diff --git a/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/donor_identity_map.csv b/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/donor_identity_map.csv
new file mode 100644
index 0000000000000000000000000000000000000000..46c47779d2af70a74cd4ec65295c939c059dc061
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/donor_identity_map.csv
@@ -0,0 +1,939 @@
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new file mode 100644
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+    "sha256": "5d457b36706d41ad26147783133b50eb939ed597b34abeea6b6467b20633fb24"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/data/c/356",
+    "bytes": 360011,
+    "sha256": "5a127f31e4c0a2333b24dce29f0a158cf1ec020a7e0a5c7dd04d200059a8fa58"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/indices/c/357",
+    "bytes": 1293434,
+    "sha256": "d35fb0597537d8872659412484f3062db005e657882551329269c5cd87505ffe"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/data/c/357",
+    "bytes": 333502,
+    "sha256": "0930ac9ca9588b5327575f06cf8f1f4e4198b431d5b22169131288e01144dfc1"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/indices/c/358",
+    "bytes": 1290832,
+    "sha256": "954a4924a5f9ce56c1d7c6696ca100951334961d915bb7e2b016c559e98021a5"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/data/c/358",
+    "bytes": 313518,
+    "sha256": "d0047659a9019b4867572f756acb21643dc601270d8f31ff9ba1bdc784a08cf4"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/indices/c/359",
+    "bytes": 1297422,
+    "sha256": "dc69b82e43c4dad5325b4874e0f272dcc4983beb2a96cf732adeafe8f42f3200"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/data/c/359",
+    "bytes": 304144,
+    "sha256": "b76fcb61a15afcda93b8d33462d3e9a0febab9b3a1076e20f8ac8c06b81e0e43"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/indices/c/360",
+    "bytes": 846728,
+    "sha256": "6ec81b30931553ddd4aa8353074c955c310e628236bf498402d8d754be8fc074"
+  },
+  {
+    "path": "/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood/counts/sc_counts/304_A_Parse_10M_PBMC_12donor_90cytokines__part_038__1d68cb1787d4.zarr/data/c/360",
+    "bytes": 206054,
+    "sha256": "41c01de4eaebb91120b52cec4809842cf2dfeb0f1494c538bebd5a020d323800"
+  }
+]
diff --git a/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_code_snapshot.py b/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_code_snapshot.py
new file mode 100644
index 0000000000000000000000000000000000000000..73f5d0f09bcedbb4a2e2bc93a76c04d25977e4ce
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_code_snapshot.py
@@ -0,0 +1,342 @@
+#!/usr/bin/env python3
+"""Bounded, CPU-only source-labelled TRAIN ILC marker support audit.
+
+Reuses the chunk/row algorithm in corpus_macrophage_markers_20261015.
+Selection is fixed by source L1 annotation and registered split, never expression.
+"""
+from __future__ import annotations
+
+import argparse
+from collections import defaultdict
+import hashlib
+import json
+from pathlib import Path
+import resource
+import time
+
+import h5py
+import numpy as np
+import pandas as pd
+import pyarrow.parquet as pq
+import zarr
+
+ROOT = Path(__file__).resolve().parent
+PLAN = ROOT.parents[1]
+M = PLAN.parents[1]
+M7 = M.parent / 'model_7_no_annotation_sh_hwang/data'
+E2 = M7 / 'e2_full_source'
+BLOCK = Path('/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood')
+KEY = ['dataset_id', 'donor_id', 'sample_id']
+SOURCE_CT = Path('/home/sj_server_1/PBISC/paper-release-20260928/manuscript/source_data/corpus/corpus_celltypes.csv')
+zarr.config.set({'async.concurrency': 2, 'threading.max_workers': 2})
+
+
+def sha(path: Path) -> str:
+    h = hashlib.sha256()
+    with Path(path).open('rb') as f:
+        for b in iter(lambda: f.read(1 << 20), b''): h.update(b)
+    return h.hexdigest()
+
+
+def receipt(path: Path) -> dict:
+    p = Path(path)
+    return {'path': str(p), 'bytes': p.stat().st_size, 'sha256': sha(p)}
