
+            collision = first == second
+    return first, second
+
+
+def similarity_study(expanded: pd.DataFrame, pairs: int) -> pd.DataFrame:
+    balanced = pd.concat(
+        [
+            group.sample(min(len(group), 894), random_state=SEED)
+            for _, group in expanded.groupby("domain", sort=True)
+        ],
+        ignore_index=True,
+    )
+    center, spread, active = robust_scale(stack(balanced.embedding))
+    scaled = apply_scale(stack(expanded.embedding), center, spread, active)
+    mutual, proxy_mutual = target_arrays(expanded, "mutual")
+    ground, proxy_ground = target_arrays(expanded, "ground_sum")
+    domains = expanded.domain.to_numpy(str)
+    strata = [("within", domain, domain) for domain in sorted(expanded.domain.unique())]
+    strata.extend(
+        ("cross_campaign", first, second)
+        for index, first in enumerate(LABELED_GIDC_CAMPAIGNS)
+        for second in LABELED_GIDC_CAMPAIGNS[index + 1 :]
+    )
+    strata.extend(
+        [
+            ("cross_topology", "exp8_exports", f"release_{CAPN}"),
+            ("cross_topology", "exp8_exports", f"release_{TRANSMON}"),
+            ("cross_topology", f"release_{CAPN}", f"release_{TRANSMON}"),
+        ]
+    )
+    rows = []
+    for stratum_index, (kind, left_domain, right_domain) in enumerate(strata):
+        left = np.flatnonzero(domains == left_domain)
+        right = np.flatnonzero(domains == right_domain)
+        first, second = sampled_pairs(
+            left, right, pairs, SEED + stratum_index, left_domain == right_domain
+        )
+        embedding_distance = np.linalg.norm(scaled[first] - scaled[second], axis=1) / math.sqrt(active.sum())
+        deltas = {
+            "absolute_log_mutual_difference": np.abs(mutual[first] - mutual[second]),
+            "absolute_log_ground_sum_difference": np.abs(ground[first] - ground[second]),
+            "joint_log_response_distance": np.sqrt(
+                (mutual[first] - mutual[second]) ** 2 + (ground[first] - ground[second]) ** 2
+            ),
+        }
+        for outcome, delta in deltas.items():
+            rows.append(
+                {
+                    "stratum": kind,
+                    "domain_a": left_domain,
+                    "domain_b": right_domain,
+                    "relation": "v4_layout_distance",
+                    "outcome": outcome,
+                    "pairs": pairs,
+                    "spearman_rho": float(spearmanr(embedding_distance, delta).statistic),
+                }
+            )
+        for name, proxy, target in (
+            ("proxy_mutual_delta", proxy_mutual, mutual),
+            ("proxy_ground_delta", proxy_ground, ground),
+        ):
+            rows.append(
+                {
+                    "stratum": kind,
+                    "domain_a": left_domain,
+                    "domain_b": right_domain,
+                    "relation": name,
+                    "outcome": "matching_target_delta",
+                    "pairs": pairs,
+                    "spearman_rho": float(
+                        spearmanr(
+                            np.abs(proxy[first] - proxy[second]),
+                            np.abs(target[first] - target[second]),
+                        ).statistic
+                    ),
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def ood_support_study(
+    expanded: pd.DataFrame,
+    library: pd.DataFrame,
+    dimensions: int = 32,
+):
+    balanced = pd.concat(
+        [
+            group.sample(min(len(group), 894), random_state=SEED)
+            for _, group in expanded.groupby("domain", sort=True)
+        ],
+        ignore_index=True,
+    )
+    support_x = stack(expanded.embedding)
+    center, spread, active = robust_scale(stack(balanced.embedding))
+    support_scaled = apply_scale(support_x, center, spread, active)
+    rng = np.random.default_rng(SEED)
+    fit_index = rng.choice(len(support_scaled), min(4000, len(support_scaled)), replace=False)
+    pca_center = support_scaled[fit_index].mean(axis=0)
+    _, _, axes = np.linalg.svd(support_scaled[fit_index] - pca_center, full_matrices=False)
+    axes = axes[:dimensions]
+    support_projected = (support_scaled - pca_center) @ axes.T
+    tree = cKDTree(support_projected)
+
+    rows = []
+    for row in library[library.campaign.isin(UNLABELED_CAMPAIGNS)].itertuples():
+        vector = np.asarray(row.embedding_v4_layout, dtype=np.float64)[None, :]
+        projected = (apply_scale(vector, center, spread, active) - pca_center) @ axes.T
+        distance, neighbor = tree.query(projected[0], k=1)
+        proxy_mutual, proxy_ground, _, _ = proxy_values(row.proxy_matrix_json)
+        rows.append(
+            {
+                "campaign": row.campaign,
+                "family": row.component_name,
+                "source_id": row.source_id,
+                "nearest_labeled_distance_32d": float(distance / math.sqrt(dimensions)),
+                "nearest_labeled_family": expanded.family.iloc[int(neighbor)],
+                "nearest_labeled_domain": expanded.domain.iloc[int(neighbor)],
+                "proxy_mutual_fF": proxy_mutual,
+                "proxy_ground_sum_fF": proxy_ground,
+            }
+        )
+    row_frame = pd.DataFrame(rows)
+    summary = (
+        row_frame.groupby(["campaign", "family"], as_index=False)
+        .agg(
+            rows=("source_id", "size"),
+            median_nearest_distance=("nearest_labeled_distance_32d", "median"),
+            p90_nearest_distance=("nearest_labeled_distance_32d", lambda x: x.quantile(0.90)),
+            p95_nearest_distance=("nearest_labeled_distance_32d", lambda x: x.quantile(0.95)),
+            median_proxy_mutual_fF=("proxy_mutual_fF", "median"),
+            median_proxy_ground_sum_fF=("proxy_ground_sum_fF", "median"),
+        )
+        .sort_values("median_nearest_distance")
+    )
+    neighbor_mix = (
+        row_frame.groupby(["campaign", "nearest_labeled_family", "nearest_labeled_domain"], as_index=False)
+        .size()
+        .rename(columns={"size": "rows"})
+    )
+    return row_frame, summary, neighbor_mix
+
+
+def matrix_spectrum_study(
+    expanded: pd.DataFrame,
+    seeds: Iterable[int],
+    cross_campaign: bool,
+) -> pd.DataFrame:
+    target_columns = [f"matrix_eigen_{index}_fF" for index in range(3)]
+    rows = []
+    gidc = expanded[(expanded.family == GIDC) & expanded.matrix_json.notna()]
+    for held_index, domain in enumerate(LABELED_GIDC_CAMPAIGNS):
+        frame = gidc[gidc.domain == domain].reset_index(drop=True)
+        if frame[target_columns].isna().any().any():
+            raise ValueError(f"The {domain} matrix spectrum is incomplete.")
