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+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-001"
+ ],
+ "research_run_mode": "CONFIRMATORY",
+ "results": {
+ "protocol": "sustained_activity_stability_v1",
+ "run_count": 20,
+ "runner": "run_sustained_stability",
+ "tick_contract": {
+ "mode": "protocol_defined_internal_trials",
+ "requested_ticks": 100000,
+ "status": "NOT_APPLICABLE"
+ },
+ "ticks_requested": 100000
+ },
+ "runtime": {
+ "duration_seconds": 12.247346400050446
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "sustained_activity_stability_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 100000
+ },
+ "software": {
+ "brain5d_version": "unknown",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "9b3639479307712f38bbb7b584b0fe50175aaee821ac9fe0b92ee3614ae91cdb",
+ "timestamp": "2026-09-06T21:23:53.295815",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R1/report.md b/research/experiments/EXP-SNN-001-R1/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..f5cf06fa4fdf0dd5a1b8a7f5aeedee1a78fa273f
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R1/report.md
@@ -0,0 +1,29 @@
+# EXP-SNN-001-R1: Sustained activity stability v1 replication
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-001`
+Hypothese: `H-SNN-001-A`
+Aufgeloester Runner: `run_sustained_stability`
+
+## Protokoll und Einstellungen
+Protokoll: `sustained_activity_stability_v1`
+Angeforderte Mindest-Ticks: `100000`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "protocol_defined_internal_trials", "requested_ticks": 100000, "status": "NOT_APPLICABLE"}`
+
+## Bedingungen
+No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
+Runs: 20; Dauer: 12.247346 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Exact protocol replication after tick-contract registry correction. Frozen PREREG-SNN-001.
diff --git a/research/experiments/EXP-SNN-001-R1/summary.md b/research/experiments/EXP-SNN-001-R1/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..9bd09135cb66bf23f16aba44304a717167eea1d8
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R1/summary.md
@@ -0,0 +1,137 @@
+# EXP-SNN-001-R1: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-001`
+- Hypothese: `H-SNN-001-A`
+- Protokoll: `sustained_activity_stability_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `100000`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `CONFIRMATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `MISMATCH`
+- Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
+- Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
+- Beobachtete Conditions: `no_input_control, tonic_drive`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: Sustained activity stability v1 replication
+- Bedingungen: No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
+- Notizen: Exact protocol replication after tick-contract registry correction. Frozen PREREG-SNN-001.
+- Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `4cfcaf64e55b5c33f6fd818605a39da2c92871f9`
+- Git dirty: `True`
+- Runtime: `12.247346400050446` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+| tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | no_input_control | 100000 | — | — | — | — | — | — |
+| 42 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 43 | no_input_control | 100000 | — | — | — | — | — | — |
+| 43 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 44 | no_input_control | 100000 | — | — | — | — | — | — |
+| 44 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 45 | no_input_control | 100000 | — | — | — | — | — | — |
+| 45 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 46 | no_input_control | 100000 | — | — | — | — | — | — |
+| 46 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 47 | no_input_control | 100000 | — | — | — | — | — | — |
+| 47 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 48 | no_input_control | 100000 | — | — | — | — | — | — |
+| 48 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 49 | no_input_control | 100000 | — | — | — | — | — | — |
+| 49 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 50 | no_input_control | 100000 | — | — | — | — | — | — |
+| 50 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 51 | no_input_control | 100000 | — | — | — | — | — | — |
+| 51 | tonic_drive | 100000 | — | — | — | — | — | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
+- [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
+- [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
+- [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
+- [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
+- [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
+- [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
+- [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
+- [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
+- [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
+- [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
+- [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
+- [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
+- [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
+- [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
+- [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
+- [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
+- [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
+- [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
+- [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-001-R1/workflow.json b/research/experiments/EXP-SNN-001-R1/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..7b03b5c9f5fb7187b572da5341afb0db5b0dd4b8
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R1/workflow.json
@@ -0,0 +1,31 @@
+{
+ "experiment_id": "EXP-SNN-001-R1",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "title": "Sustained activity stability v1 replication",
+ "conditions": "No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.",
+ "ticks": 100000,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "sustained_activity_stability_v1",
+ "resolved_runner": "run_sustained_stability",
+ "tick_validation": {
+ "status": "NOT_APPLICABLE",
+ "mode": "protocol_defined_internal_trials",
+ "requested_ticks": 100000
+ },
+ "notes": "Exact protocol replication after tick-contract registry correction. Frozen PREREG-SNN-001.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only."
+}
diff --git a/research/experiments/EXP-SNN-001-R2/DATA/current_run.json b/research/experiments/EXP-SNN-001-R2/DATA/current_run.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab77fcbb4e1085b18be1c9af35b6e34dbcb5f811
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R2/DATA/current_run.json
@@ -0,0 +1,11 @@
+{
+ "last_archived_run": 19,
+ "raw_artifact": {
+ "format": "json.gz",
+ "path": "DATA/raw/run-0019-tonic_drive-seed-51.json.gz",
+ "sha256": "5d8f358b0dd07b4a4710d9cb4fad3740611d28864cf9f826b57213b2f34bfaa5",
+ "size_bytes": 24781,
+ "uncompressed_bytes": 2102883
+ },
+ "status": "idle"
+}
diff --git a/research/experiments/EXP-SNN-001-R2/DATA/runs_index.json b/research/experiments/EXP-SNN-001-R2/DATA/runs_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..05c2c92a9e06af2b64ea0407525d216757ca9dec
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R2/DATA/runs_index.json
@@ -0,0 +1,207 @@
+{
+ "run_count": 20,
+ "runs": [
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0000-no_input_control-seed-42.json.gz",
+ "run_index": 0,
+ "seed": 42,
+ "sha256": "1d380b138c3236b1f16fc5678c49bfe718e14dff866ba0d39a0bab38d8e1b833",
+ "size_bytes": 12759,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0001-tonic_drive-seed-42.json.gz",
+ "run_index": 1,
+ "seed": 42,
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+ "size_bytes": 24782,
+ "uncompressed_bytes": 2102883
+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0002-no_input_control-seed-43.json.gz",
+ "run_index": 2,
+ "seed": 43,
+ "sha256": "91710b7b6b48ba617f7f517c9492c328075dce9671aaf5f0539019637c3ffe71",
+ "size_bytes": 12763,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
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+ "run_index": 3,
+ "seed": 43,
+ "sha256": "a7eb31e524d2219b1eae76d97cf126f65d7165ec3a0f1ae1223c7290274564eb",
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+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0004-no_input_control-seed-44.json.gz",
+ "run_index": 4,
+ "seed": 44,
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+ },
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+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0005-tonic_drive-seed-44.json.gz",
+ "run_index": 5,
+ "seed": 44,
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+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0006-no_input_control-seed-45.json.gz",
+ "run_index": 6,
+ "seed": 45,
+ "sha256": "12f71a7c4e0ccd7161e91a2bc1cc9341f780a9e5e6150d628e9e2aafce08bfab",
+ "size_bytes": 12762,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0007-tonic_drive-seed-45.json.gz",
+ "run_index": 7,
+ "seed": 45,
+ "sha256": "88807880d816110d774d2749d8d9fca991275d76906e495c4971a419f5931082",
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+ },
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+ "condition": "no_input_control",
+ "format": "json.gz",
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+ "run_index": 8,
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+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
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+ "run_index": 9,
+ "seed": 46,
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+ "run_index": 11,
+ "seed": 47,
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+ "format": "json.gz",
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+ "run_index": 12,
+ "seed": 48,
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+ {
+ "condition": "tonic_drive",
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+ "run_index": 13,
+ "seed": 48,
+ "sha256": "5e3a3022a4e8cfaf1a076c7c4d640fe90ec16c1ce7aa6aa08a754b4fc9a59f4a",
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+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
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+ "run_index": 14,
+ "seed": 49,
+ "sha256": "2c5e1780590992618336d54734764bacbf92e1d428d513223606eb37c52c87b3",
+ "size_bytes": 12760,
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+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
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+ "config": {
+ "path": "F:\\Brain-5D\\configs\\learning_experiment.yaml",
+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
+ },
+ "created_at": "2026-09-06T19:24:27.349674+00:00",
+ "data_partitions": [],
+ "execution_contract": {
+ "hypothesis_id": "H-SNN-001-A",
+ "protocol": "sustained_activity_stability_v1",
+ "question_id": "RQ-SNN-001",
+ "resolved_runner": "run_sustained_stability",
+ "runtime_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "runtime_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "source_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "source_runtime_consistency": "MATCH",
+ "source_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "tick_validation": {
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "observed_max": 100000,
+ "observed_min": 100000,
+ "requested_ticks": 100000,
+ "run_count": 20,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 100000
+ },
+ "experiment_id": "EXP-SNN-001-R2",
+ "experiment_status": "completed",
+ "git": {
+ "branch": "main",
+ "commit": "4cfcaf64e55b5c33f6fd818605a39da2c92871f9",
+ "dirty": true
+ },
+ "hypotheses": [
+ "H-SNN-001-A"
+ ],
+ "network_mode": "OFFLINE",
+ "provenance_digests": {
+ "code": "824a8ef8529a5cc6a61601b5becbc8c932a7b33870a738c5d840ed38193b977a",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "data": "7c6b92100f5b5d479f5c541778394984affcf96541beae410e80e63b1580e956",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-001"
+ ],
+ "research_run_mode": "CONFIRMATORY",
+ "results": {
+ "protocol": "sustained_activity_stability_v1",
+ "run_count": 20,
+ "runner": "run_sustained_stability",
+ "tick_contract": {
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "observed_max": 100000,
+ "observed_min": 100000,
+ "requested_ticks": 100000,
+ "run_count": 20,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 100000
+ },
+ "runtime": {
+ "duration_seconds": 11.832557399990037
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "sustained_activity_stability_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 100000
+ },
+ "software": {
+ "brain5d_version": "unknown",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "d308c6620ea1e1c7b9c4c8bc6b37e37e48d3de12a96df53f695af6d8627a8e32",
+ "timestamp": "2026-09-06T21:24:27.349646",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R2/report.md b/research/experiments/EXP-SNN-001-R2/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..f9f848f80063016c10f8e52cfd6c09c650459e10
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R2/report.md
@@ -0,0 +1,29 @@
+# EXP-SNN-001-R2: Sustained activity stability v1 final replication
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-001`
+Hypothese: `H-SNN-001-A`
+Aufgeloester Runner: `run_sustained_stability`
+
+## Protokoll und Einstellungen
+Protokoll: `sustained_activity_stability_v1`
+Angeforderte Mindest-Ticks: `100000`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "minimum_per_run_with_control_and_treatment", "observed_max": 100000, "observed_min": 100000, "requested_ticks": 100000, "run_count": 20, "status": "SATISFIED"}`
+
+## Bedingungen
+No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
+Runs: 20; Dauer: 11.832557 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Final exact replication under frozen PREREG-SNN-001 with satisfied tick contract.
