Contextualize isolated feedback motifs with archived whole-brain input accounting

2026-09-30 01:07:42 +08:00
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 - [预先声明的诊断方案](models/male-cns-lif/lif-semantics-audit-v1/PROTOCOL.md)
 - [新阶段校验清单](registry/phase4ac-lif-semantics/seal-verification.json)
 - [上一轮末梢作用范围诊断](docs/progress/phase4ab-terminal-scope.md)
+- [小回路与全脑背景的只读复核](docs/progress/feedback-context-review-20260930.md)
+
+## 后续只读复核的重要限制
+
+所选五节点只贡献三个 ALLN 晚期正到达权重的 7.57%–8.99%；达到各自80%正输入需要49–52个来源。
+因此孤立小回路可以自持，不意味着它是全脑唯一原因。回路外输入也可能受该回路反馈影响，不是已证明独立的外源驱动。
+对旧数据的派生分解没有新模拟或训练；正输入比例不能当因果责任比例。
+独立新模型分支尚待用户确认，不把持续目标自动当作改变冻结动力学的许可。

 ## 新增机制证据

@@ -0,0 +1,48 @@
+# 小反馈环在全脑中的位置：充分性不等于唯一原因
+
+这是对既有封存记录的只读复核，不是新的训练阶段或新模型。
+使用六个旧校准气味、seed 131101 的全部 frozen_reference 记录，没有运行新模拟，也没有使用 held-out 输入。
+
+## 新量化结果
+
+晚期时窗为 500–1,000 ms，下表逐气味统计后列范围。
+比例分母为该目标的全部正突触到达权重之和，不是净输入、g 轨迹、电压贡献或因果责任。
+
+| 目标 | 五节点小回路内部正输入比例 | 正输入来源神经元数 | 累计80%正输入需要的来源数 |
+|---|---:|---:|---:|
+| ALLN 1197 / body11289 | 8.96%–8.99% | 197 | 49 |
+| ALLN 1381 / body11478 | 7.86%–7.89% | 214 | 51 |
+| ALLN 1511 / body11620 | 7.57%–7.60% | 205 | 52 |
+| VA6 PN 133666 / body527577 | 10.18%–10.22% | 119 | 31 |
+| KC 82312 / body109321 | 36.16%–36.39% | 8 | 4 |
+
+因此，4AC 的小回路能在隔离条件下持续放电，但其成员在全脑里接收了大量回路外输入。
+不能据此把某两条边定为唯一病因，或者认为只修两枚细胞就能解决学习问题。
+“回路外”仅表示不属于所选五节点；这些来源仍可能受该小回路反馈影响，**并非已经证明独立于该回路**，也不是外部刺激。
+
+## 同时公开已有条件回放，避免只挑支持某个解释的结果
+
+| 目标 | 基线晚期 spike | 忽略ALLN到达 | 忽略ALPN到达 |
+|---|---:|---:|---:|
+| ALLN 1197 | 228 | 217–218 | 227 |
+| ALLN 1381 | 227–228 | 227–228 | 227 |
+| ALLN 1511 | 228 | 219–221 | 227–228 |
+| VA6 PN 133666 | 212–214 | 0 | 208–210 |
+| KC 82312 | 60–61 | 60–61 | 0 |
+
+这些结果来自已封存 Phase 4Z，不是本轮重新模拟；回放固定其他已记录输入和 APL，不能替代实时全脑干预。
+ALLN 原本已接近模型不应期限制，放电计数相同不等于膜状态或输入没有影响。
+PN→KC 中有些比例并不占绝大多数的输入仍能决定阈值跨越，不能按比例大小直接删边。
+
+4Z 当时没有孤立回路自持的证据；4AC 已补上数学模型下的孤立充分性。
+二者合起来仍没有证明该小回路在全脑中的必要性、唯一性或真实果蝇中的同样动力学。
+
+## 对下一步的实际影响
+
+独立新模型实验若获授权，应先核对原作者算子，再检验分布式局部反馈与输入特异性；不把两神经元回路上的改善直接外推全脑学习。
+仍不得靠提高抑制、改符号、调学习率/阈值或扩大mask来追求通过。
+在此之前，本轮不会启动替代动力学、conditioning、游戏或decoder。
+
+来源与逐试次完整表：`registry/feedback-context-review-20260930/`。
+9个输入文件均对照原有封存manifest核验，前后字节一致；13条小回路边与旧事件会计的源、目标、count逐条一致。
+全部输出有manifest和SHA-256。后续更新应新增材料，不重写已封存数据。
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+# Feedback motif contextualization — read-only review
+
+This is not a new learning stage, intervention or model. Source: six archived calibration odors, one seed 131101, frozen_reference; all cases retained.