+        observed = np.log(np.maximum(frame[target_columns].to_numpy(float), CAP_FLOOR_FF))
+        if cross_campaign:
+            train = gidc[gidc.domain != domain]
+            train_x = stack(train.embedding)
+            train_y = np.log(np.maximum(train[target_columns].to_numpy(float), CAP_FLOOR_FF))
+            test_x, test_y = stack(frame.embedding), observed
+        else:
+            _, proxy = target_arrays(frame, "mutual")
+            train_index, test_index = exact_stratified_split(proxy, 0.2, SEED + 808 + held_index)
+            train_x, test_x = stack(frame.embedding.iloc[train_index]), stack(frame.embedding.iloc[test_index])
+            train_y, test_y = observed[train_index], observed[test_index]
+        for seed in seeds:
+            model = RandomFeatureRidge(seed).fit(train_x, train_y)
+            prediction = model.predict(test_x)
+            for index in range(3):
+                rows.append(
+                    {
+                        "held_campaign": domain,
+                        "protocol": "cross_campaign" if cross_campaign else "within_campaign",
+                        "eigenmode": index,
+                        "seed": seed,
+                        **regression_metrics(test_y[:, index], prediction[:, index]),
+                    }
+                )
+    return pd.DataFrame(rows)
+
+
+def dataset_summary(expanded: pd.DataFrame) -> pd.DataFrame:
+    return (
+        expanded.groupby(["family", "domain"], as_index=False)
+        .agg(
+            rows=("row_id", "size"),
+            unique_geometries=("physical_geometry_sha256", "nunique"),
+            mutual_median_fF=("mutual_fF", "median"),
+            ground_sum_median_fF=("ground_sum_fF", "median"),
+            proxy_mutual_median_fF=("proxy_mutual_fF", "median"),
+        )
+        .sort_values(["family", "domain"])
+    )
+
+
+def parser() -> argparse.ArgumentParser:
+    result = argparse.ArgumentParser(description=__doc__)
+    result.add_argument(
+        "--library",
+        type=Path,
+        default=Path("tutorials/runtime/study-library/study-library-v4.parquet"),
+    )
+    result.add_argument(
+        "--old-balanced",
+        type=Path,
+        default=Path("tutorials/runtime/tutorial24/planar-terminal-graph-v4-balanced.parquet"),
+    )
+    result.add_argument(
+        "--release-v4",
+        type=Path,
+        default=Path("../SQuADDS-hf-v34-release/metadata/planar-terminal-graph-v4.parquet"),
+    )
+    result.add_argument(
+        "--exp8-results",
+        type=Path,
+        default=Path.home() / "Downloads/squadds_data/exports/squadds",
+    )
+    result.add_argument(
+        "--output",
+        type=Path,
+        default=Path("tutorials/runtime/expanded-capacitance-transfer-v2"),
+    )
+    result.add_argument("--draws", type=int, default=20)
+    result.add_argument("--seeds", type=int, default=5)
+    result.add_argument("--pairs", type=int, default=5000)
+    result.add_argument("--allow-downloads", action="store_true")
+    return result
+
+
+def main() -> None:
+    args = parser().parse_args()
+    args.output.mkdir(parents=True, exist_ok=True)
+    seeds = tuple(SEED + 100 * index for index in range(args.seeds))
+    (
+        expanded,
+        historical,
+        library,
+        coverage,
+        raw_paths,
+        raw_summary,
+        representation_summary,
+    ) = load_expanded_corpus(
+        args.library, args.old_balanced, args.release_v4, not args.allow_downloads
+    )
+    dataset_summary(expanded).to_csv(args.output / "dataset_summary.csv", index=False)
+    coverage.to_csv(args.output / "label_coverage.csv", index=False)
+    raw_summary.to_csv(args.output / "raw_database_summary.csv", index=False)
+    representation_summary.to_csv(args.output / "representation_coverage.csv", index=False)
+
+    matrix_audit = audit_exp8_matrices(args.exp8_results, expanded)
+    matrix_audit.to_csv(args.output / "exp8_matrix_audit.csv", index=False)
+    matrix_summary = matrix_audit.describe(percentiles=[0.5, 0.9, 0.95, 0.99]).T
+    matrix_summary.to_csv(args.output / "exp8_matrix_audit_summary.csv")
+    database_audit = database_matrix_audit(expanded)
+    database_audit.to_csv(args.output / "database_matrix_audit_rows.csv", index=False)
+    database_audit.groupby("domain").agg(
+        rows=("source_id", "size"),
+        maximum_reciprocal_relative_asymmetry=("maximum_reciprocal_relative_asymmetry", "max"),
+        positive_off_diagonal_entries=("positive_off_diagonal_entries", "sum"),
+        median_row_sum_fraction=("maximum_row_sum_fraction", "median"),
+        p95_row_sum_fraction=("maximum_row_sum_fraction", lambda values: values.quantile(0.95)),
+        minimum_eigenvalue_fF=("minimum_eigenvalue_fF", "min"),
+    ).reset_index().to_csv(args.output / "database_matrix_audit_summary.csv", index=False)
+
+    within = within_domain_study(expanded, seeds)
+    within.to_csv(args.output / "within_domain_predictive.csv", index=False)
+    cross_campaign = cross_campaign_study(expanded, seeds)
+    cross_campaign.to_csv(args.output / "cross_campaign_zero_shot.csv", index=False)
+    topology = topology_zero_shot(expanded, historical, seeds)
+    topology.to_csv(args.output / "topology_zero_shot.csv", index=False)
+
+    low, assignments = low_shot_topology(
+        expanded, historical, seeds, args.draws, (1, 3, 10, 30, 100)
+    )