diff --git a/research/experiments/EXP-SNN-001-R2/summary.md b/research/experiments/EXP-SNN-001-R2/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..e213173cfcaac6edc223e313dd676085dbebb5e4
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R2/summary.md
@@ -0,0 +1,137 @@
+# EXP-SNN-001-R2: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-001`
+- Hypothese: `H-SNN-001-A`
+- Protokoll: `sustained_activity_stability_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `100000`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `CONFIRMATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `MISMATCH`
+- Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
+- Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
+- Beobachtete Conditions: `no_input_control, tonic_drive`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: Sustained activity stability v1 final replication
+- Bedingungen: No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
+- Notizen: Final exact replication under frozen PREREG-SNN-001 with satisfied tick contract.
+- Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `4cfcaf64e55b5c33f6fd818605a39da2c92871f9`
+- Git dirty: `True`
+- Runtime: `11.832557399990037` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+| tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | no_input_control | 100000 | — | — | — | — | — | — |
+| 42 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 43 | no_input_control | 100000 | — | — | — | — | — | — |
+| 43 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 44 | no_input_control | 100000 | — | — | — | — | — | — |
+| 44 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 45 | no_input_control | 100000 | — | — | — | — | — | — |
+| 45 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 46 | no_input_control | 100000 | — | — | — | — | — | — |
+| 46 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 47 | no_input_control | 100000 | — | — | — | — | — | — |
+| 47 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 48 | no_input_control | 100000 | — | — | — | — | — | — |
+| 48 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 49 | no_input_control | 100000 | — | — | — | — | — | — |
+| 49 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 50 | no_input_control | 100000 | — | — | — | — | — | — |
+| 50 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 51 | no_input_control | 100000 | — | — | — | — | — | — |
+| 51 | tonic_drive | 100000 | — | — | — | — | — | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
+- [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
+- [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
+- [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
+- [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
+- [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
+- [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
+- [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
+- [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
+- [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
+- [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
+- [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
+- [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
+- [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
+- [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
+- [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
+- [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
+- [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
+- [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
+- [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-001-R2/workflow.json b/research/experiments/EXP-SNN-001-R2/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..362599ca66492735e37bf0632ba9290f396607b5
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R2/workflow.json
@@ -0,0 +1,34 @@
+{
+ "experiment_id": "EXP-SNN-001-R2",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "title": "Sustained activity stability v1 final replication",
+ "conditions": "No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.",
+ "ticks": 100000,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "sustained_activity_stability_v1",
+ "resolved_runner": "run_sustained_stability",
+ "tick_validation": {
+ "status": "SATISFIED",
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "requested_ticks": 100000,
+ "observed_min": 100000,
+ "observed_max": 100000,
+ "run_count": 20
+ },
+ "notes": "Final exact replication under frozen PREREG-SNN-001 with satisfied tick contract.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only."
+}
diff --git a/research/experiments/EXP-SNN-001-R3/DATA/current_run.json b/research/experiments/EXP-SNN-001-R3/DATA/current_run.json
new file mode 100644
index 0000000000000000000000000000000000000000..bfb3e2aac5c8e9996128713bb91468d671156440
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R3/DATA/current_run.json
@@ -0,0 +1,11 @@
+{
+ "last_archived_run": 19,
+ "raw_artifact": {
+ "format": "json.gz",
+ "path": "DATA/raw/run-0019-tonic_drive-seed-51.json.gz",
+ "sha256": "550e71ef31aeb7ab155160148702df4ed4287e8dc001b1a472163a568cd5a3cb",
+ "size_bytes": 24781,
+ "uncompressed_bytes": 2102883
+ },
+ "status": "idle"
+}
diff --git a/research/experiments/EXP-SNN-001-R3/DATA/runs_index.json b/research/experiments/EXP-SNN-001-R3/DATA/runs_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ee0b3c3878a655b3ab868c2e49271e015f9dd40
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R3/DATA/runs_index.json
@@ -0,0 +1,207 @@
+{
+ "run_count": 20,
+ "runs": [
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0000-no_input_control-seed-42.json.gz",
+ "run_index": 0,
+ "seed": 42,
+ "sha256": "be06b0f286e7e8cf83ca141a35a952f39cc0eef2969dfb5d9778278d5c027b77",
+ "size_bytes": 12759,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0001-tonic_drive-seed-42.json.gz",
+ "run_index": 1,
+ "seed": 42,
+ "sha256": "7c125949d71ff6c984d20edc54a10afafbff689a2a41f223d188a5d98c49279e",
+ "size_bytes": 24782,
+ "uncompressed_bytes": 2102883
+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0002-no_input_control-seed-43.json.gz",
+ "run_index": 2,
+ "seed": 43,
+ "sha256": "04dc615ccd87ee4dc21de21682e3a3b0f7476df8eea89ad11b5d8a00b985aae0",
+ "size_bytes": 12763,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0003-tonic_drive-seed-43.json.gz",
+ "run_index": 3,
+ "seed": 43,
+ "sha256": "7188ea16321b13e3d2040390cb464b2db7deb829e9e31eb7710c8de3f8bea2ce",
+ "size_bytes": 24782,
+ "uncompressed_bytes": 2102883
+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0004-no_input_control-seed-44.json.gz",
+ "run_index": 4,
+ "seed": 44,
+ "sha256": "d44f346c8b291ec780b5917fec98649d019a74455825cb23e1188a1db4a473a1",
+ "size_bytes": 12763,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0005-tonic_drive-seed-44.json.gz",
+ "run_index": 5,
+ "seed": 44,
+ "sha256": "d77ebf38a17acf72f2ceca5f1c3637e673e81638fb845b8372dfa12451e1b996",
+ "size_bytes": 24782,
+ "uncompressed_bytes": 2102883
+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0006-no_input_control-seed-45.json.gz",
+ "run_index": 6,
+ "seed": 45,
+ "sha256": "983f2861edc4cc2e6dd7a4accac84e9a001294aec48281639ab6d59aadc71fae",
+ "size_bytes": 12762,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0007-tonic_drive-seed-45.json.gz",
+ "run_index": 7,
+ "seed": 45,
+ "sha256": "4833bc496dff013b24fd0d4dcf1a0c5700547a105586c00f1298bb9e9b67f6b8",
+ "size_bytes": 24782,
+ "uncompressed_bytes": 2102883
+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0008-no_input_control-seed-46.json.gz",
+ "run_index": 8,
+ "seed": 46,
+ "sha256": "545308e2858d98f8a13fe960146b78c83798e3b068ef350712c5abd4bac98474",
+ "size_bytes": 12764,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0009-tonic_drive-seed-46.json.gz",
+ "run_index": 9,
+ "seed": 46,
+ "sha256": "b39540faa8e902d7b4f04d7589daeb5682bc77578d40a23dcee79e3ba6845287",
+ "size_bytes": 24782,
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+ "formulas": {
+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "generated_by": "deterministic_statistics_engine",
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
+ "ticks_executed": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "tonic_drive",
+ "ratio": 1.0,
+ "reference_condition": "no_input_control"
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R3/manifest.json b/research/experiments/EXP-SNN-001-R3/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1fa8cd051cd31c8856bd39743773aef293a46e9b
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R3/manifest.json
@@ -0,0 +1,128 @@
+{
+ "ai_exposure": "none",
+ "ai_interactions": [],
+ "ai_protocol": {
+ "bump_reason": "initial registration",
+ "version": 1
+ },
+ "ai_reproducibility": "R0",
+ "artifacts": {
+ "ai_packet": "analysis/ai_packet.json",
+ "ai_packet_digest": "analysis/ai_packet_digest.json",
+ "current_run": "DATA/current_run.json",
+ "data": "DATA/runs.json",
+ "data_index": "DATA/runs_index.json",
+ "preregistration": "preregistrations/PREREG-SNN-001.json",
+ "raw_run_index": "DATA/runs_index.json",
+ "report": "report.md",
+ "statistics": "analysis/statistics.json",
+ "workflow": "workflow.json"
+ },
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "causal_taint": "PURE",
+ "config": {
+ "path": "C:\\Users\\T_hei\\AppData\\Local\\Temp\\brain5d-clean-snn-r3\\configs\\learning_experiment.yaml",
+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
+ },
+ "created_at": "2026-09-07T14:09:01.933323+00:00",
+ "data_partitions": [],
+ "epistemic_layers": {
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
+ "interpretation": "post-hoc AI or human interpretation; never execution input",
+ "ui_state": "operator/dashboard progress only; not a scientific result"
+ },
+ "execution_contract": {
+ "hypothesis_id": "H-SNN-001-A",
+ "protocol": "sustained_activity_stability_v1",
+ "question_id": "RQ-SNN-001",
+ "resolved_runner": "run_sustained_stability",
+ "runtime_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "runtime_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "source_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "source_runtime_consistency": "MATCH",
+ "source_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "tick_validation": {
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "observed_max": 100000,
+ "observed_min": 100000,
+ "requested_ticks": 100000,
+ "run_count": 20,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 100000
+ },
+ "experiment_id": "EXP-SNN-001-R3",
+ "experiment_status": "completed",
+ "git": {
+ "branch": "HEAD",
+ "commit": "23dbbd2607d8d6075b57474184d6e6cc3e217b85",