+
+
+def dump(name: str, value: dict | list) -> None:
+    (ROOT / name).write_text(json.dumps(value, indent=2) + '\n')
+
+
+def targets() -> pd.DataFrame:
+    p = ROOT / 'locked_target_genes.csv'
+    t = pd.read_csv(p)
+    assert len(t) == 48 and not t.gene_id.duplicated().any()
+    return t
+
+
+def prepare() -> None:
+    start = time.time()
+    target_source = PLAN / 'ilc_targeted_audit_genes.csv'
+    t = pd.read_csv(target_source)
+    t.to_csv(ROOT / 'locked_target_genes.csv', index=False)
+    t = targets()
+    cols = t.raw_panel_position.to_numpy(dtype=int)
+    var = pd.read_parquet(E2 / 'metadata/var.parquet')
+    assert len(var) == 20097
+    assert var.iloc[cols].gene_id.tolist() == t.gene_id.tolist()
+    assert var.iloc[cols].gene_symbol.tolist() == t.gene_symbol.tolist()
+    assert var.iloc[cols].panel_index.tolist() == cols.tolist()
+    cfgp = M / 'runs/f01_6k_1x_seed20260627/effective_config.json'
+    cfg = json.loads(cfgp.read_text())['data']
+    assert cfg['n_genes'] == 6000
+    manifest_path = Path(cfg['gene_selection_manifest'])
+    position_path = Path(cfg['gene_positions_path'])
+    assert sha(manifest_path) == cfg['gene_selection_manifest_sha256']
+    assert sha(position_path) == cfg['gene_positions_sha256']
+    manifest = json.loads(manifest_path.read_text())
+    selected_path = Path(manifest['selected_genes_path'])
+    assert sha(selected_path) == manifest['selected_genes_sha256']
+    selected = pd.read_csv(selected_path)
+    assert len(selected) == 6000 and not selected.gene_id.duplicated().any()
+    assert np.array_equal(np.load(position_path), selected.panel_position)
+    assert var.iloc[selected.panel_position].gene_id.tolist() == selected.gene_id.tolist()
+    assert np.array_equal(t.in_final6k, t.gene_id.isin(selected.gene_id))
+    records = [receipt(p) for p in [target_source, ROOT / 'locked_target_genes.csv',
+               E2 / 'metadata/var.parquet', SOURCE_CT, cfgp, manifest_path, position_path, selected_path]]
+    si_path = M7 / 'e2_full_index/metadata/sample_index.parquet'
+    si = pd.read_parquet(si_path)
+    for k in KEY: si[k] = si[k].astype(str)
+    assert not si.duplicated(KEY).any()
+    records.append(receipt(si_path))
+    ct = pd.read_csv(SOURCE_CT)
+    expected = ct[ct.celltype_l1 == 'ILC'].groupby('dataset_prefix').n_cells.sum().to_dict()
+    assert sum(expected.values()) == 19329
+    files = [p for p in sorted((E2 / 'metadata/obs_sc.parquet').glob('*.parquet'))
+             if any(p.stem.startswith(k + '_') for k in expected)
+             and not p.name.endswith('__strict_overlay.parquet')]
+    selected_rows, checks, all_ilc = [], [], defaultdict(int)
+    for fi, ep in enumerate(files):
+        bp = BLOCK / 'metadata/obs_sc.parquet' / ep.name
+        eq, bq = pq.ParquetFile(ep), pq.ParquetFile(bp)
+        assert eq.metadata.num_rows == bq.metadata.num_rows
+        bcols = ['source_row_index', 'row_index', 'celltype_l1']
+        extra = [x for x in ['source_celltype_l1', 'label_original', 'source_file'] if x in bq.schema.names]
+        bi = iter(bq.iter_batches(batch_size=32768, columns=bcols + extra, use_threads=False))
+        offset = n_ilc = n_train = 0
+        for eb in eq.iter_batches(batch_size=32768, columns=KEY + ['source_row_index', 'row_index'], use_threads=False):
+            e, b = eb.to_pandas(), next(bi).to_pandas()
+            assert len(e) == len(b) and np.array_equal(e.source_row_index, b.source_row_index)
+            assert np.array_equal(e.row_index, np.arange(offset, offset + len(e)))
+            keep = b.celltype_l1.astype(str).eq('ILC').to_numpy()
+            if keep.any():
+                v = e.loc[keep].copy()
+                for k in KEY: v[k] = v[k].astype(str)
+                v['shard_row'] = v.row_index
+                v['block_pre_qc_row_index'] = b.loc[keep, 'row_index'].to_numpy()