+    low.to_csv(args.output / "topology_low_shot_metrics.csv", index=False)
+    summarize_metrics(
+        low,
+        ["held_family", "target", "source_protocol", "budget", "method"],
+    ).to_csv(args.output / "topology_low_shot_summary.csv", index=False)
+    paired_topology_comparisons(low).to_csv(
+        args.output / "topology_paired_comparisons.csv", index=False
+    )
+    assignments.to_csv(args.output / "topology_split_assignments.csv", index=False)
+
+    campaign_low = low_shot_campaign(
+        expanded, seeds, args.draws, (1, 3, 10, 30, 100)
+    )
+    campaign_low.to_csv(args.output / "campaign_low_shot_metrics.csv", index=False)
+    summarize_metrics(
+        campaign_low,
+        ["held_campaign", "budget", "method"],
+    ).to_csv(args.output / "campaign_low_shot_summary.csv", index=False)
+
+    similarity_study(expanded, args.pairs).to_csv(
+        args.output / "similarity_correlations.csv", index=False
+    )
+    ood_rows, ood_summary, neighbor_mix = ood_support_study(expanded, library)
+    ood_rows.to_csv(args.output / "unlabeled_ood_support_rows.csv", index=False)
+    ood_summary.to_csv(args.output / "unlabeled_ood_support_summary.csv", index=False)
+    neighbor_mix.to_csv(args.output / "unlabeled_nearest_domain_mix.csv", index=False)
+
+    matrix_spectrum_study(expanded, seeds, False).to_csv(
+        args.output / "gidc_matrix_spectrum_within.csv", index=False
+    )
+    matrix_spectrum_study(expanded, seeds, True).to_csv(
+        args.output / "gidc_matrix_spectrum_cross_campaign.csv", index=False
+    )
+
+    manifest = {
+        "study": "expanded-capacitance-transfer-v2",
+        "seed": SEED,
+        "labeled_rows": len(expanded),
+        "labeled_rows_by_domain": expanded.domain.value_counts().sort_index().to_dict(),
+        "historical_comparator_rows": len(historical),
+        "study_library_rows": len(library),
+        "exp8_raw_matrices": len(matrix_audit),
+        "exp8_raw_matrix_set_sha256": hashlib.sha256(
+            "\n".join(
+                f"{row.source_id}:{row.csv_sha256}" for row in matrix_audit.itertuples()
+            ).encode("utf-8")
+        ).hexdigest(),
+        "protocol": {
+            "targets": ["ln(mutual_fF)", "ln(ground_a_fF + ground_b_fF)"],
+            "representation": "planar-terminal-graph-v4 permutation-invariant 544-D layout view",
+            "canonical_proxy": "v4 graph proxy matrix stored per layout",
+            "topology_targets": [CAPN, TRANSMON],
+            "labeled_gidc_campaigns": list(LABELED_GIDC_CAMPAIGNS),
+            "unlabeled_ood_campaigns": list(UNLABELED_CAMPAIGNS),
+            "budgets": [0, 1, 3, 10, 30, 100],
+            "draws": args.draws,
+            "model_seeds": list(seeds),
+            "held_target_test_fraction": 0.5,
+            "within_domain_test_fraction": 0.2,
+        },
+        "input_sha256": {
+            str(args.library): sha256(args.library),
+            str(args.old_balanced): sha256(args.old_balanced),
+            str(args.release_v4): sha256(args.release_v4),
+            **{str(path): sha256(path) for path in raw_paths.values()},
+        },
+        "implementation_sha256": sha256(Path(__file__)),
+        "runtime": {
+            "python": sys.version,
+            "platform": platform.platform(),
+            "numpy": np.__version__,
+            "pandas": pd.__version__,
+        },
+        "limitations": [
+            "Concentric, star, exp9, and Firas campaigns have v4 geometry/proxy data but no joined high-fidelity labels.",
+            "Only two non-GIDC labeled topology families are available for strict topology transfer.",
+            "The three historical topology families influenced representation development; topology LOFO remains exploratory.",
+            "RFF ridge is a controlled nonlinear probe, not the proposed final graph neural network.",
+        ],
+    }
+    (args.output / "manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8")
+    print(
+        json.dumps(
+            {
+                "output": str(args.output),
+                "labeled_rows": len(expanded),
+                "exp8_matrices": len(matrix_audit),
+                "files": len(list(args.output.iterdir())),
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

studies/expanded-capacitance-transfer-v2/similarity_correlations.csv

@@ -0,0 +1,56 @@
+stratum,domain_a,domain_b,relation,outcome,pairs,spearman_rho
+within,exp7_exports,exp7_exports,v4_layout_distance,absolute_log_mutual_difference,5000,0.45445514898253064
+within,exp7_exports,exp7_exports,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.4679947872579653
+within,exp7_exports,exp7_exports,v4_layout_distance,joint_log_response_distance,5000,0.5354679620180407
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+within,exp8_exports,exp8_exports,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.561877301963092
+within,exp8_exports,exp8_exports,v4_layout_distance,joint_log_response_distance,5000,0.629842451465698
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+within,exp8_exports,exp8_exports,proxy_ground_delta,matching_target_delta,5000,0.3003695240787809
+within,exports,exports,v4_layout_distance,absolute_log_mutual_difference,5000,0.7223723879189388
+within,exports,exports,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.6260365658617613