+ "dirty": true
+ },
+ "hypotheses": [
+ "H-SNN-001-A"
+ ],
+ "network_mode": "OFFLINE",
+ "provenance_digests": {
+ "code": "2e69185c8d5e78e204d592620b6946eedce5c3fe2d51f2cc09e171790f9dc92d",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "data": "242d72dde8c16637f8ebda383292bc4fbd03a90a780ca63931a1a17c940e7306",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-001"
+ ],
+ "research_run_mode": "CONFIRMATORY",
+ "results": {
+ "protocol": "sustained_activity_stability_v1",
+ "run_count": 20,
+ "runner": "run_sustained_stability",
+ "tick_contract": {
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "observed_max": 100000,
+ "observed_min": 100000,
+ "requested_ticks": 100000,
+ "run_count": 20,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 100000
+ },
+ "runtime": {
+ "duration_seconds": 10.707760399905965
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "sustained_activity_stability_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 100000
+ },
+ "software": {
+ "brain5d_version": "unknown",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "75c6ee0d7614a47f00540dc5d29b19aba3e4062b0b3cd4f6b8747f23f943ba87",
+ "timestamp": "2026-09-07T16:09:01.933291",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R3/report.md b/research/experiments/EXP-SNN-001-R3/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..dc4f26f2c4c3ca31b4dd4987bedb3527c5219b0f
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R3/report.md
@@ -0,0 +1,35 @@
+# EXP-SNN-001-R3: RQ-SNN-001 frozen clean-tree replication
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-001`
+Hypothese: `H-SNN-001-A`
+Aufgeloester Runner: `run_sustained_stability`
+
+## Protokoll und Einstellungen
+Protokoll: `sustained_activity_stability_v1`
+Angeforderte Mindest-Ticks: `100000`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "minimum_per_run_with_control_and_treatment", "observed_max": 100000, "observed_min": 100000, "requested_ticks": 100000, "run_count": 20, "status": "SATISFIED"}`
+
+## Bedingungen
+Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
+Runs: 20; Dauer: 10.707760 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Epistemische Ebenen
+UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.
+DATA: Rohdaten, Run-Index und deterministische Statistik.
+EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.
+Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Exact frozen protocol rerun from a clean Git worktree.
diff --git a/research/experiments/EXP-SNN-001-R3/summary.md b/research/experiments/EXP-SNN-001-R3/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..038d788932f7b624f9b9e4cf1b90220e9abc1890
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R3/summary.md
@@ -0,0 +1,137 @@
+# EXP-SNN-001-R3: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-001`
+- Hypothese: `H-SNN-001-A`
+- Protokoll: `sustained_activity_stability_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `100000`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `CONFIRMATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `MISMATCH`
+- Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
+- Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
+- Beobachtete Conditions: `no_input_control, tonic_drive`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: RQ-SNN-001 frozen clean-tree replication
+- Bedingungen: Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
+- Notizen: Exact frozen protocol rerun from a clean Git worktree.
+- Konfiguration: `C:\Users\T_hei\AppData\Local\Temp\brain5d-clean-snn-r3\configs\learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `23dbbd2607d8d6075b57474184d6e6cc3e217b85`
+- Git dirty: `True`
+- Runtime: `10.707760399905965` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+| tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | no_input_control | 100000 | — | — | — | — | — | — |
+| 42 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 43 | no_input_control | 100000 | — | — | — | — | — | — |
+| 43 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 44 | no_input_control | 100000 | — | — | — | — | — | — |
+| 44 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 45 | no_input_control | 100000 | — | — | — | — | — | — |
+| 45 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 46 | no_input_control | 100000 | — | — | — | — | — | — |
+| 46 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 47 | no_input_control | 100000 | — | — | — | — | — | — |
+| 47 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 48 | no_input_control | 100000 | — | — | — | — | — | — |
+| 48 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 49 | no_input_control | 100000 | — | — | — | — | — | — |
+| 49 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 50 | no_input_control | 100000 | — | — | — | — | — | — |
+| 50 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 51 | no_input_control | 100000 | — | — | — | — | — | — |
+| 51 | tonic_drive | 100000 | — | — | — | — | — | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
+- [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
+- [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
+- [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
+- [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
+- [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
+- [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
+- [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
+- [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
+- [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
+- [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
+- [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
+- [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
+- [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
+- [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
+- [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
+- [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
+- [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
+- [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
+- [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-001-R3/workflow.json b/research/experiments/EXP-SNN-001-R3/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..c113ae5ee1a704e16be9fe0305ea2c7f8f6deb63
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R3/workflow.json
@@ -0,0 +1,40 @@
+{
+ "experiment_id": "EXP-SNN-001-R3",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "title": "RQ-SNN-001 frozen clean-tree replication",
+ "conditions": "Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks",
+ "ticks": 100000,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "sustained_activity_stability_v1",
+ "resolved_runner": "run_sustained_stability",
+ "tick_validation": {
+ "status": "SATISFIED",
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "requested_ticks": 100000,
+ "observed_min": 100000,
+ "observed_max": 100000,
+ "run_count": 20
+ },
+ "notes": "Exact frozen protocol rerun from a clean Git worktree.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only.",
+ "epistemic_layers": {
+ "ui_state": "operator/dashboard progress only; not a scientific result",
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
+ "interpretation": "post-hoc AI or human interpretation; never execution input"
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R5/DATA/current_run.json b/research/experiments/EXP-SNN-001-R5/DATA/current_run.json
new file mode 100644
index 0000000000000000000000000000000000000000..4763984704bc1b268ddc8818b2dda9957c6351a2
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/DATA/current_run.json
@@ -0,0 +1,11 @@
+{
+ "last_archived_run": 19,
+ "raw_artifact": {
+ "format": "json.gz",
+ "path": "DATA/raw/run-0019-tonic_drive-seed-51.json.gz",
+ "sha256": "30cd6cc4aeffde7350ed232fca3c051b0e134e75c50ae609ded2ae30fcdd1c88",
+ "size_bytes": 24781,
+ "uncompressed_bytes": 2102883
+ },
+ "status": "idle"
+}
diff --git a/research/experiments/EXP-SNN-001-R5/DATA/runs_index.json b/research/experiments/EXP-SNN-001-R5/DATA/runs_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..dfce6953c5321ba00188e7eaf912eee64c75e670
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/DATA/runs_index.json
@@ -0,0 +1,207 @@
+{
+ "run_count": 20,
+ "runs": [
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0000-no_input_control-seed-42.json.gz",
+ "run_index": 0,
+ "seed": 42,
+ "sha256": "502ca6e3d1cb9c8c2aec727008e888de3eed8582c3db1ae0fc323fb118ddee7d",
+ "size_bytes": 12759,
+ "uncompressed_bytes": 2102704
+ },
+ {
+ "condition": "tonic_drive",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0001-tonic_drive-seed-42.json.gz",
+ "run_index": 1,
+ "seed": 42,
+ "sha256": "e52540ff8787887a75567378a2801e854d87f4774ac34268d101c6e0dd696bf3",
+ "size_bytes": 24782,
+ "uncompressed_bytes": 2102883
+ },
+ {
+ "condition": "no_input_control",
+ "format": "json.gz",
+ "path": "DATA/raw/run-0002-no_input_control-seed-43.json.gz",
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+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ }
+ },
+ "formulas": {
+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
+ "ticks_executed": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "tonic_drive",
+ "ratio": 1.0,
+ "reference_condition": "no_input_control"
+ }
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R5/analysis/ai_packet_digest.json b/research/experiments/EXP-SNN-001-R5/analysis/ai_packet_digest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1dbc4ae3d589a5cfe3270fd18956ccece329045c
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/analysis/ai_packet_digest.json
@@ -0,0 +1,7 @@
+{
+ "path": "analysis/ai_packet.json",
+ "raw_index_sha256": "312b9705c44c4e341a920a73a29b3b9509d75cfe4fec9800b3b43c4a93a9da7b",
+ "schema_version": "1.0",
+ "sha256": "e929f5556e3f4f6b01967f4b2612b163eca3157b97aa46db35e46f29a2473bb8",
+ "size_bytes": 87718
+}
diff --git a/research/experiments/EXP-SNN-001-R5/analysis/statistics.json b/research/experiments/EXP-SNN-001-R5/analysis/statistics.json
new file mode 100644
index 0000000000000000000000000000000000000000..8b6344be35efbb6dd13791a97228d81a02750e96
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/analysis/statistics.json
@@ -0,0 +1,72 @@
+{
+ "conditions": {
+ "no_input_control": {
+ "metrics": {
+ "ticks_executed": {
+ "max": 100000.0,
+ "mean": 100000.0,
+ "median": 100000.0,
+ "min": 100000.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ },
+ "tonic_drive": {
+ "metrics": {
+ "ticks_executed": {
+ "max": 100000.0,
+ "mean": 100000.0,
+ "median": 100000.0,
+ "min": 100000.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ }
+ },