+Late interval 500–1,000 ms. Fractions measure positive arrived weights, not causal importance, g attribution or net excitation after inhibition/APL.
+
+| Target idx / type | Five-node internal positive input | Positive sources | Sources needed for 80% | Base late spikes | omit ALLN | omit ALPN |
+|---|---:|---:|---:|---:|---:|---:|
+| 1197 / lLN1_bc | 8.96–8.99% | 197–197 | 49–49 | 228–228 | 217–218 | 227–227 |
+| 1381 / lLN1_bc | 7.86–7.89% | 214–214 | 51–51 | 227–228 | 227–228 | 227–227 |
+| 1511 / lLN1_bc | 7.57–7.60% | 205–205 | 52–52 | 228–228 | 219–221 | 227–228 |
+| 82312 / KC | 36.16–36.39% | 8–8 | 4–4 | 60–61 | 60–61 | 0–0 |
+| 133666 / VA6_adPN | 10.18–10.22% | 119–119 | 31–31 | 212–214 | 0–0 | 208–210 |
+
+## Consequence for the next experiment
+
+The two-cell loop can sustain activity in isolation, but the full-brain watched ALLNs receive most positive input from outside the five-cell motif. Do not target only this pair as the established unique cause.
+Existing conditional omissions freeze other recorded events: removing ALLN arrivals still leaves PN/other recorded drive. This establishes conditional sufficiency of residual input, NOT that an ALLN knockout in the live brain would fail.
+Likewise a large input fraction is not a causal fraction; small inputs can matter near threshold. The VA6->KC edge illustrates why fractions alone are not a deletion target ranking.
+Future separately authorized model tests should first validate operator semantics, then assess distributed local feedback and preservation of stimulus information, before learning. No gain/threshold/eta/mask tuning is licensed by this table.
+No new simulations, learning, weights or frozen-file changes. No heldout data used. Usable associative learning remains unachieved.
@@ -0,0 +1,8 @@
+0c3c6db65be1f886f43e6c1d626d11086a41d739c3b74e8789ea14dd99b8ac5c  REPORT.md
+980024748379a30d470672e2fce6b4a0b1ad636a4f54ff630c1f10f1bd0fa387  context-fractions.csv
+1f3e85da1527d3ae4720b6f0673ce1e90ad3739c992fbe25753a8c688e8ec01c  existing-conditional-replay.csv
+320a33e0a21653091c25c4eaf07b949accd2a7ce6d5918c440ad07e09b6ffed0  protocol.json
+c6415f02fc8832830ce6f8b260583fe2e16964540b2ba49c527a67de4e14f518  source-neuron-fractions.csv
+29384ffd4844d46019a8e65c314f22a09f86d4645806107f07ce8b5de7c93962  source-type-fractions.csv
+c53fefb51bad059f176f90ecf53fa2ea3c53e57f710a88c3f61f5004a5d6ad67  summary.csv
+d7e99c7af2a28424d064eb025b0ff05b93242c2f285263d966ca63a568c25ee8  verification.json
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+trial,period,target_neuron_idx,body_id,annotation_type,scope,target_in_scope,positive_arrival_sum,negative_arrival_sum,internal_positive_sum,internal_positive_fraction,outside_positive_fraction,active_positive_source_neurons,sources_for_80_percent,ALLN_positive_fraction,ALPN_positive_fraction
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+      "path": "summary.csv",
+      "bytes": 908,
+      "sha256": "c53fefb51bad059f176f90ecf53fa2ea3c53e57f710a88c3f61f5004a5d6ad67"
+    },
+    {
+      "path": "verification.json",
+      "bytes": 266,
+      "sha256": "d7e99c7af2a28424d064eb025b0ff05b93242c2f285263d966ca63a568c25ee8"
+    }
+  ]
+}
@@ -0,0 +1,64 @@
+{
+  "timestamp_utc": "2026-09-29T17:05:44.827567+00:00",
+  "source": "tools/review_feedback_context.py",
+  "source_sha256": "f2ca2dddc2b3ae562758ea5ea2264a325756d2360d8f3134c6328a3a3c65b286",
+  "analysis": "Post-hoc contextualization of preselected motifs using existing sealed accounting; no new simulations.",
+  "scope": "All six archived calibration odors, seed 131101, frozen_reference only; not heldout or learning evidence.",
+  "inputs": [
+    {
+      "path": "experiments/phase4z-pn-feedback-audit/drive/edge-accounting.parquet",
+      "bytes": 1118095,
+      "sha256": "b577a89457ddbfef44aebf44597b7341a1b135fbf8cb6d930b297432fc413227",
+      "sealed_manifest_sha256": "992fdd04143da9814dd7d6df4dfcfd087ceba1a65b7eeba3bf48a582c744117e"