+within,exports,exports,v4_layout_distance,joint_log_response_distance,5000,0.7445530062205515
+within,exports,exports,proxy_mutual_delta,matching_target_delta,5000,0.03681227476309293
+within,exports,exports,proxy_ground_delta,matching_target_delta,5000,0.16912317647763453
+within,release_CapNInterdigitalTee,release_CapNInterdigitalTee,v4_layout_distance,absolute_log_mutual_difference,5000,0.7083227336548947
+within,release_CapNInterdigitalTee,release_CapNInterdigitalTee,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.7777149658391779
+within,release_CapNInterdigitalTee,release_CapNInterdigitalTee,v4_layout_distance,joint_log_response_distance,5000,0.7667147672718578
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+within,release_CapNInterdigitalTee,release_CapNInterdigitalTee,proxy_ground_delta,matching_target_delta,5000,0.3292988654863387
+within,release_TransmonCross,release_TransmonCross,v4_layout_distance,absolute_log_mutual_difference,5000,0.41743459569356817
+within,release_TransmonCross,release_TransmonCross,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.7635343440526221
+within,release_TransmonCross,release_TransmonCross,v4_layout_distance,joint_log_response_distance,5000,0.6661144683564463
+within,release_TransmonCross,release_TransmonCross,proxy_mutual_delta,matching_target_delta,5000,0.4760228940511613
+within,release_TransmonCross,release_TransmonCross,proxy_ground_delta,matching_target_delta,5000,0.11597541514685072
+cross_campaign,exports,exp7_exports,v4_layout_distance,absolute_log_mutual_difference,5000,0.4381361934294477
+cross_campaign,exports,exp7_exports,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.5047615273264611
+cross_campaign,exports,exp7_exports,v4_layout_distance,joint_log_response_distance,5000,0.5176286905611476
+cross_campaign,exports,exp7_exports,proxy_mutual_delta,matching_target_delta,5000,0.050504251972170074
+cross_campaign,exports,exp7_exports,proxy_ground_delta,matching_target_delta,5000,0.24741244541649776
+cross_campaign,exports,exp8_exports,v4_layout_distance,absolute_log_mutual_difference,5000,0.5867528065421123
+cross_campaign,exports,exp8_exports,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.575979203807168
+cross_campaign,exports,exp8_exports,v4_layout_distance,joint_log_response_distance,5000,0.6496829522593179
+cross_campaign,exports,exp8_exports,proxy_mutual_delta,matching_target_delta,5000,0.07482545142681388
+cross_campaign,exports,exp8_exports,proxy_ground_delta,matching_target_delta,5000,0.24423790461751615
+cross_campaign,exp7_exports,exp8_exports,v4_layout_distance,absolute_log_mutual_difference,5000,0.4923756405026597
+cross_campaign,exp7_exports,exp8_exports,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.618841810527377
+cross_campaign,exp7_exports,exp8_exports,v4_layout_distance,joint_log_response_distance,5000,0.6171878543011393
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+cross_campaign,exp7_exports,exp8_exports,proxy_ground_delta,matching_target_delta,5000,0.36154807913527753
+cross_topology,exp8_exports,release_CapNInterdigitalTee,v4_layout_distance,absolute_log_mutual_difference,5000,0.5768237051849482
+cross_topology,exp8_exports,release_CapNInterdigitalTee,v4_layout_distance,absolute_log_ground_sum_difference,5000,0.23269298754771947
+cross_topology,exp8_exports,release_CapNInterdigitalTee,v4_layout_distance,joint_log_response_distance,5000,0.555463925418557
+cross_topology,exp8_exports,release_CapNInterdigitalTee,proxy_mutual_delta,matching_target_delta,5000,-0.040773893278955725
+cross_topology,exp8_exports,release_CapNInterdigitalTee,proxy_ground_delta,matching_target_delta,5000,-0.5337254174770166
+cross_topology,exp8_exports,release_TransmonCross,v4_layout_distance,absolute_log_mutual_difference,5000,0.10682054760082188
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+cross_topology,exp8_exports,release_TransmonCross,proxy_mutual_delta,matching_target_delta,5000,0.1321587474303499
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+cross_topology,release_CapNInterdigitalTee,release_TransmonCross,v4_layout_distance,joint_log_response_distance,5000,0.21202799425853852
+cross_topology,release_CapNInterdigitalTee,release_TransmonCross,proxy_mutual_delta,matching_target_delta,5000,0.016609198078745856
+cross_topology,release_CapNInterdigitalTee,release_TransmonCross,proxy_ground_delta,matching_target_delta,5000,0.29548212137275

studies/expanded-capacitance-transfer-v2/topology_low_shot_summary.csv

@@ -0,0 +1,93 @@
+held_family,target,source_protocol,budget,method,median_relative_error_mean,median_relative_error_std,p90_relative_error_mean,p90_relative_error_std,bias_log_mean,bias_log_std,slope_log_mean,slope_log_std,r2_log_mean,r2_log_std,medae_fF_mean,medae_fF_std,rmse_fF_mean,rmse_fF_std
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studies/expanded-capacitance-transfer-v2/topology_paired_comparisons.csv

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studies/expanded-capacitance-transfer-v2/topology_zero_shot.csv

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studies/expanded-capacitance-transfer-v2/unlabeled_nearest_domain_mix.csv

@@ -0,0 +1,16 @@
+campaign,nearest_labeled_family,nearest_labeled_domain,rows
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+exp9_exports,GeneralizedCapNInterdigital,exports,3953
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+firas_sims_exp1,GeneralizedCapNInterdigital,exports,447