+ "formulas": {
+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "generated_by": "deterministic_statistics_engine",
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
+ "ticks_executed": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "tonic_drive",
+ "ratio": 1.0,
+ "reference_condition": "no_input_control"
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R5/manifest.json b/research/experiments/EXP-SNN-001-R5/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..bdc217f4b856f6040494d0fa7d4acdea166113fd
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/manifest.json
@@ -0,0 +1,128 @@
+{
+ "ai_exposure": "none",
+ "ai_interactions": [],
+ "ai_protocol": {
+ "bump_reason": "initial registration",
+ "version": 1
+ },
+ "ai_reproducibility": "R0",
+ "artifacts": {
+ "ai_packet": "analysis/ai_packet.json",
+ "ai_packet_digest": "analysis/ai_packet_digest.json",
+ "current_run": "DATA/current_run.json",
+ "data": "DATA/runs.json",
+ "data_index": "DATA/runs_index.json",
+ "preregistration": "preregistrations/PREREG-SNN-001.json",
+ "raw_run_index": "DATA/runs_index.json",
+ "report": "report.md",
+ "statistics": "analysis/statistics.json",
+ "workflow": "workflow.json"
+ },
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "causal_taint": "PURE",
+ "config": {
+ "path": "configs/learning_experiment.yaml",
+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
+ },
+ "created_at": "2026-09-07T14:12:05.577478+00:00",
+ "data_partitions": [],
+ "epistemic_layers": {
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
+ "interpretation": "post-hoc AI or human interpretation; never execution input",
+ "ui_state": "operator/dashboard progress only; not a scientific result"
+ },
+ "execution_contract": {
+ "hypothesis_id": "H-SNN-001-A",
+ "protocol": "sustained_activity_stability_v1",
+ "question_id": "RQ-SNN-001",
+ "resolved_runner": "run_sustained_stability",
+ "runtime_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "runtime_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "source_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "source_runtime_consistency": "MATCH",
+ "source_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "tick_validation": {
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "observed_max": 100000,
+ "observed_min": 100000,
+ "requested_ticks": 100000,
+ "run_count": 20,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 100000
+ },
+ "experiment_id": "EXP-SNN-001-R5",
+ "experiment_status": "completed",
+ "git": {
+ "branch": "HEAD",
+ "commit": "4300e43de48ae08d59381830faf0a633e1ccfebf",
+ "dirty": false
+ },
+ "hypotheses": [
+ "H-SNN-001-A"
+ ],
+ "network_mode": "OFFLINE",
+ "provenance_digests": {
+ "code": "2e69185c8d5e78e204d592620b6946eedce5c3fe2d51f2cc09e171790f9dc92d",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "data": "6266b6404a7babd028619cf1a711e0f4ef1ff280a02c6dd6c79dbbb3ccedd6a5",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-001"
+ ],
+ "research_run_mode": "CONFIRMATORY",
+ "results": {
+ "protocol": "sustained_activity_stability_v1",
+ "run_count": 20,
+ "runner": "run_sustained_stability",
+ "tick_contract": {
+ "mode": "minimum_per_run_with_control_and_treatment",
+ "observed_max": 100000,
+ "observed_min": 100000,
+ "requested_ticks": 100000,
+ "run_count": 20,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 100000
+ },
+ "runtime": {
+ "duration_seconds": 10.860079000005499
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "sustained_activity_stability_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 100000
+ },
+ "software": {
+ "brain5d_version": "unknown",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "b32bb3e5bf8f26f517332bd8eb0353b3b19018e9da1db3b1d11462fdeb085a4d",
+ "timestamp": "2026-09-07T16:12:05.577444",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-001-R5/report.md b/research/experiments/EXP-SNN-001-R5/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..76e79a0f77afe5cc6daceb7656dde1253b770e0d
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/report.md
@@ -0,0 +1,35 @@
+# EXP-SNN-001-R5: RQ-SNN-001 frozen clean-tree replication
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-001`
+Hypothese: `H-SNN-001-A`
+Aufgeloester Runner: `run_sustained_stability`
+
+## Protokoll und Einstellungen
+Protokoll: `sustained_activity_stability_v1`
+Angeforderte Mindest-Ticks: `100000`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "minimum_per_run_with_control_and_treatment", "observed_max": 100000, "observed_min": 100000, "requested_ticks": 100000, "run_count": 20, "status": "SATISFIED"}`
+
+## Bedingungen
+Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
+Runs: 20; Dauer: 10.860079 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Epistemische Ebenen
+UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.
+DATA: Rohdaten, Run-Index und deterministische Statistik.
+EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.
+Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Exact frozen protocol rerun from a clean Git worktree.
diff --git a/research/experiments/EXP-SNN-001-R5/summary.md b/research/experiments/EXP-SNN-001-R5/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..69f68285607a0c12c5a8104fbea97e93914f2443
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/summary.md
@@ -0,0 +1,137 @@
+# EXP-SNN-001-R5: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-001`
+- Hypothese: `H-SNN-001-A`
+- Protokoll: `sustained_activity_stability_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `100000`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `CONFIRMATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `DIRECT_MATCH`
+- Evidence Readiness: `BLOCKED_HUMAN_REVIEW`
+- Begründung: RQ-SNN-001 verwendet das dedizierte Sustained-Activity-Protokoll mit Kontroll- und Tonic-Drive-Bedingung. Clean-Freeze, semantische Zuordnung und Tickvertrag sind erfüllt; Human Review und EVID-Gate bleiben offen.
+- Beobachtete Conditions: `no_input_control, tonic_drive`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: RQ-SNN-001 frozen clean-tree replication
+- Bedingungen: Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
+- Notizen: Exact frozen protocol rerun from a clean Git worktree.
+- Konfiguration: `configs/learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `4300e43de48ae08d59381830faf0a633e1ccfebf`
+- Git dirty: `False`
+- Runtime: `10.860079000005499` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+| tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | no_input_control | 100000 | — | — | — | — | — | — |
+| 42 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 43 | no_input_control | 100000 | — | — | — | — | — | — |
+| 43 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 44 | no_input_control | 100000 | — | — | — | — | — | — |
+| 44 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 45 | no_input_control | 100000 | — | — | — | — | — | — |
+| 45 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 46 | no_input_control | 100000 | — | — | — | — | — | — |
+| 46 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 47 | no_input_control | 100000 | — | — | — | — | — | — |
+| 47 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 48 | no_input_control | 100000 | — | — | — | — | — | — |
+| 48 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 49 | no_input_control | 100000 | — | — | — | — | — | — |
+| 49 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 50 | no_input_control | 100000 | — | — | — | — | — | — |
+| 50 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 51 | no_input_control | 100000 | — | — | — | — | — | — |
+| 51 | tonic_drive | 100000 | — | — | — | — | — | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
+- [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
+- [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
+- [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
+- [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
+- [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
+- [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
+- [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
+- [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
+- [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
+- [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
+- [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
+- [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
+- [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
+- [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
+- [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
+- [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
+- [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
+- [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
+- [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `DIRECT_MATCH`, Clean Freeze `dirty=false`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-001-R5/workflow.json b/research/experiments/EXP-SNN-001-R5/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..d1277c88fc0b2792feca6071cb0061788570f0e0
--- /dev/null
+++ b/research/experiments/EXP-SNN-001-R5/workflow.json
@@ -0,0 +1,40 @@
+{
+ "experiment_id": "EXP-SNN-001-R5",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "title": "RQ-SNN-001 frozen clean-tree replication",
+ "conditions": "Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks",
+ "ticks": 100000,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "sustained_activity_stability_v1",
+ "resolved_runner": "run_sustained_stability",
+ "tick_validation": {
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+ "item_count": 100,
+ "tail": [
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+ },
+ "window_spike_counts": {
+ "_projection": "bounded_sequence",
+ "head": [
+ 310,
+ 300,
+ 300,
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+ 300,
+ 300,
+ 300,
+ 300,
+ 300,
+ 300,
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+ ],
+ "item_count": 100,
+ "tail": [
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+ 300,
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+ 300,
+ 300
+ ]
+ },
+ "window_ticks": 1000
+ },
+ "runtime_error": null,
+ "seed": 51,
+ "state_digest_after": "b506e401a757560ee0bdfa1841fd71b097ac51998fe6615fd9a812272f969e1b",
+ "state_digest_before": "f6d0a3123b44b3b9c141e3af6d7066e61151bd30695c4123bcd72a7374414e06"
+ }
+ ]
+ },
+ "schema_version": "2.0",
+ "statistics": {
+ "conditions": {
+ "no_input_control": {
+ "metrics": {
+ "ticks_executed": {
+ "max": 100000.0,
+ "mean": 100000.0,
+ "median": 100000.0,
+ "min": 100000.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ },
+ "tonic_drive": {