+    },
+    {
+      "path": "experiments/phase4z-pn-feedback-audit/replay/results.parquet",
+      "bytes": 10290,
+      "sha256": "e98b2115556dd756a184c4ff842932e41b35f7bc2d77360950362c4a06b0d2c7",
+      "sealed_manifest_sha256": "992fdd04143da9814dd7d6df4dfcfd087ceba1a65b7eeba3bf48a582c744117e"
+    },
+    {
+      "path": "experiments/phase4z-pn-feedback-audit/drive/late-ISI-and-g.parquet",
+      "bytes": 12092,
+      "sha256": "b9d33056207b3756f33bb7133b3e687f51a9fcd476112dc8e756b8b2f243b442",
+      "sealed_manifest_sha256": "992fdd04143da9814dd7d6df4dfcfd087ceba1a65b7eeba3bf48a582c744117e"
+    },
+    {
+      "path": "experiments/phase4z-pn-feedback-audit/audit/incoming-source-metadata.parquet",
+      "bytes": 238792,
+      "sha256": "4336f9b4759fdcd6120568a908a5e0ec67a9f6cd6baff48e75f5d2167d476485",
+      "sealed_manifest_sha256": "992fdd04143da9814dd7d6df4dfcfd087ceba1a65b7eeba3bf48a582c744117e"
+    },
+    {
+      "path": "experiments/phase4z-pn-feedback-audit/audit/watched-metadata.parquet",
+      "bytes": 35578,
+      "sha256": "6acb942009013be7a0b328ada99df493b5826bd375a57f73418a09d361eb900c",
+      "sealed_manifest_sha256": "992fdd04143da9814dd7d6df4dfcfd087ceba1a65b7eeba3bf48a582c744117e"
+    },
+    {
+      "path": "experiments/phase4z-pn-feedback-audit/regression/trials.json",
+      "bytes": 40209,
+      "sha256": "c9148f77ee46b9c3096c141d6251794ba0a3b7c44c869eafe981ab24d79354bb",
+      "sealed_manifest_sha256": "992fdd04143da9814dd7d6df4dfcfd087ceba1a65b7eeba3bf48a582c744117e"
+    },
+    {
+      "path": "experiments/phase4ac-lif-semantics/audit/selected-real-edges.csv",
+      "bytes": 547,
+      "sha256": "9103e9d03fa21058a4f6cd21780c56fed8cdcfca26c34c2a43726e39aed8ec55",
+      "sealed_manifest_sha256": "4e7d1606e107fe72f5dad935952705260577edaba6f811d65b4105ce07abd72c"
+    },
+    {
+      "path": "experiments/phase4ac-lif-semantics/motifs/summary.csv",
+      "bytes": 1009,
+      "sha256": "4e762a26ecc7d3c3f862260495b6064a60832431b61dd41b944e8c501617a906",
+      "sealed_manifest_sha256": "4e7d1606e107fe72f5dad935952705260577edaba6f811d65b4105ce07abd72c"
+    },
+    {
+      "path": "experiments/phase4ac-lif-semantics/audit/final-status.json",
+      "bytes": 227,
+      "sha256": "c61d57cfb8808b0af5133e5d061e3ed6c435a6475f7a23b90f16c2de45d999fb",
+      "sealed_manifest_sha256": "4e7d1606e107fe72f5dad935952705260577edaba6f811d65b4105ce07abd72c"
+    }
+  ],
+  "metric": "sum of positive arrived synaptic weights, NOT g trajectory attribution or causal responsibility"
+}
@@ -0,0 +1,6 @@
+neuron_idx,body_id,type,internal_positive_fraction_min,internal_positive_fraction_max,sources_for_80_percent_min,sources_for_80_percent_max,positive_sources_min,positive_sources_max,isolated_five_late_spikes,whole_brain_late_rate_min,whole_brain_late_rate_max,base_late_min,base_late_max,omit_ALLN_late_min,omit_ALLN_late_max,omit_ALPN_late_min,omit_ALPN_late_max
+1197,11289,lLN1_bc,0.08960140811364586,0.08988441360051412,49,49,197,197,185,456.0,456.0,228,228,217,218,227,227
+1381,11478,lLN1_bc,0.07863660098309871,0.0789103216189623,51,51,214,214,185,454.0,456.0,227,228,227,228,227,227
+1511,11620,lLN1_bc,0.07569838634101086,0.07596087892954709,52,52,205,205,186,456.0,456.0,228,228,219,221,227,228
+82312,109321,KC,0.3616168737979565,0.3639311754071379,4,4,8,8,41,120.0,122.0,60,61,60,61,0,0
+133666,527577,VA6_adPN,0.10176837945956113,0.10217650374647959,31,31,119,119,150,424.0,428.0,212,214,0,0,208,210
@@ -0,0 +1,9 @@
+{
+  "pass_all": true,
+  "inputs_before_after_identical": true,
+  "inputs_checked": 9,
+  "edge_identity_matches": 13,
+  "frozen_source_CPU_replay_checks": "Previously sealed Phase4Z regressions, not rerun here",
+  "new_simulations": 0,
+  "learning_achieved": false
+}
@@ -0,0 +1,151 @@
+"""Read-only reanalysis of sealed event accounting; no simulation/model mutation."""