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+firas_sims_exp2,GeneralizedCapNInterdigital,exp8_exports,839
+firas_sims_exp2,GeneralizedCapNInterdigital,exports,1106

studies/expanded-capacitance-transfer-v2/unlabeled_ood_support_summary.csv

@@ -0,0 +1,6 @@
+campaign,family,rows,median_nearest_distance,p90_nearest_distance,p95_nearest_distance,median_proxy_mutual_fF,median_proxy_ground_sum_fF
+firas_sims_exp1,GeneralizedCapNInterdigital,1000,0.30927532143073044,0.4304579170789471,0.46689525720874714,1.1139999136600727,4.502839207608435
+firas_sims_exp2,GeneralizedCapNInterdigital,2082,0.31917721017534617,0.5009230848106508,0.5501872452703083,1.1131259596239231,2.800066944137839
+exp9_exports,GeneralizedCapNInterdigital,15000,1.5815282603253484,2.354972802035698,2.4966442209056114,1.2133606293156483,5.10022116520272
+exp11_exports,GeneralizedCapStar,2000,3.062113000482955,3.3003222208087974,3.349930556792923,3.0164788379006273,10.558239763550416
+exp10_exports,GeneralizedCapConcentric,2000,3.353176442443611,4.110347431808498,4.243257815081421,15.917750495472225,28.90363923829596

studies/expanded-capacitance-transfer-v2/within_domain_predictive.csv

@@ -0,0 +1,121 @@
+domain,family,target,method,seed,source_offset,n_test,median_relative_error,p90_relative_error,p95_relative_error,bias_log,slope_log,r2_log,medae_fF,rmse_fF
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+exp7_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_proxy_residual,20261401,1.1267851987460638,288,0.18908354435028568,0.5136976369517372,0.6324235746470906,-0.02395012816542258,0.9581161772933445,0.668766918935511,0.5373463415953879,1.680077493732011
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+exp7_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261201,1.5882159440481904,288,0.023632680185085464,0.06325342547976708,0.07826181241978747,0.0005927240741790499,0.9711629819429913,0.9859922325082556,0.27904344828061767,0.6747909065215344
+exp7_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261201,1.5882159440481904,288,0.06484784382621511,0.20804730064496105,0.2684631677188335,-0.0021329418178276166,0.9607450727247379,0.8955430746305314,0.8231138797206601,1.700211872024502
+exp7_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261301,1.5882159440481904,288,0.02305823870010093,0.06344805739307098,0.07663031042572442,-0.0015803798936763712,0.9646889141819661,0.9882816683235651,0.2943438299480037,0.5801744994962507
+exp7_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261301,1.5882159440481904,288,0.060699444762935595,0.16458413610634606,0.20995479790736482,-0.008236732804103654,0.9720353750652477,0.9254409668044308,0.7659003403328759,1.521880850243854
+exp7_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261401,1.5882159440481904,288,0.018456586703995193,0.06565415156393711,0.09610744827457146,-0.0038590016712250663,0.9577588543003966,0.9852448473681114,0.23584304063433503,0.7653642780487032
+exp7_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261401,1.5882159440481904,288,0.060700910859819435,0.15638061055737076,0.19260251084818195,-0.0007787329752889009,0.9599168292409974,0.9407170649979775,0.726907661477477,1.3520473479264632
+exp8_exports,GeneralizedCapNInterdigital,mutual,P0_raw_proxy,-1,,3276,0.7242532600505216,0.9709130850050204,0.986819013607536,-1.622476834251216,-0.06419862378093977,-5.9101944552441585,3.5348171735427263,6.517073346979242
+exp8_exports,GeneralizedCapNInterdigital,mutual,P1_source_offset,-1,1.2835940695502774,3276,0.7742068084630633,1.7040506985349684,1.9484547017680152,-0.3388827647009384,-0.06419862378093981,-1.9783671616272125,3.2165457568409344,6.305629894345295
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_direct,20261001,1.2835940695502774,3276,0.0498652039551733,0.13623323563452483,0.17677342775977795,0.0006605168641538121,0.9856135545860898,0.9878956757172154,0.22441102695043447,0.7222815498918551
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_proxy_residual,20261001,1.2835940695502774,3276,0.22807367857119665,0.7031884878014774,0.9010118000092484,-0.010037268060560564,0.9838485826219573,0.6152958117031868,1.0381580646570845,4.696255342165742
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+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_proxy_residual,20261101,1.2835940695502774,3276,0.2164123253743404,0.6991840765655636,0.8890061739117425,-0.01805357737744558,0.9798407666114425,0.6324714675134739,0.940562769686222,4.452589735227361
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_direct,20261201,1.2835940695502774,3276,0.047284146333766375,0.1336817229347358,0.17379433188869875,-0.00030720821999779565,0.9889516651020009,0.9883819498461377,0.21140028111450238,0.6579380998834636
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_proxy_residual,20261201,1.2835940695502774,3276,0.23184697484847605,0.7120279052841582,0.8820913623168701,-0.010679833309725065,0.9870316623238607,0.6282270392642908,1.0722536028949867,4.225253178054448
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_direct,20261301,1.2835940695502774,3276,0.043701130436411884,0.11780181962795916,0.1459323086948062,0.0008334063668620472,0.9916436767651658,0.9913049592557104,0.18964377347780853,0.5922330991213388