+ "metrics": {
+ "ticks_executed": {
+ "max": 100000.0,
+ "mean": 100000.0,
+ "median": 100000.0,
+ "min": 100000.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ }
+ },
+ "formulas": {
+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
+ "ticks_executed": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "tonic_drive",
+ "ratio": 1.0,
+ "reference_condition": "no_input_control"
+ }
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-001/analysis/ai_packet_digest.json b/research/experiments/EXP-SNN-001/analysis/ai_packet_digest.json
new file mode 100644
index 0000000000000000000000000000000000000000..ceb8f25ea4807a5d8a0af74cb965656ae8099599
--- /dev/null
+++ b/research/experiments/EXP-SNN-001/analysis/ai_packet_digest.json
@@ -0,0 +1,7 @@
+{
+ "path": "analysis/ai_packet.json",
+ "raw_index_sha256": "ab6b14d1b44d410b49bf488ed0aff72465da2ffbdfe9e26be6873ffb769c2853",
+ "schema_version": "1.0",
+ "sha256": "aba7e108851e02b76d410327e18234450ef6d2449bfa24d77bfe3e9a414d3bfb",
+ "size_bytes": 87670
+}
diff --git a/research/experiments/EXP-SNN-001/analysis/statistics.json b/research/experiments/EXP-SNN-001/analysis/statistics.json
new file mode 100644
index 0000000000000000000000000000000000000000..8b6344be35efbb6dd13791a97228d81a02750e96
--- /dev/null
+++ b/research/experiments/EXP-SNN-001/analysis/statistics.json
@@ -0,0 +1,72 @@
+{
+ "conditions": {
+ "no_input_control": {
+ "metrics": {
+ "ticks_executed": {
+ "max": 100000.0,
+ "mean": 100000.0,
+ "median": 100000.0,
+ "min": 100000.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ },
+ "tonic_drive": {
+ "metrics": {
+ "ticks_executed": {
+ "max": 100000.0,
+ "mean": 100000.0,
+ "median": 100000.0,
+ "min": 100000.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ }
+ },
+ "formulas": {
+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "generated_by": "deterministic_statistics_engine",
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
+ "ticks_executed": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "tonic_drive",
+ "ratio": 1.0,
+ "reference_condition": "no_input_control"
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-001/manifest.json b/research/experiments/EXP-SNN-001/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..ecb53df3287d757c72c5f6b11dfcb43b6138418c
--- /dev/null
+++ b/research/experiments/EXP-SNN-001/manifest.json
@@ -0,0 +1,116 @@
+{
+ "ai_exposure": "none",
+ "ai_interactions": [],
+ "ai_protocol": {
+ "bump_reason": "initial registration",
+ "version": 1
+ },
+ "ai_reproducibility": "R0",
+ "artifacts": {
+ "ai_packet": "analysis/ai_packet.json",
+ "ai_packet_digest": "analysis/ai_packet_digest.json",
+ "current_run": "DATA/current_run.json",
+ "data": "DATA/runs.json",
+ "data_index": "DATA/runs_index.json",
+ "preregistration": "preregistrations/PREREG-SNN-001.json",
+ "raw_run_index": "DATA/runs_index.json",
+ "report": "report.md",
+ "statistics": "analysis/statistics.json",
+ "workflow": "workflow.json"
+ },
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "causal_taint": "PURE",
+ "config": {
+ "path": "F:\\Brain-5D\\configs\\learning_experiment.yaml",
+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
+ },
+ "created_at": "2026-09-06T19:07:05.930563+00:00",
+ "data_partitions": [],
+ "execution_contract": {
+ "hypothesis_id": "H-SNN-001-A",
+ "protocol": "sustained_activity_stability_v1",
+ "question_id": "RQ-SNN-001",
+ "resolved_runner": "run_sustained_stability",
+ "runtime_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "runtime_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "source_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
+ "source_runtime_consistency": "MATCH",
+ "source_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "tick_validation": {
+ "mode": "protocol_defined_internal_trials",
+ "requested_ticks": 100000,
+ "status": "NOT_APPLICABLE"
+ },
+ "ticks_requested": 100000
+ },
+ "experiment_id": "EXP-SNN-001",
+ "experiment_status": "completed",
+ "git": {
+ "branch": "main",
+ "commit": "68c72acda0cad461c5adb2b8b7bb5e70a3d15193",
+ "dirty": true
+ },
+ "hypotheses": [
+ "H-SNN-001-A"
+ ],
+ "network_mode": "OFFLINE",
+ "provenance_digests": {
+ "code": "824a8ef8529a5cc6a61601b5becbc8c932a7b33870a738c5d840ed38193b977a",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "data": "101afffba2e4c59fb9264d9d3c51c5f1a5e3384157dc2b7e04e632692e4c563b",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-001"
+ ],
+ "research_run_mode": "EXPLORATORY",
+ "results": {
+ "protocol": "sustained_activity_stability_v1",
+ "run_count": 20,
+ "runner": "run_sustained_stability",
+ "tick_contract": {
+ "mode": "protocol_defined_internal_trials",
+ "requested_ticks": 100000,
+ "status": "NOT_APPLICABLE"
+ },
+ "ticks_requested": 100000
+ },
+ "runtime": {
+ "duration_seconds": 12.384917100076564
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "sustained_activity_stability_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 100000
+ },
+ "software": {
+ "brain5d_version": "unknown",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "da8f47e06ed2206157973cf458b1ca1624cdc8b752142698a80e4cec7b374d21",
+ "timestamp": "2026-09-06T21:07:05.930530",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-001/report.md b/research/experiments/EXP-SNN-001/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..cf2866f4451d4acaeb8ef9050a411ab94ea96514
--- /dev/null
+++ b/research/experiments/EXP-SNN-001/report.md
@@ -0,0 +1,29 @@
+# EXP-SNN-001: Sustained activity stability v1
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-001`
+Hypothese: `H-SNN-001-A`
+Aufgeloester Runner: `run_sustained_stability`
+
+## Protokoll und Einstellungen
+Protokoll: `sustained_activity_stability_v1`
+Angeforderte Mindest-Ticks: `100000`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "protocol_defined_internal_trials", "requested_ticks": 100000, "status": "NOT_APPLICABLE"}`
+
+## Bedingungen
+No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
+Runs: 20; Dauer: 12.384917 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Frozen confirmatory protocol PREREG-SNN-001. Full per-tick spike trace retained; AI post-hoc only.
diff --git a/research/experiments/EXP-SNN-001/summary.md b/research/experiments/EXP-SNN-001/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..52a26a7aae5b0687c65db3044274de440a6da618
--- /dev/null
+++ b/research/experiments/EXP-SNN-001/summary.md
@@ -0,0 +1,137 @@
+# EXP-SNN-001: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-001`
+- Hypothese: `H-SNN-001-A`
+- Protokoll: `sustained_activity_stability_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `100000`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `EXPLORATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `MISMATCH`
+- Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
+- Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
+- Beobachtete Conditions: `no_input_control, tonic_drive`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: Sustained activity stability v1
+- Bedingungen: No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
+- Notizen: Frozen confirmatory protocol PREREG-SNN-001. Full per-tick spike trace retained; AI post-hoc only.
+- Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `68c72acda0cad461c5adb2b8b7bb5e70a3d15193`
+- Git dirty: `True`
+- Runtime: `12.384917100076564` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+| tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | no_input_control | 100000 | — | — | — | — | — | — |
+| 42 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 43 | no_input_control | 100000 | — | — | — | — | — | — |
+| 43 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 44 | no_input_control | 100000 | — | — | — | — | — | — |
+| 44 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 45 | no_input_control | 100000 | — | — | — | — | — | — |
+| 45 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 46 | no_input_control | 100000 | — | — | — | — | — | — |
+| 46 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 47 | no_input_control | 100000 | — | — | — | — | — | — |
+| 47 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 48 | no_input_control | 100000 | — | — | — | — | — | — |
+| 48 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 49 | no_input_control | 100000 | — | — | — | — | — | — |
+| 49 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 50 | no_input_control | 100000 | — | — | — | — | — | — |
+| 50 | tonic_drive | 100000 | — | — | — | — | — | — |
+| 51 | no_input_control | 100000 | — | — | — | — | — | — |
+| 51 | tonic_drive | 100000 | — | — | — | — | — | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
+- [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
+- [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
+- [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
+- [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
+- [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
+- [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
+- [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
+- [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
+- [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
+- [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
+- [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
+- [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
+- [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
+- [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
+- [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
+- [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
+- [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
+- [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
+- [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-001/workflow.json b/research/experiments/EXP-SNN-001/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..50b296c0928e662351c3a325e4b2ce8925882ec6
--- /dev/null
+++ b/research/experiments/EXP-SNN-001/workflow.json
@@ -0,0 +1,31 @@
+{
+ "experiment_id": "EXP-SNN-001",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "title": "Sustained activity stability v1",
+ "conditions": "No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.",
+ "ticks": 100000,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "sustained_activity_stability_v1",
+ "resolved_runner": "run_sustained_stability",
+ "tick_validation": {
+ "status": "NOT_APPLICABLE",
+ "mode": "protocol_defined_internal_trials",
+ "requested_ticks": 100000
+ },
+ "notes": "Frozen confirmatory protocol PREREG-SNN-001. Full per-tick spike trace retained; AI post-hoc only.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only."