+from datetime import datetime, timezone
+import hashlib
+import json
+from pathlib import Path
+import sys
+import numpy as np
+import pandas as pd
+
+ROOT=Path('/data/digital-drosophila')
+Z=ROOT/'experiments/phase4z-pn-feedback-audit'
+AC=ROOT/'experiments/phase4ac-lif-semantics'
+OUT=ROOT/'registry/feedback-context-review-20260930'
+
+
+def sha(path):
+    h=hashlib.sha256()
+    with path.open('rb') as f:
+        for b in iter(lambda:f.read(8<<20),b''):h.update(b)
+    return h.hexdigest()
+
+
+def save(path,value):
+    with path.open('x',encoding='utf-8') as f:json.dump(value,f,indent=2,ensure_ascii=False,allow_nan=False);f.write('\n')
+
+
+def verify_inputs():
+    selection={Z:['drive/edge-accounting.parquet','replay/results.parquet','drive/late-ISI-and-g.parquet',
+                  'audit/incoming-source-metadata.parquet','audit/watched-metadata.parquet','regression/trials.json'],
+               AC:['audit/selected-real-edges.csv','motifs/summary.csv','audit/final-status.json']}
+    rows=[]
+    for folder,names in selection.items():
+        manifest=json.loads((folder/'manifest.json').read_text())
+        indexed={r['path']:r for r in manifest['files']}
+        for name in names:
+            p=folder/name;r=indexed[name]
+            assert p.is_file() and p.stat().st_size==r['bytes'] and sha(p)==r['sha256'],str(p)
+            rows.append(dict(path=str(p.relative_to(ROOT)),bytes=r['bytes'],sha256=r['sha256'],
+                sealed_manifest_sha256=sha(folder/'manifest.json')))
+    return rows
+
+
+def analyze():
+    before=verify_inputs()
+    if OUT.exists():raise RuntimeError('Refusing existing output')
+    OUT.mkdir()
+    save(OUT/'protocol.json',dict(timestamp_utc=datetime.now(timezone.utc).isoformat(),
+        source=str(Path(__file__).resolve().relative_to(ROOT)),source_sha256=sha(Path(__file__)),
+        analysis='Post-hoc contextualization of preselected motifs using existing sealed accounting; no new simulations.',
+        scope='All six archived calibration odors, seed 131101, frozen_reference only; not heldout or learning evidence.',
+        inputs=before,metric='sum of positive arrived synaptic weights, NOT g trajectory attribution or causal responsibility'))
+    d=pd.read_parquet(Z/'drive/edge-accounting.parquet')
+    required={'trial','period','edge_id','source_neuron_idx','target_neuron_idx','raw_synapse_count','effective_weight',
+              'edge_event_count','positive_arrival_sum','negative_arrival_sum','source_role'}
+    assert required<=set(d)
+    d=d[d.trial.str.endswith('-frozen_reference')].copy()
+    expected={f'{i}-131101-frozen_reference' for i in range(6)}
+    assert set(d.trial)==expected and not d.duplicated(['trial','period','edge_id']).any()
+    assert set(d.period)=={'input_on','post_input','late'}
+    assert np.allclose(d.positive_arrival_sum,d.edge_event_count*np.maximum(d.effective_weight,0),rtol=0,atol=0)
+    assert np.allclose(d.negative_arrival_sum,d.edge_event_count*np.minimum(d.effective_weight,0),rtol=0,atol=0)
+    edges=pd.read_csv(AC/'audit/selected-real-edges.csv')
+    for e in edges.itertuples():
+        records=d[d.edge_id==e.edge_id]
+        assert len(records)>0
+        assert records.source_neuron_idx.eq(e.source_neuron_idx).all()
+        assert records.target_neuron_idx.eq(e.target_neuron_idx).all()
+        assert records.raw_synapse_count.eq(e.raw_count).all()
+    metadata=pd.read_parquet(Z/'audit/incoming-source-metadata.parquet').set_index('neuron_idx')
+    watch=pd.read_parquet(Z/'audit/watched-metadata.parquet').set_index('neuron_idx')
+    assert metadata.index.is_unique and watch.index.is_unique
+    nodes=[1197,1381,1511,82312,133666]