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_proxy_residual,20261301,1.2835940695502774,3276,0.245822177747546,0.7636970809055443,0.9642744433881286,-0.008914633617997792,0.9651780769096876,0.5726729823749981,1.1003843166736922,4.007440854673496
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_direct,20261401,1.2835940695502774,3276,0.04791185040383844,0.1224182655427773,0.15198654624848715,-0.0015902125154902123,0.990355972134932,0.9899320126182812,0.22173860734516793,0.6752961844323098
+exp8_exports,GeneralizedCapNInterdigital,mutual,RFF_v4_layout_proxy_residual,20261401,1.2835940695502774,3276,0.23844882982850912,0.7063074783204883,0.8787680880659303,-0.0028833136308199104,0.9934922884747155,0.6422485470262356,1.068196471060483,3.9302114953986735
+exp8_exports,GeneralizedCapNInterdigital,ground_sum,P0_raw_proxy,-1,,3276,0.8214712158308635,0.9065870789962001,0.9235271609769942,-1.7848866106769052,0.5487114366571822,-13.649767838072862,17.049703299277688,20.425946389224134
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+exp8_exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261001,1.7263749158815287,3276,0.06246472871790576,0.16771127521235124,0.20328199366737493,0.0013603072860101303,0.9863674945154115,0.9532034165236456,1.1604971697956508,2.4606130596064566
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+exports,GeneralizedCapNInterdigital,ground_sum,P0_raw_proxy,-1,,449,0.8028218274139376,0.8989384546058411,0.9193886034533234,-1.6812915669617463,0.24949206930658627,-20.896512618259692,14.15768365468585,15.962634105131396
+exports,GeneralizedCapNInterdigital,ground_sum,P1_source_offset,-1,1.6145424618499113,449,0.23169262768471754,0.6153110706696437,0.6666010862751814,-0.06674910511183516,0.2494920693065862,-0.18304121627632575,3.7291642164668346,6.920116867158837
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261001,1.6145424618499113,449,0.02140443958964955,0.05569850649513035,0.06962899659643242,2.9217781882856e-05,0.9789196081718556,0.9906981119365437,0.3539752861663459,0.6847313738601424
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261001,1.6145424618499113,449,0.05510686385334562,0.1486004718261088,0.18039799302239504,-0.004725839692393459,0.9488284332402257,0.941225811515997,0.8642071708331134,1.7787910117757664
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261101,1.6145424618499113,449,0.02048411617164314,0.052100147119944304,0.06950072476475237,-0.002023150137610887,0.981510710397977,0.9916567972778928,0.3418658964793284,0.6780995515815618
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261101,1.6145424618499113,449,0.05015242259431063,0.13798052338661104,0.17211582358668812,-0.0060399651487428666,0.9688581518806809,0.9468916496093789,0.7983203619656738,1.741653750187048
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261201,1.6145424618499113,449,0.016733552787821848,0.04489257112696232,0.05900798005572833,0.000917776595473865,0.9805288548750555,0.9930478421657215,0.28336297551535594,0.5992452243654789
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261201,1.6145424618499113,449,0.05374706767712334,0.15323928832982672,0.1901106186468417,-0.002813777182424074,0.9449708568833374,0.9355729157159405,0.917895009041608,1.8633798123085226
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261301,1.6145424618499113,449,0.019146145446589102,0.047325865038880144,0.05845066459522003,0.0007157602418107298,0.9796525196491239,0.992297797239003,0.3213016856455493,0.6559196985846745
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261301,1.6145424618499113,449,0.04664104377879541,0.1311299389587435,0.16155408281091843,0.001258403211373283,0.9599415330268556,0.9532859203392621,0.7545070447053135,1.5942752773571514
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_direct,20261401,1.6145424618499113,449,0.018868234222549907,0.05395813476036538,0.07131644482246631,-0.0008513316048191053,0.9775638735721703,0.9910716539025312,0.29945838763525146,0.6984751203367424
+exports,GeneralizedCapNInterdigital,ground_sum,RFF_v4_layout_proxy_residual,20261401,1.6145424618499113,449,0.045330158909166696,0.11816089147295583,0.13857334331367915,0.0012380417393347122,0.952366920978233,0.959346262381735,0.7118701742595839,1.476599332669562
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,P0_raw_proxy,-1,,179,0.9607599145903863,0.9919601384018506,0.9955940990879173,-3.036345532048919,-0.2541115372823518,-9.049267454231225,11.874303295694888,16.649438300846334
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,P1_source_offset,-1,3.1883477204400403,179,0.7228997558935477,6.742629395996557,18.834304954151367,0.15200218839112126,-0.25411153728235225,-1.113687908994232,10.665683834637168,13.980164874066448
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_direct,20261001,3.1883477204400403,179,0.05439526762208091,0.16808344896619015,0.2426429979420631,-0.005429687709724559,0.9818927969245118,0.989068471232935,0.53717957187561,1.6407858329039111
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_proxy_residual,20261001,3.1883477204400403,179,0.21632770572723714,0.7196821957176526,0.9409190834653857,-0.025647343880656422,0.9664961657418534,0.7875373744878698,1.6518581953240812,8.0580305023483
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_direct,20261101,3.1883477204400403,179,0.050030249642848745,0.17638853166422655,0.2217775112909787,0.0015428849357232543,0.9777894377698252,0.9895743194978224,0.39819121048985906,1.7883201000391051