+}
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--- /dev/null
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index 0000000000000000000000000000000000000000..1badd6cce5affb3e9fca036dfc4ae845eac0cca0
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+ "provenance_digests": {
+ "code": "5a11ea288e7e695c2dcd456642b533cbb3e6daf38b72e150134e60e69059531f",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "data": "7505d471a4847ef3c8bd6c4a83ad609f5efdf6dee25758c6ff2899c72387c576",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-002"
+ ],
+ "research_run_mode": "EXPLORATORY",
+ "results": {
+ "protocol": "science_suite_v1",
+ "run_count": 20,
+ "runner": "run_ping",
+ "tick_contract": {
+ "mode": "minimum_per_run",
+ "observed_max": 8,
+ "observed_min": 8,
+ "requested_ticks": 8,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 8
+ },
+ "runtime": {
+ "duration_seconds": 0.015287599992007017
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "science_suite_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 8
+ },
+ "software": {
+ "brain5d_version": "unknown",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "324734191899563653796a7c50e6e2cdb72b185e3e4b337a69de49f90ae0ffaa",
+ "timestamp": "2026-09-08T21:44:55.821932",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-002-R1/report.md b/research/experiments/EXP-SNN-002-R1/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..3b087fc9dbb46958a9c8d92dda7ac517746a5664
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R1/report.md
@@ -0,0 +1,35 @@
+# EXP-SNN-002-R1: RQ-SNN-002 constant-input spike reproducibility
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-002`
+Hypothese: `H-SNN-002-A`
+Aufgeloester Runner: `run_ping`
+
+## Protokoll und Einstellungen
+Protokoll: `science_suite_v1`
+Angeforderte Mindest-Ticks: `8`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "minimum_per_run", "observed_max": 8, "observed_min": 8, "requested_ticks": 8, "status": "SATISFIED"}`
+
+## Bedingungen
+Constant impulse input; recurrence_off versus recurrence_on; 10 independent seeds
+Runs: 20; Dauer: 0.015288 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Epistemische Ebenen
+UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.
+DATA: Rohdaten, Run-Index und deterministische Statistik.
+EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.
+Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Technical reproducibility run; AI post-hoc interpretation only; no evidence promotion.
diff --git a/research/experiments/EXP-SNN-002-R1/summary.md b/research/experiments/EXP-SNN-002-R1/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..04cf2037acda389d8fe848480c750b21816a2188
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R1/summary.md
@@ -0,0 +1,150 @@
+# EXP-SNN-002-R1: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-002`
+- Hypothese: `H-SNN-002-A`
+- Protokoll: `science_suite_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `8`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `8 .. 8` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `EXPLORATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `DIRECT_MATCH`
+- Evidence Readiness: `HUMAN_REVIEW_REQUIRED`
+- Begründung: RQ-SNN-002 erwartet einen kontrollierten Impulsantwort-Vergleich mit recurrence_off und recurrence_on.
+- Beobachtete Conditions: `recurrence_off, recurrence_on`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: RQ-SNN-002 constant-input spike reproducibility
+- Bedingungen: Constant impulse input; recurrence_off versus recurrence_on; 10 independent seeds
+- Notizen: Technical reproducibility run; AI post-hoc interpretation only; no evidence promotion.
+- Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `61cf1059e0b2dd622ab57b20e0967eaec31c1663`
+- Git dirty: `False`
+- Runtime: `0.015287599992007017` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| recurrence_off | 10 | 42,43,44,45,46,47,48,49,50,51 | 8 | 3 | 2 | 3 | 0 | 1 |
+| recurrence_on | 10 | 42,43,44,45,46,47,48,49,50,51 | 8 | 7 | 6 | 3 | 2 | 4 |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `activated_neurons`: $\Delta=0$; $R=1$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `delivered_synaptic_events`: $\Delta=4$; $R=3$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `first_response_latency`: $\Delta=0$; $R=1$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `last_response_latency`: $\Delta=3$; $R=2.5$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `propagation_depth`: $\Delta=3$; $R=4$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `recurrent_events`: $\Delta=2$; $R=—$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `synaptic_activity_ticks`: $\Delta=4$; $R=3$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `total_spikes`: $\Delta=4$; $R=2.33333$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+
+### 5.2 Inter-Spike-Intervalle
+
+- `recurrence_off`: n=20, mean=1, median=1, min=1, max=1 Ticks.
+- `recurrence_on`: n=60, mean=1.16667, median=1, min=1, max=2 Ticks.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 42 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 43 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 43 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 44 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 44 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 45 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 45 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 46 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 46 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 47 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 47 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 48 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 48 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 49 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 49 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 50 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 50 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 51 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 51 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-recurrence_off-seed-42.json.gz](DATA/raw/run-0000-recurrence_off-seed-42.json.gz)
+- [DATA/raw/run-0001-recurrence_on-seed-42.json.gz](DATA/raw/run-0001-recurrence_on-seed-42.json.gz)
+- [DATA/raw/run-0002-recurrence_off-seed-43.json.gz](DATA/raw/run-0002-recurrence_off-seed-43.json.gz)
+- [DATA/raw/run-0003-recurrence_on-seed-43.json.gz](DATA/raw/run-0003-recurrence_on-seed-43.json.gz)
+- [DATA/raw/run-0004-recurrence_off-seed-44.json.gz](DATA/raw/run-0004-recurrence_off-seed-44.json.gz)
+- [DATA/raw/run-0005-recurrence_on-seed-44.json.gz](DATA/raw/run-0005-recurrence_on-seed-44.json.gz)
+- [DATA/raw/run-0006-recurrence_off-seed-45.json.gz](DATA/raw/run-0006-recurrence_off-seed-45.json.gz)
+- [DATA/raw/run-0007-recurrence_on-seed-45.json.gz](DATA/raw/run-0007-recurrence_on-seed-45.json.gz)
+- [DATA/raw/run-0008-recurrence_off-seed-46.json.gz](DATA/raw/run-0008-recurrence_off-seed-46.json.gz)
+- [DATA/raw/run-0009-recurrence_on-seed-46.json.gz](DATA/raw/run-0009-recurrence_on-seed-46.json.gz)
+- [DATA/raw/run-0010-recurrence_off-seed-47.json.gz](DATA/raw/run-0010-recurrence_off-seed-47.json.gz)
+- [DATA/raw/run-0011-recurrence_on-seed-47.json.gz](DATA/raw/run-0011-recurrence_on-seed-47.json.gz)
+- [DATA/raw/run-0012-recurrence_off-seed-48.json.gz](DATA/raw/run-0012-recurrence_off-seed-48.json.gz)
+- [DATA/raw/run-0013-recurrence_on-seed-48.json.gz](DATA/raw/run-0013-recurrence_on-seed-48.json.gz)
+- [DATA/raw/run-0014-recurrence_off-seed-49.json.gz](DATA/raw/run-0014-recurrence_off-seed-49.json.gz)
+- [DATA/raw/run-0015-recurrence_on-seed-49.json.gz](DATA/raw/run-0015-recurrence_on-seed-49.json.gz)
+- [DATA/raw/run-0016-recurrence_off-seed-50.json.gz](DATA/raw/run-0016-recurrence_off-seed-50.json.gz)
+- [DATA/raw/run-0017-recurrence_on-seed-50.json.gz](DATA/raw/run-0017-recurrence_on-seed-50.json.gz)
+- [DATA/raw/run-0018-recurrence_off-seed-51.json.gz](DATA/raw/run-0018-recurrence_off-seed-51.json.gz)
+- [DATA/raw/run-0019-recurrence_on-seed-51.json.gz](DATA/raw/run-0019-recurrence_on-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `DIRECT_MATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-002-R1/workflow.json b/research/experiments/EXP-SNN-002-R1/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..00ab71ba6e64fbe2858760abcd6fe99dd2819e18
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R1/workflow.json
@@ -0,0 +1,39 @@
+{
+ "experiment_id": "EXP-SNN-002-R1",
+ "research_question": "RQ-SNN-002",
+ "hypothesis": "H-SNN-002-A",
+ "title": "RQ-SNN-002 constant-input spike reproducibility",
+ "conditions": "Constant impulse input; recurrence_off versus recurrence_on; 10 independent seeds",
+ "ticks": 8,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "science_suite_v1",
+ "resolved_runner": "run_ping",
+ "tick_validation": {
+ "status": "SATISFIED",
+ "mode": "minimum_per_run",
+ "requested_ticks": 8,
+ "observed_min": 8,
+ "observed_max": 8
+ },
+ "notes": "Technical reproducibility run; AI post-hoc interpretation only; no evidence promotion.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only.",
+ "epistemic_layers": {
+ "ui_state": "operator/dashboard progress only; not a scientific result",
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
+ "interpretation": "post-hoc AI or human interpretation; never execution input"
+ }
+}
diff --git a/research/experiments/EXP-SNN-002-R2/DATA/current_run.json b/research/experiments/EXP-SNN-002-R2/DATA/current_run.json
new file mode 100644
index 0000000000000000000000000000000000000000..bed9e33e4a99db60362c0f4994c42ce85a4b1e56
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/DATA/current_run.json
@@ -0,0 +1,11 @@
+{
+ "last_archived_run": 19,
+ "raw_artifact": {
+ "format": "json.gz",
+ "path": "DATA/raw/run-0019-recurrence_on-seed-51.json.gz",
+ "sha256": "cef0f365c4c7277fd4a6377ccf73c6c40187c3e9a0e0832035c8765d749fd071",
+ "size_bytes": 595,
+ "uncompressed_bytes": 1182
+ },
+ "status": "idle"
+}
diff --git a/research/experiments/EXP-SNN-002-R2/DATA/runs_index.json b/research/experiments/EXP-SNN-002-R2/DATA/runs_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..91870a7c41fe7550e07927055a0968e2e90772f5
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/DATA/runs_index.json
@@ -0,0 +1,207 @@