+    scopes={'pair':{1381,1511},'trio':{1197,1381,1511},'five':set(nodes)}
+    summaries=[];types=[];sources=[]
+    for (trial,period,target),g in d[d.target_neuron_idx.isin(nodes)].groupby(['trial','period','target_neuron_idx']):
+        den=float(g.positive_arrival_sum.sum());neg=float(g.negative_arrival_sum.sum())
+        assert den>0
+        per=g.groupby('source_neuron_idx',sort=False).positive_arrival_sum.sum().sort_values(ascending=False,kind='stable')
+        pos=per[per>0];n80=int(np.searchsorted(pos.cumsum().to_numpy(),.8*den,side='left')+1)
+        alln=float(g.loc[g.source_role.eq('ALLN'),'positive_arrival_sum'].sum())
+        alpn=float(g.loc[g.source_role.eq('ALPN'),'positive_arrival_sum'].sum())
+        for scope,subset in scopes.items():
+            internal=g.source_neuron_idx.isin(subset)&g.target_neuron_idx.isin(subset)
+            num=float(g.loc[internal,'positive_arrival_sum'].sum())
+            summaries.append(dict(trial=trial,period=period,target_neuron_idx=int(target),body_id=int(watch.loc[target,'body_id']),
+                annotation_type=watch.loc[target,'annotation_type'],scope=scope,target_in_scope=bool(target in subset),
+                positive_arrival_sum=den,negative_arrival_sum=neg,internal_positive_sum=num,
+                internal_positive_fraction=num/den,outside_positive_fraction=(den-num)/den,
+                active_positive_source_neurons=int(len(pos)),sources_for_80_percent=n80,
+                ALLN_positive_fraction=alln/den,ALPN_positive_fraction=alpn/den))
+        for src,value in per.items():
+            m=metadata.loc[src]
+            sources.append(dict(trial=trial,period=period,target_neuron_idx=int(target),source_neuron_idx=int(src),
+                source_body_id=int(m.body_id),source_type=m.annotation_type,positive_arrival_sum=float(value),positive_fraction=float(value/den)))
+        gg=g.assign(source_type=g.source_neuron_idx.map(metadata.annotation_type).fillna('UNKNOWN_TYPE'))
+        for typ,z in gg.groupby('source_type'):
+            types.append(dict(trial=trial,period=period,target_neuron_idx=int(target),source_type=str(typ),
+                positive_arrival_sum=float(z.positive_arrival_sum.sum()),positive_fraction=float(z.positive_arrival_sum.sum()/den),
+                source_neurons=int(z.source_neuron_idx.nunique())))
+    summary=pd.DataFrame(summaries);summary.to_csv(OUT/'context-fractions.csv',index=False)
+    pd.DataFrame(types).to_csv(OUT/'source-type-fractions.csv',index=False)
+    pd.DataFrame(sources).to_csv(OUT/'source-neuron-fractions.csv',index=False)
+    rep=pd.read_parquet(Z/'replay/results.parquet')
+    rep=rep[rep.trial.isin(expected)&rep.neuron_idx.isin(nodes)&rep['mode'].isin(['base','omit_ALLN','omit_ALPN'])].copy()
+    assert len(rep)==6*5*3
+    rep.to_csv(OUT/'existing-conditional-replay.csv',index=False)
+    late=summary[(summary.period=='late')&(summary.scope=='five')]
+    iso=pd.read_csv(AC/'motifs/summary.csv')
+    isi=pd.read_parquet(Z/'drive/late-ISI-and-g.parquet')
+    isi=isi[isi.trial.isin(expected)&isi.neuron_idx.isin(nodes)]
+    comparison=[]
+    for node,g in late.groupby('target_neuron_idx'):
+        rr=rep[rep.neuron_idx==node];ii=isi[isi.neuron_idx==node]
+        row=dict(neuron_idx=int(node),body_id=int(watch.loc[node,'body_id']),type=str(watch.loc[node,'annotation_type']),
+            internal_positive_fraction_min=float(g.internal_positive_fraction.min()),
+            internal_positive_fraction_max=float(g.internal_positive_fraction.max()),
+            sources_for_80_percent_min=int(g.sources_for_80_percent.min()),sources_for_80_percent_max=int(g.sources_for_80_percent.max()),