+                v['source_label'] = b.loc[keep, 'celltype_l1'].astype(str).to_numpy()
+                for c in extra: v[c] = b.loc[keep, c].astype(str).to_numpy()
+                v = v.merge(si[KEY + ['split', 'sample_key']], on=KEY, how='left', validate='many_to_one')
+                assert v.split.notna().all()
+                prefix = next(k for k in expected if ep.stem.startswith(k + '_'))
+                all_ilc[prefix] += len(v)
+                n_ilc += len(v)
+                v = v[v.split == 'train'].copy()
+                v['shard_id'] = ep.stem
+                n_train += len(v)
+                if len(v): selected_rows.append(v)
+            offset += len(e)
+        assert next(bi, None) is None and offset == eq.metadata.num_rows
+        checks.append({'shard_id': ep.stem, 'metadata_rows': offset, 'source_ilc': n_ilc,
+                       'train_ilc': n_train, 'source_identity_and_physical_order_match': True})
+        records.extend([receipt(ep), receipt(bp)])
+        if fi % 20 == 0 or fi + 1 == len(files): print(f'metadata {fi+1}/{len(files)}', flush=True)
+    assert dict(all_ilc) == expected, (dict(all_ilc), expected)
+    v = pd.concat(selected_rows, ignore_index=True)
+    assert len(v) == 16439 and not v.duplicated(['shard_id', 'shard_row']).any()
+    v.to_parquet(ROOT / 'selected_training_ilc.parquet', index=False)
+    rows_by_sid = {x['shard_id']: x['metadata_rows'] for x in checks}
+    plans = []
+    for sid, d in v.groupby('shard_id', sort=True):
+        zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr')
+        g = zarr.open_group(str(zp), mode='r')
+        for p in sorted(zp.glob('**/zarr.json')): records.append(receipt(p))
+        assert g.attrs['shape'] == [rows_by_sid[sid], 20097] and g.attrs['shard_id'] == sid
+        mask = np.asarray(g['measurement_mask'][:])[cols].astype(bool)
+        ptr = np.asarray(g['indptr'][:], dtype=np.int64)
+        for a in ['indptr', 'measurement_mask']:
+            for p in sorted((zp / a / 'c').glob('*')):
+                if p.is_file(): records.append(receipt(p))
+        r = d.shard_row.to_numpy()
+        size = g['indices'].chunks[0]
+        assert size == g['data'].chunks[0] == 1000000
+        chunks = sorted(set(c for rr in r for c in range(int(ptr[rr]) // size, (int(ptr[rr+1]) - 1) // size + 1)))
+        paths = [zp / a / 'c' / str(c) for c in chunks for a in ['indices', 'data']]
+        plans.append({'shard_id': sid, 'cells': len(d), 'chunks': chunks,
+                      'compressed_bytes': sum(p.stat().st_size for p in paths if p.exists()),
+                      'decoded_bytes_upper_bound': len(chunks) * size * 12,
+                      'selected_nnz': int((ptr[r+1]-ptr[r]).sum()), 'measured': mask.tolist(),
+                      'source_count_matrix': g.attrs['source_count_matrix'], 'source_path': g.attrs['source_path']})
+    plan = {'status': 'prepared', 'source_ilc': sum(all_ilc.values()), 'train_ilc': len(v),
+            'source_ilc_by_dataset_prefix': all_ilc, 'metadata_shards_scanned': len(files),
+            'metadata_rows_scanned': sum(x['metadata_rows'] for x in checks),
+            'selected_shards': len(plans), 'compressed_bytes': sum(x['compressed_bytes'] for x in plans),
+            'decoded_bytes_upper_bound': sum(x['decoded_bytes_upper_bound'] for x in plans),
+            'plans': plans, 'metadata_checks': checks, 'input_sources': records,
+            'elapsed_seconds': time.time()-start}
+    dump('extraction_plan.json', plan)
+    print(json.dumps({k: plan[k] for k in ['source_ilc', 'train_ilc', 'selected_shards', 'compressed_bytes', 'decoded_bytes_upper_bound', 'elapsed_seconds']}), flush=True)
+
+
+def extract() -> None:
+    start = time.time()
+    plan = json.loads((ROOT / 'extraction_plan.json').read_text())
+    # Coordinator reviewed the 12.40 GiB cost gate and authorized this exact local subset.