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+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "inter_spike_intervals": {
+ "recurrence_off": {
+ "max": 1.0,
+ "mean": 1.0,
+ "median": 1.0,
+ "min": 1.0,
+ "n": 20,
+ "std": 0.0
+ },
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+ "mean": 1.1666666666666667,
+ "median": 1.0,
+ "min": 1.0,
+ "n": 60,
+ "std": 0.37267799624996495
+ }
+ },
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
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+ "comparison_condition": "recurrence_on",
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+ "delivered_synaptic_events": {
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+ "comparison_condition": "recurrence_on",
+ "ratio": 3.0,
+ "reference_condition": "recurrence_off"
+ },
+ "first_response_latency": {
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+ "comparison_condition": "recurrence_on",
+ "ratio": 1.0,
+ "reference_condition": "recurrence_off"
+ },
+ "last_response_latency": {
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+ "comparison_condition": "recurrence_on",
+ "ratio": 2.5,
+ "reference_condition": "recurrence_off"
+ },
+ "propagation_depth": {
+ "absolute_difference": 3.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 4.0,
+ "reference_condition": "recurrence_off"
+ },
+ "recurrent_events": {
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+ "comparison_condition": "recurrence_on",
+ "ratio": null,
+ "reference_condition": "recurrence_off"
+ },
+ "synaptic_activity_ticks": {
+ "absolute_difference": 4.0,
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+ "ratio": 3.0,
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+ "comparison_condition": "recurrence_on",
+ "ratio": 2.3333333333333335,
+ "reference_condition": "recurrence_off"
+ }
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-002-R2/analysis/ai_packet_digest.json b/research/experiments/EXP-SNN-002-R2/analysis/ai_packet_digest.json
new file mode 100644
index 0000000000000000000000000000000000000000..d274995b5050d906890530865b3aab41f4cbed29
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/analysis/ai_packet_digest.json
@@ -0,0 +1,7 @@
+{
+ "path": "analysis/ai_packet.json",
+ "raw_index_sha256": "de43ff31dfa3a058c917096214c5d81ec2a4bf33c686170b658719e01ac6c26d",
+ "schema_version": "1.0",
+ "sha256": "4a76cba91246c5377c3e588f041878c63362d3f1c16b571d8a8ca86f157fedf2",
+ "size_bytes": 40525
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diff --git a/research/experiments/EXP-SNN-002-R2/analysis/statistics.json b/research/experiments/EXP-SNN-002-R2/analysis/statistics.json
new file mode 100644
index 0000000000000000000000000000000000000000..f55c0573a4468d2be46e12b00cb88b5d51857d81
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/analysis/statistics.json
@@ -0,0 +1,274 @@
+{
+ "conditions": {
+ "recurrence_off": {
+ "metrics": {
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+ }
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+ "seeds": [
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+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ },
+ "recurrence_on": {
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+ },
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+ "mean": 6.0,
+ "median": 6.0,
+ "min": 6.0,
+ "n": 10,
+ "std": 0.0
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+ "ticks_executed": {
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+ "mean": 8.0,
+ "median": 8.0,
+ "min": 8.0,
+ "n": 10,
+ "std": 0.0
+ },
+ "total_spikes": {
+ "max": 7.0,
+ "mean": 7.0,
+ "median": 7.0,
+ "min": 7.0,
+ "n": 10,
+ "std": 0.0
+ }
+ },
+ "run_count": 10,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ]
+ }
+ },
+ "formulas": {
+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
+ "mean": "mean(x) = (1/n) * sum_i x_i",
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
+ },
+ "generated_by": "deterministic_statistics_engine",
+ "inter_spike_intervals": {
+ "recurrence_off": {
+ "max": 1.0,
+ "mean": 1.0,
+ "median": 1.0,
+ "min": 1.0,
+ "n": 20,
+ "std": 0.0
+ },
+ "recurrence_on": {
+ "max": 2.0,
+ "mean": 1.1666666666666667,
+ "median": 1.0,
+ "min": 1.0,
+ "n": 60,
+ "std": 0.37267799624996495
+ }
+ },
+ "run_count": 20,
+ "schema_version": "2.0",
+ "two_condition_effects": {
+ "activated_neurons": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 1.0,
+ "reference_condition": "recurrence_off"
+ },
+ "delivered_synaptic_events": {
+ "absolute_difference": 4.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 3.0,
+ "reference_condition": "recurrence_off"
+ },
+ "first_response_latency": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 1.0,
+ "reference_condition": "recurrence_off"
+ },
+ "last_response_latency": {
+ "absolute_difference": 3.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 2.5,
+ "reference_condition": "recurrence_off"
+ },
+ "propagation_depth": {
+ "absolute_difference": 3.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 4.0,
+ "reference_condition": "recurrence_off"
+ },
+ "recurrent_events": {
+ "absolute_difference": 2.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": null,
+ "reference_condition": "recurrence_off"
+ },
+ "synaptic_activity_ticks": {
+ "absolute_difference": 4.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 3.0,
+ "reference_condition": "recurrence_off"
+ },
+ "ticks_executed": {
+ "absolute_difference": 0.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 1.0,
+ "reference_condition": "recurrence_off"
+ },
+ "total_spikes": {
+ "absolute_difference": 4.0,
+ "comparison_condition": "recurrence_on",
+ "ratio": 2.3333333333333335,
+ "reference_condition": "recurrence_off"
+ }
+ }
+}
diff --git a/research/experiments/EXP-SNN-002-R2/manifest.json b/research/experiments/EXP-SNN-002-R2/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1657614529231c7a637fa1b38596eb604c2e656c
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/manifest.json
@@ -0,0 +1,125 @@
+{
+ "ai_exposure": "none",
+ "ai_interactions": [],
+ "ai_protocol": {
+ "bump_reason": "initial registration",
+ "version": 1
+ },
+ "ai_reproducibility": "R0",
+ "artifacts": {
+ "ai_packet": "analysis/ai_packet.json",
+ "ai_packet_digest": "analysis/ai_packet_digest.json",
+ "current_run": "DATA/current_run.json",
+ "data": "DATA/runs.json",
+ "data_index": "DATA/runs_index.json",
+ "raw_run_index": "DATA/runs_index.json",
+ "report": "report.md",
+ "statistics": "analysis/statistics.json",
+ "workflow": "workflow.json"
+ },
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "causal_taint": "PURE",
+ "config": {
+ "path": "F:\\Brain-5D\\configs\\learning_experiment.yaml",
+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
+ },
+ "created_at": "2026-09-08T19:45:52.571631+00:00",
+ "data_partitions": [],
+ "epistemic_layers": {
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
+ "interpretation": "post-hoc AI or human interpretation; never execution input",
+ "ui_state": "operator/dashboard progress only; not a scientific result"
+ },
+ "execution_contract": {
+ "hypothesis_id": "H-SNN-002-A",
+ "protocol": "science_suite_v1",
+ "question_id": "RQ-SNN-002",
+ "resolved_runner": "run_ping",
+ "runtime_runner_bytecode_sha256": "27f0f839cc4696ea57ad0e8dd53469cf19031876bdd8f9cef86d840f2a04511e",
+ "runtime_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "source_runner_bytecode_sha256": "27f0f839cc4696ea57ad0e8dd53469cf19031876bdd8f9cef86d840f2a04511e",
+ "source_runtime_consistency": "MATCH",
+ "source_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
+ "tick_validation": {
+ "mode": "minimum_per_run",
+ "observed_max": 8,
+ "observed_min": 8,
+ "requested_ticks": 8,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 8
+ },
+ "experiment_id": "EXP-SNN-002-R2",
+ "experiment_status": "completed",
+ "git": {
+ "branch": "main",
+ "commit": "ac3474a155968a78a1f0def2c40af0225a15610d",
+ "dirty": false
+ },
+ "hypotheses": [
+ "H-SNN-002-A"
+ ],
+ "network_mode": "OFFLINE",
+ "provenance_digests": {
+ "code": "5a11ea288e7e695c2dcd456642b533cbb3e6daf38b72e150134e60e69059531f",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "data": "f1885ed0a93355b3fed0f9a8ff6a26eb9919965cf54af9cc8be52735bf550d19",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
+ },
+ "research_questions": [
+ "RQ-SNN-002"
+ ],
+ "research_run_mode": "EXPLORATORY",
+ "results": {
+ "protocol": "science_suite_v1",
+ "run_count": 20,
+ "runner": "run_ping",
+ "tick_contract": {
+ "mode": "minimum_per_run",
+ "observed_max": 8,
+ "observed_min": 8,
+ "requested_ticks": 8,
+ "status": "SATISFIED"
+ },
+ "ticks_requested": 8
+ },
+ "runtime": {
+ "duration_seconds": 0.01839570002630353
+ },
+ "runtime_errors": [],
+ "simulation": {
+ "protocol": "science_suite_v1",
+ "seed": 42,
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "ticks": 8
+ },
+ "software": {
+ "brain5d_version": "0.6.0a1",
+ "os": "Windows 11",
+ "python": "3.13.14"
+ },
+ "source_freeze_sha": "a207aff8c25654a5e439617ff3632ef2f6e419eae0ae2906a866fbc36e6b9e81",
+ "timestamp": "2026-09-08T21:45:52.571605",
+ "validity": {
+ "fatal_error_count": 0,
+ "reason": null,
+ "runtime_error_count": 0,
+ "valid": true
+ }
+}
diff --git a/research/experiments/EXP-SNN-002-R2/report.md b/research/experiments/EXP-SNN-002-R2/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..ad3e8ed424e173bcab241f1065baa549f5e9d115
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/report.md
@@ -0,0 +1,35 @@
+# EXP-SNN-002-R2: RQ-SNN-002 constant-input spike reproducibility replication
+
+## Forschungszuordnung
+Forschungsfrage: `RQ-SNN-002`
+Hypothese: `H-SNN-002-A`
+Aufgeloester Runner: `run_ping`
+
+## Protokoll und Einstellungen
+Protokoll: `science_suite_v1`
+Angeforderte Mindest-Ticks: `8`
+Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
+Tick-Vertrag: `{"mode": "minimum_per_run", "observed_max": 8, "observed_min": 8, "requested_ticks": 8, "status": "SATISFIED"}`
+
+## Bedingungen
+Constant impulse input; recurrence_off versus recurrence_on; 10 independent seeds
+Runs: 20; Dauer: 0.018396 s
+
+## Daten und Statistik
+Kompakte Run-Projektion: `DATA/runs.json`
+Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
+KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
+Deterministische deskriptive Statistik: `analysis/statistics.json`
+Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
+
+## Epistemische Ebenen
+UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.