+            positive_sources_min=int(g.active_positive_source_neurons.min()),positive_sources_max=int(g.active_positive_source_neurons.max()),
+            isolated_five_late_spikes=int(iso[(iso.motif=='five')&(iso.neuron_idx==node)].late_spikes.iloc[0]),
+            whole_brain_late_rate_min=float(ii.late_rate_hz.min()),whole_brain_late_rate_max=float(ii.late_rate_hz.max()))
+        for mode in ('base','omit_ALLN','omit_ALPN'):
+            vals=rr.loc[rr['mode']==mode,'late_spikes']
+            row[mode+'_late_min']=int(vals.min());row[mode+'_late_max']=int(vals.max())
+        comparison.append(row)
+    comp=pd.DataFrame(comparison);comp.to_csv(OUT/'summary.csv',index=False)
+    lines=['# Feedback motif contextualization — read-only review','',
+        'This is not a new learning stage, intervention or model. Source: six archived calibration odors, one seed 131101, frozen_reference; all cases retained.',
+        'Late interval 500–1,000 ms. Fractions measure positive arrived weights, not causal importance, g attribution or net excitation after inhibition/APL.', '',
+        '| Target idx / type | Five-node internal positive input | Positive sources | Sources needed for 80% | Base late spikes | omit ALLN | omit ALPN |',
+        '|---|---:|---:|---:|---:|---:|---:|']
+    for r in comp.itertuples():
+        lines.append(f'| {r.neuron_idx} / {r.type} | {100*r.internal_positive_fraction_min:.2f}–{100*r.internal_positive_fraction_max:.2f}% | {r.positive_sources_min}–{r.positive_sources_max} | {r.sources_for_80_percent_min}–{r.sources_for_80_percent_max} | {r.base_late_min}–{r.base_late_max} | {r.omit_ALLN_late_min}–{r.omit_ALLN_late_max} | {r.omit_ALPN_late_min}–{r.omit_ALPN_late_max} |')
+    lines+=['', '## Consequence for the next experiment', '',
+        'The two-cell loop can sustain activity in isolation, but the full-brain watched ALLNs receive most positive input from outside the five-cell motif. Do not target only this pair as the established unique cause.',
+        'Existing conditional omissions freeze other recorded events: removing ALLN arrivals still leaves PN/other recorded drive. This establishes conditional sufficiency of residual input, NOT that an ALLN knockout in the live brain would fail.',
+        'Likewise a large input fraction is not a causal fraction; small inputs can matter near threshold. The VA6->KC edge illustrates why fractions alone are not a deletion target ranking.',
+        'Future separately authorized model tests should first validate operator semantics, then assess distributed local feedback and preservation of stimulus information, before learning. No gain/threshold/eta/mask tuning is licensed by this table.',
+        'No new simulations, learning, weights or frozen-file changes. No heldout data used. Usable associative learning remains unachieved.','']
+    with (OUT/'REPORT.md').open('x',encoding='utf-8') as f:f.write('\n'.join(lines))
+    after=verify_inputs();assert before==after
+    save(OUT/'verification.json',dict(pass_all=True,inputs_before_after_identical=True,
+        inputs_checked=len(before),edge_identity_matches=13,frozen_source_CPU_replay_checks='Previously sealed Phase4Z regressions, not rerun here',
+        new_simulations=0,learning_achieved=False))
+    records=[dict(path=str(p.relative_to(OUT)),bytes=p.stat().st_size,sha256=sha(p)) for p in sorted(OUT.iterdir()) if p.is_file()]
+    save(OUT/'manifest.json',dict(files=records))
+    with (OUT/'checksums.sha256').open('x') as f:
+        for r in records:f.write(r['sha256']+'  '+r['path']+'\n')
+    print(comp.to_string(index=False));print('REVIEW SEALED; no new model or simulation')
+
+
+if __name__=='__main__':analyze()