+    assert plan['compressed_bytes'] <= 13 * 2**30, 'Cost exceeds coordinator-reviewed 12.40 GiB subset'
+    t = targets(); cols = t.raw_panel_position.to_numpy(dtype=int)
+    v = pd.read_parquet(ROOT / 'selected_training_ilc.parquet')
+    out = np.zeros((len(v), len(cols)), dtype=np.int64)
+    measured = np.zeros_like(out, dtype=bool)
+    totals = np.zeros(len(v), dtype=np.int64)
+    chunks_read, shard_checks = [], []
+    dataset_timings = []; ds_started = time.time(); ds_bytes = 0; current_ds = None
+    for pi, pp in enumerate(plan['plans']):
+        sid = pp['shard_id']; d = v[v.shard_id == sid].sort_values('shard_row')
+        ds = str(d.dataset_id.iloc[0])
+        if current_ds is None: current_ds = ds
+        elif ds != current_ds:
+            timing = {'dataset_id': current_ds, 'seconds': time.time()-ds_started, 'compressed_bytes': ds_bytes,
+                      'process_peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss}
+            dataset_timings.append(timing); print(json.dumps({'dataset_done': timing}), flush=True)
+            current_ds = ds; ds_started = time.time(); ds_bytes = 0
+        zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr')
+        g = zarr.open_group(str(zp), mode='r')
+        mask = np.asarray(g['measurement_mask'][:])[cols].astype(bool)
+        assert mask.tolist() == pp['measured']
+        measured[d.index] = mask
+        ptr = np.asarray(g['indptr'][:], dtype=np.int64)
+        segs = defaultdict(list); size = 1000000
+        for oi, r in zip(d.index, d.shard_row):
+            a, b = int(ptr[r]), int(ptr[r+1])
+            while a < b:
+                c = a // size; end = min(b, (c+1)*size)
+                segs[c].append((int(oi), a-c*size, end-c*size)); a = end
+        assert sorted(segs) == pp['chunks']
+        nnz = 0
+        for c, pieces in sorted(segs.items()):
+            a, b = c*size, min((c+1)*size, g['indices'].shape[0])
+            ix = np.asarray(g['indices'][a:b], dtype=np.int32)
+            val = np.asarray(g['data'][a:b])
+            assert (val >= 0).all() and np.array_equal(val, val.astype(np.int64))
+            val = val.astype(np.int64)
+            for oi, lo, hi in pieces:
+                ids, values = ix[lo:hi], val[lo:hi]
+                totals[oi] += values.sum(); nnz += hi-lo
+                for j, col in enumerate(cols): out[oi, j] += values[ids == col].sum()
+            for arr in ['indices', 'data']:
+                p = zp / arr / 'c' / str(c)
+                if p.exists(): chunks_read.append(receipt(p))
+        assert nnz == pp['selected_nnz']
+        shard_checks.append({'shard_id': sid, 'cells': len(d), 'selected_nnz': nnz, 'chunks': len(segs)})
+        ds_bytes += pp['compressed_bytes']
+        print(f'expression {pi+1}/{len(plan["plans"])} {sid[:6]} cells={len(d)}', flush=True)
+    assert (totals > 0).all() and (out[~measured] == 0).all()
+    for j, gid in enumerate(t.gene_id):
+        v[gid] = out[:, j]
+        v[gid + '__measured'] = measured[:, j]
+    v['total_counts_20097'] = totals
+    v.to_parquet(ROOT / 'training_ilc_marker_counts.parquet', index=False)
+    dump('expression_chunk_receipts.json', chunks_read)
+    dataset_timings.append({'dataset_id': current_ds, 'seconds': time.time()-ds_started, 'compressed_bytes': ds_bytes,
+                            'process_peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss})
+    dump('extraction_result.json', {'status': 'extracted', 'cells': len(v), 'genes': len(t),
+         'elapsed_seconds': time.time()-start, 'shard_checks': shard_checks,
+         'process_peak_rss_kib': resource.getrusage(resource.RUSAGE_SELF).ru_maxrss, 'dataset_timings': dataset_timings,
+         'chunk_receipts': receipt(ROOT / 'expression_chunk_receipts.json')})
+    summarize(v, t)
+
+
+def summarize(v: pd.DataFrame, t: pd.DataFrame) -> None:
+    tables = {'overall': [], 'by_dataset': [], 'by_donor': []}
+    grouped = [('overall', {}, v)]
+    grouped += [('by_dataset', {'dataset_id': k}, d) for k, d in v.groupby('dataset_id', sort=True)]
+    grouped += [('by_donor', dict(zip(KEY[:2], k)), d) for k, d in v.groupby(KEY[:2], sort=True)]