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_proxy_residual,20261101,3.1883477204400403,179,0.17708060883000443,0.729097446173809,0.8969812098876739,-0.02457795171059487,0.9624183325972586,0.8004116212687964,1.8073043209994069,7.550776260772086
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_direct,20261201,3.1883477204400403,179,0.07125191481439219,0.18781664025797543,0.22907824494850607,0.020100144400540927,0.9782990497400915,0.9898004138785139,0.5476996905496523,1.9264338141749209
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_proxy_residual,20261201,3.1883477204400403,179,0.20716620348221204,0.7088063011637127,0.9554150352299805,-0.004969875971160697,0.9764306150757792,0.8110373126570145,2.136277595358469,7.026210286402598
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_direct,20261301,3.1883477204400403,179,0.05418786668800046,0.17697937332028618,0.2421160483899709,0.002978873701452804,0.9798706914560141,0.9873006574132249,0.46347298228986133,1.8832356328013191
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_proxy_residual,20261301,3.1883477204400403,179,0.21387998542477993,0.6455041872084433,0.8984721723643982,-0.028098296005401414,0.9554718406226248,0.7962166166949574,1.8467253619835626,7.839548722199212
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_direct,20261401,3.1883477204400403,179,0.058879516582850844,0.14738119043843426,0.20183164279227805,0.0011035121826412492,0.9769441210974885,0.9903539744841127,0.4838235120883745,1.5528124984768787
+release_CapNInterdigitalTee,CapNInterdigitalTee,mutual,RFF_v4_layout_proxy_residual,20261401,3.1883477204400403,179,0.2252302087844018,0.7060084392015789,0.9178925138574905,-0.029055541700104374,0.9594479517567236,0.8011516796476225,2.193700932309568,7.699934289592186
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,P0_raw_proxy,-1,,179,0.9677699714121136,0.9775099686370569,0.9813776940125428,-3.3937831617229652,0.7213125217234322,-77.51129900360122,56.30428669677094,62.673891605264195
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,P1_source_offset,-1,3.4420511906374047,179,0.1978486358133411,0.970172049167662,1.4894963690512395,0.04826802891443918,0.7213125217234331,-0.5891377185854436,12.453588472325634,24.081266883946046
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_direct,20261001,3.4420511906374047,179,0.020092305753325324,0.06098690959180737,0.08371084242954549,-0.002581687812405844,0.9673663259254751,0.9880641918738661,1.1659627019758076,3.314934473789195
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_proxy_residual,20261001,3.4420511906374047,179,0.07291803638562322,0.2340670061249162,0.29045783577747925,0.008497382309655181,0.9652536797074497,0.7771644727514373,4.076699829557313,6.905713378023417
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_direct,20261101,3.4420511906374047,179,0.026309419432478,0.0688354581615565,0.09021925881522427,0.0005985336460595594,0.9610545077736579,0.9868599280733948,1.284696945075524,3.4852487018938514
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_proxy_residual,20261101,3.4420511906374047,179,0.06765180913733704,0.20590007668186122,0.2340005182434624,0.007614692607527363,0.9910739971748472,0.8124777929321578,3.6820795887148776,6.447600496227974
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_direct,20261201,3.4420511906374047,179,0.01934397455142478,0.05519085674054824,0.07982844498332872,0.0027822422454742463,0.9711765295412017,0.9907318799955446,1.0111270006630946,2.847958636761289
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_proxy_residual,20261201,3.4420511906374047,179,0.08643933606705878,0.2394227343658033,0.3131926670240873,0.003345501657399845,0.9782994664510771,0.7276447379644182,4.896500487282154,8.218785503793498
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_direct,20261301,3.4420511906374047,179,0.02052287113613538,0.054940134362718454,0.07048454860353083,0.002000783173841603,0.970803868966335,0.9880501622131197,1.0075544399511074,3.491635083261747
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_proxy_residual,20261301,3.4420511906374047,179,0.06377444663226028,0.2065709916652038,0.2843845373535374,0.0037101198930674308,0.9617178226949467,0.778531358465602,3.6330821474886363,6.678830468868706
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_direct,20261401,3.4420511906374047,179,0.018932524215520496,0.05192896587802797,0.08398990822042034,-0.0010315610801697572,0.962243033795035,0.990352726284586,1.1508965772380364,2.4827731768219414
+release_CapNInterdigitalTee,CapNInterdigitalTee,ground_sum,RFF_v4_layout_proxy_residual,20261401,3.4420511906374047,179,0.06273408147773218,0.25567860329447994,0.30443966800959604,0.007252932977753912,0.9522623388156115,0.7820434486698105,3.7169956857468165,6.988123112624241
+release_TransmonCross,TransmonCross,mutual,P0_raw_proxy,-1,,387,1.5012066821763894,2.360296909616815,2.5403958786391336,0.8165305473753294,0.41137749458226525,-2.5704806836822867,6.408137295888795,7.210142868229758
+release_TransmonCross,TransmonCross,mutual,P1_source_offset,-1,-0.9070124827544038,387,0.17796578388951512,0.5372083076896011,0.6425165485214392,-0.09048193537907456,0.41137749458226475,0.13328743695527434,0.7242068931869914,2.5018329196548508
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_direct,20261001,-0.9070124827544038,387,0.019631723599978668,0.047148310479043554,0.05698389518158816,-0.002484673445336028,0.976179332285825,0.995597205161535,0.08710404013253381,0.2537527323544554