+    for level, labels, d in grouped:
+        for r in t.itertuples(index=False):
+            eligible = d[r.gene_id + '__measured']
+            det = eligible & d[r.gene_id].gt(0)
+            e, p = d[eligible], d[det]
+            n = len(e)
+            tables[level].append({**labels, 'gene_id': r.gene_id, 'gene_symbol': r.gene_symbol,
+                'raw_panel_position': int(r.raw_panel_position), 'source_labelled_ilc_cells': len(d),
+                'measured_eligible_cells': n, 'unmeasured_cells': len(d)-n, 'detected_cells': len(p),
+                'detection_fraction_among_measured': len(p)/n if n else None,
+                'total_raw_counts': int(e[r.gene_id].sum()),
+                'mean_raw_counts_among_measured': float(e[r.gene_id].mean()) if n else None,
+                'measured_dataset_scoped_donors': len(e[KEY[:2]].drop_duplicates()),
+                'detected_dataset_scoped_donors': len(p[KEY[:2]].drop_duplicates()),
+                'measured_samples': e.sample_key.nunique(), 'detected_samples': p.sample_key.nunique(),
+                'in_final6k': bool(r.in_final6k), 'in_7k': bool(r.in_7k), 'in_8k': bool(r.in_8k),
+                'sentinel': bool(r.sentinel)})
+    for name, rows in tables.items(): pd.DataFrame(rows).to_csv(ROOT / f'marker_support_{name}.csv', index=False)
+    depths = []
+    for name, d in [('ALL', v)] + list(v.groupby('dataset_id', sort=True)):
+        q = d.total_counts_20097.quantile([0, .1, .25, .5, .75, .9, 1]).to_dict()
+        depths.append({'dataset_id': name, 'cells': len(d), 'source_donor_ids': d.donor_id.nunique(),
+                       'samples': d.sample_key.nunique(), **{f'canonical_count_q{k}': x for k, x in q.items()}})
+    pd.DataFrame(depths).to_csv(ROOT / 'native_depth_support.csv', index=False)
+    v.groupby(['dataset_id', 'source_label', 'label_original'], dropna=False).size().rename('cells').reset_index().to_csv(ROOT / 'source_label_support.csv', index=False)
+
+
+def verify() -> None:
+    t = targets(); cols = t.raw_panel_position.to_numpy(dtype=int); ids = t.gene_id.tolist()
+    var = pd.read_parquet(E2 / 'metadata/var.parquet')
+    v = pd.read_parquet(ROOT / 'training_ilc_marker_counts.parquet')
+    physical, direct, original = [], [], []
+    first_ds, boundary_ds = set(), set()
+    for sid, d in v.groupby('shard_id', sort=True):
+        zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr')
+        g = zarr.open_group(str(zp), mode='r')
+        ep = E2 / 'metadata/obs_sc.parquet' / (sid + '.parquet')
+        bp = BLOCK / 'metadata/obs_sc.parquet' / (sid + '.parquet')
+        e = pq.read_table(ep, columns=['row_index', 'source_row_index']).to_pandas()
+        b = pq.read_table(bp, columns=['row_index', 'source_row_index', 'celltype_l1']).to_pandas()
+        rows = d.shard_row.to_numpy()
+        assert np.array_equal(e.iloc[rows].row_index, rows)
+        assert np.array_equal(e.iloc[rows].source_row_index, d.source_row_index)
+        assert np.array_equal(b.iloc[rows].source_row_index, d.source_row_index)
+        assert b.iloc[rows].celltype_l1.astype(str).eq('ILC').all()
+        assert g.attrs['shape'] == [len(e), len(var)]
+        physical.append({'shard_id': sid, 'cells': len(d), 'physical_identity_pass': True,
+                         'block_pre_qc_row_different': int((b.iloc[rows].row_index.to_numpy() != rows).sum())})
+        ds = str(d.dataset_id.iloc[0]); chosen = set()
+        if ds not in first_ds: chosen.update([d.index[0], d.index[-1]])
+        ptr = np.asarray(g['indptr'][:])
+        boundary = ptr[rows] // 1000000 != (ptr[rows+1]-1) // 1000000
+        if ds not in boundary_ds and boundary.any():
+            chosen.add(d.index[np.flatnonzero(boundary)[0]]); boundary_ds.add(ds)
+        for ix in sorted(chosen):
+            r = v.loc[ix]; a, stop = map(int, ptr[int(r.shard_row):int(r.shard_row)+2])
+            ri, rv = np.asarray(g['indices'][a:stop]), np.asarray(g['data'][a:stop])
+            observed = np.array([rv[ri == c].sum() for c in cols])
+            assert np.array_equal(observed, r[ids].to_numpy(dtype=np.int64))
+            assert int(rv.sum()) == int(r.total_counts_20097)
+            rec = {'dataset_id': ds, 'shard_id': sid, 'shard_row': int(r.shard_row),
+                   'counts': observed.tolist(), 'row_sum': int(rv.sum()),
+                   'crosses_chunk_boundary': a // 1000000 != (stop-1) // 1000000}
+            direct.append(rec)
+            if ds not in first_ds:
+                hp = Path('/home/sj_server_1/gblee') / g.attrs['source_path']
+                with h5py.File(hp, 'r') as h:
+                    key = g.attrs['source_count_matrix']; vg = h['raw/var' if key.startswith('raw/') else 'var']
+                    rawids = [x.decode() if isinstance(x, bytes) else str(x) for x in vg[vg.attrs['_index']][:]]
+                    assert len(rawids) == len(set(rawids))
+                    rawpos = {gid: j for j, gid in enumerate(rawids)}; mx = h[key]
+                    assert mx.attrs['encoding-type'] == 'csr_matrix'
+                    aa, zz = map(int, mx['indptr'][int(r.source_row_index):int(r.source_row_index)+2])
+                    oi, ov = mx['indices'][aa:zz], mx['data'][aa:zz]
+                    raw_counts = np.array([ov[oi == rawpos[gid]].sum() if gid in rawpos else 0 for gid in ids])
+                    raw_mask = np.array([gid in rawpos for gid in ids])
+                    assert np.array_equal(raw_counts, observed)
+                    assert np.array_equal(raw_mask, r[[gid + '__measured' for gid in ids]].to_numpy(dtype=bool))
+                    canonical = np.array([rawpos[gid] for gid in var.gene_id if gid in rawpos])
+                    assert ov[np.isin(oi, canonical)].sum() == r.total_counts_20097
+                    selected_bytes_hash = hashlib.sha256(oi.tobytes() + ov.tobytes()).hexdigest()
+                original.append({'dataset_id': ds, 'path': str(hp), 'bytes': hp.stat().st_size,
+                    'mtime_ns': hp.stat().st_mtime_ns, 'matrix': key, 'source_row_index': int(r.source_row_index),
+                    'shard_id': sid, 'shard_row': int(r.shard_row), '48_counts_match': True,
+                    '48_measurement_mask_matches_original_gene_axis': True, 'canonical_count_sum_matches': True,
+                    'original_selected_row_indices_values_sha256': selected_bytes_hash,
+                    'original_gene_axis_sha256': hashlib.sha256('\n'.join(rawids).encode()).hexdigest()})
+                first_ds.add(ds)
+    assert len(original) == v.dataset_id.nunique() == 12
+    assert sum(x['cells'] for x in physical) == 16439
+    dump('verification.json', {'status': 'PASS', 'physical_rows': physical, 'direct_csr_rows': direct,
+         'original_h5ad_rows': original, 'raw_positions': cols.tolist(), 'gene_ids': ids,
+         'source_label_is_not_independently_verified_biological_identity': True})
+    print(json.dumps({'status': 'PASS', 'cells': len(v), 'shards': len(physical),
+         'direct_csr_rows': len(direct), 'original_h5ad_rows': len(original)}), flush=True)
+
+
+if __name__ == '__main__':
+    ap = argparse.ArgumentParser(); ap.add_argument('stage', choices=['prepare', 'extract', 'verify'])
+    globals()[ap.parse_args().stage]()
diff --git a/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_plan.json b/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_plan.json
new file mode 100644
index 0000000000000000000000000000000000000000..d924629fb20a4bf61d67e81b4503aec98a46a98c
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261015T141849Z-rare-cell-7k-8k-panels/diffusion/dataset/server_1/model8/plans/rare_gene_context_expansion_20261015/coordination/ilc_training_support/extraction_plan.json
@@ -0,0 +1,23832 @@
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+    "010_A": 631,
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+    "065_A": 5533,
+    "093_C": 405,
+    "200_A": 2047,
+    "300b_A": 11,
+    "301_A": 127,
+    "302_A": 92,
+    "304_A": 8356
+  },
+  "metadata_shards_scanned": 142,
+  "metadata_rows_scanned": 32944864,
+  "selected_shards": 125,
+  "compressed_bytes": 13310541599,
+  "decoded_bytes_upper_bound": 98304000000,
+  "plans": [
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