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_proxy_residual,20261001,-0.9070124827544038,387,0.046061443325760676,0.12296242766750615,0.15205550851244673,-0.007001245595767445,0.9403829266793978,0.9607087036400961,0.1994631720052542,0.8179586823346982
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_direct,20261101,-0.9070124827544038,387,0.023628846390658764,0.05725191940247188,0.07770463804118172,-0.0025853758852602132,0.9701578475700607,0.9943529576697842,0.10531451587767648,0.23013139138559605
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_proxy_residual,20261101,-0.9070124827544038,387,0.04560864547380883,0.11377081353440617,0.14362057370795417,-0.003690841265597831,0.9443579025774667,0.971414292036478,0.18367274497930275,0.6671026611130749
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_direct,20261201,-0.9070124827544038,387,0.028414089866022008,0.0644979290242541,0.08098295727640838,-0.0017383813201956482,0.9646634415259978,0.9925823929124007,0.12166993443104257,0.29461437930702833
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_proxy_residual,20261201,-0.9070124827544038,387,0.0521904858820641,0.14062204975344797,0.1654466777160835,-0.002738031990068596,0.9390078628540591,0.9651519254861892,0.2096208670795221,0.6817629241281349
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_direct,20261301,-0.9070124827544038,387,0.02256273429605793,0.058845819300592364,0.07052947643028924,-0.003064110457141863,0.974768073215622,0.9945389099831785,0.09707117618194161,0.237514187070254
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_proxy_residual,20261301,-0.9070124827544038,387,0.05293340478756681,0.1490516020357644,0.1760893457999115,-0.002568930598160068,0.9506621488351965,0.9601171078353684,0.22128284835954615,0.7154947645496857
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_direct,20261401,-0.9070124827544038,387,0.025650990999950718,0.07984656962557636,0.09979668706222425,-0.0036010351235953,0.9699072409568231,0.99100212396897,0.11513890878528699,0.26314420012238776
+release_TransmonCross,TransmonCross,mutual,RFF_v4_layout_proxy_residual,20261401,-0.9070124827544038,387,0.050035596738913446,0.1402592105034265,0.18065108412643469,-0.004752034726053623,0.9494778447547627,0.9655098483928906,0.22782013627124087,0.6653651741990662
+release_TransmonCross,TransmonCross,ground_sum,P0_raw_proxy,-1,,387,0.9861864202608648,0.9933694103098586,0.99514664194946,-4.307140515576169,-0.09993157009394532,-272.6359748756654,245.66923665682089,250.83554902634611
+release_TransmonCross,TransmonCross,ground_sum,P1_source_offset,-1,4.28730580421059,387,0.3311082142167219,0.9402756785508017,1.6067603285418022,-0.019834711365578944,-0.09993157009394495,-4.606562077605879,84.08238100732522,158.92009072241558
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_direct,20261001,4.28730580421059,387,0.01858288707235245,0.03482149265174922,0.04048846765091895,-0.0014610180572188554,0.9776691769623317,0.9920030216225876,4.487784085022724,5.913351278506022
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_proxy_residual,20261001,4.28730580421059,387,0.0610092931058408,0.17430893983461146,0.2313553235064352,-0.0030144968228427663,0.9876815466787169,0.7344023588936115,14.729040347978923,30.226433861218084
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_direct,20261101,4.28730580421059,387,0.0181422342787394,0.034717783062574545,0.043854016667445664,-0.0015914031985845838,0.9765043001387228,0.991714859617084,4.356592578808545,6.40527467142121
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_proxy_residual,20261101,4.28730580421059,387,0.0539978457246445,0.1429012122086905,0.19622673397231227,-0.0036740925270515646,1.0093337582460953,0.784559957313901,12.30747597541881,27.868373403277996
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_direct,20261201,4.28730580421059,387,0.01842730539765758,0.031998234864068456,0.037650548968949836,-0.001494701583025617,0.9833604625160585,0.9926095110012128,4.309954822006972,5.716045369850028
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_proxy_residual,20261201,4.28730580421059,387,0.06826963682602338,0.18993379260604554,0.23299946688068696,-0.0119840844739865,0.9877270917881754,0.7050822208444538,16.4465742699484,32.828513453959104
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_direct,20261301,4.28730580421059,387,0.017777028744621837,0.03288462921050432,0.039137333345473015,-0.0013211930818228905,0.9734524162843471,0.9918698056646099,4.140965449873519,6.529686276444381
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_proxy_residual,20261301,4.28730580421059,387,0.06673271703089523,0.17762695308410614,0.2543095815898274,-0.004898000979447563,0.9634058638845983,0.6845041487877761,15.531493193994322,32.90608809873852
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_direct,20261401,4.28730580421059,387,0.018617594236010504,0.03337741848107697,0.03927339130304974,-0.00211526072868763,0.9827766265779964,0.992468506110085,4.140622893736435,5.878043244013486
+release_TransmonCross,TransmonCross,ground_sum,RFF_v4_layout_proxy_residual,20261401,4.28730580421059,387,0.05283591084305961,0.1374679228238057,0.19104206365498894,-0.009736598698941955,0.981869204548366,0.8071909632650467,12.245001427859421,26.207203068861766