+DATA: Rohdaten, Run-Index und deterministische Statistik.
+EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.
+Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.
+
+## Evidenzstatus
+DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
+
+## Hinweise
+Clean-tree technical replication; AI post-hoc interpretation only; no evidence promotion.
diff --git a/research/experiments/EXP-SNN-002-R2/summary.md b/research/experiments/EXP-SNN-002-R2/summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..6c6653f788d10a983e52ab01274c5678cf86bdc0
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/summary.md
@@ -0,0 +1,150 @@
+# EXP-SNN-002-R2: Wissenschaftliche Zusammenfassung
+
+Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
+
+## 1. Identifikation und Status
+
+- Experimentstatus: `completed`
+- Forschungsfrage: `RQ-SNN-002`
+- Hypothese: `H-SNN-002-A`
+- Protokoll: `science_suite_v1`
+- Durchlaeufe: `20`
+- Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
+- Angeforderte Ticks: `8`
+- Tickgebundene SNN-Läufe (PING/TEMP/5D): `8 .. 8` ausgeführte Ticks
+- Tick-Vertrag: `SATISFIED`
+- TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
+- Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
+- Laufmodus: `EXPLORATORY`
+- Netzwerkmodus: `OFFLINE`
+
+## 2. Semantische Konsistenz
+
+- RQ/Condition-Pruefung: `DIRECT_MATCH`
+- Evidence Readiness: `HUMAN_REVIEW_REQUIRED`
+- Begründung: RQ-SNN-002 erwartet einen kontrollierten Impulsantwort-Vergleich mit recurrence_off und recurrence_on.
+- Beobachtete Conditions: `recurrence_off, recurrence_on`
+
+`DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
+
+## 3. Ausfuehrungsparameter
+
+- Titel: RQ-SNN-002 constant-input spike reproducibility replication
+- Bedingungen: Constant impulse input; recurrence_off versus recurrence_on; 10 independent seeds
+- Notizen: Clean-tree technical replication; AI post-hoc interpretation only; no evidence promotion.
+- Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
+- Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
+- Git Commit: `ac3474a155968a78a1f0def2c40af0225a15610d`
+- Git dirty: `False`
+- Runtime: `0.01839570002630353` s
+
+## 4. Deterministische Formeln
+
+Die im Bericht verwendeten deskriptiven Groessen sind:
+
+- Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
+- Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
+- Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
+- Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
+- Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
+
+Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
+
+## 5. Ergebnisse nach Bedingung
+
+| Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
+| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| recurrence_off | 10 | 42,43,44,45,46,47,48,49,50,51 | 8 | 3 | 2 | 3 | 0 | 1 |
+| recurrence_on | 10 | 42,43,44,45,46,47,48,49,50,51 | 8 | 7 | 6 | 3 | 2 | 4 |
+
+### 5.1 Deskriptive Zwei-Bedingungs-Effekte
+
+- `activated_neurons`: $\Delta=0$; $R=1$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `delivered_synaptic_events`: $\Delta=4$; $R=3$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `first_response_latency`: $\Delta=0$; $R=1$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `last_response_latency`: $\Delta=3$; $R=2.5$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `propagation_depth`: $\Delta=3$; $R=4$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `recurrent_events`: $\Delta=2$; $R=—$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `synaptic_activity_ticks`: $\Delta=4$; $R=3$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `ticks_executed`: $\Delta=0$; $R=1$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+- `total_spikes`: $\Delta=4$; $R=2.33333$; Referenz `recurrence_off`, Vergleich `recurrence_on`.
+
+### 5.2 Inter-Spike-Intervalle
+
+- `recurrence_off`: n=20, mean=1, median=1, min=1, max=1 Ticks.
+- `recurrence_on`: n=60, mean=1.16667, median=1, min=1, max=2 Ticks.
+
+## 6. Einzelne Läufe
+
+| Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
+| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
+| 42 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 42 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 43 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 43 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 44 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 44 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 45 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 45 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 46 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 46 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 47 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 47 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 48 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 48 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 49 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 49 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 50 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 50 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+| 51 | recurrence_off | 8 | 3 | 2 | 3 | 0 | 1 | — |
+| 51 | recurrence_on | 8 | 7 | 6 | 3 | 2 | 4 | — |
+
+## 7. Reproduzierbarkeit und Provenienz
+
+Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
+
+Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
+
+## 8. AI Research Report
+
+- AIRR Status: `unavailable`
+- Wissenschaftliche Evidenz durch KI: `false`
+- Human Review: `PENDING`
+
+## 9. Artefakte
+
+- [analysis/ai_packet.json](analysis/ai_packet.json)
+- [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
+- [analysis/statistics.json](analysis/statistics.json)
+- [DATA/current_run.json](DATA/current_run.json)
+- [DATA/raw/run-0000-recurrence_off-seed-42.json.gz](DATA/raw/run-0000-recurrence_off-seed-42.json.gz)
+- [DATA/raw/run-0001-recurrence_on-seed-42.json.gz](DATA/raw/run-0001-recurrence_on-seed-42.json.gz)
+- [DATA/raw/run-0002-recurrence_off-seed-43.json.gz](DATA/raw/run-0002-recurrence_off-seed-43.json.gz)
+- [DATA/raw/run-0003-recurrence_on-seed-43.json.gz](DATA/raw/run-0003-recurrence_on-seed-43.json.gz)
+- [DATA/raw/run-0004-recurrence_off-seed-44.json.gz](DATA/raw/run-0004-recurrence_off-seed-44.json.gz)
+- [DATA/raw/run-0005-recurrence_on-seed-44.json.gz](DATA/raw/run-0005-recurrence_on-seed-44.json.gz)
+- [DATA/raw/run-0006-recurrence_off-seed-45.json.gz](DATA/raw/run-0006-recurrence_off-seed-45.json.gz)
+- [DATA/raw/run-0007-recurrence_on-seed-45.json.gz](DATA/raw/run-0007-recurrence_on-seed-45.json.gz)
+- [DATA/raw/run-0008-recurrence_off-seed-46.json.gz](DATA/raw/run-0008-recurrence_off-seed-46.json.gz)
+- [DATA/raw/run-0009-recurrence_on-seed-46.json.gz](DATA/raw/run-0009-recurrence_on-seed-46.json.gz)
+- [DATA/raw/run-0010-recurrence_off-seed-47.json.gz](DATA/raw/run-0010-recurrence_off-seed-47.json.gz)
+- [DATA/raw/run-0011-recurrence_on-seed-47.json.gz](DATA/raw/run-0011-recurrence_on-seed-47.json.gz)
+- [DATA/raw/run-0012-recurrence_off-seed-48.json.gz](DATA/raw/run-0012-recurrence_off-seed-48.json.gz)
+- [DATA/raw/run-0013-recurrence_on-seed-48.json.gz](DATA/raw/run-0013-recurrence_on-seed-48.json.gz)
+- [DATA/raw/run-0014-recurrence_off-seed-49.json.gz](DATA/raw/run-0014-recurrence_off-seed-49.json.gz)
+- [DATA/raw/run-0015-recurrence_on-seed-49.json.gz](DATA/raw/run-0015-recurrence_on-seed-49.json.gz)
+- [DATA/raw/run-0016-recurrence_off-seed-50.json.gz](DATA/raw/run-0016-recurrence_off-seed-50.json.gz)
+- [DATA/raw/run-0017-recurrence_on-seed-50.json.gz](DATA/raw/run-0017-recurrence_on-seed-50.json.gz)
+- [DATA/raw/run-0018-recurrence_off-seed-51.json.gz](DATA/raw/run-0018-recurrence_off-seed-51.json.gz)
+- [DATA/raw/run-0019-recurrence_on-seed-51.json.gz](DATA/raw/run-0019-recurrence_on-seed-51.json.gz)
+- [DATA/runs.json](DATA/runs.json)
+- [DATA/runs_index.json](DATA/runs_index.json)
+- [manifest.json](manifest.json)
+- [report.md](report.md)
+- [workflow.json](workflow.json)
+
+## 10. Wissenschaftliche Grenze und Schlussfolgerung
+
+Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
+
+**Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `DIRECT_MATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
diff --git a/research/experiments/EXP-SNN-002-R2/workflow.json b/research/experiments/EXP-SNN-002-R2/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..f58a5b8e3078813eb89c06cefe0b95c95eb7c0ee
--- /dev/null
+++ b/research/experiments/EXP-SNN-002-R2/workflow.json
@@ -0,0 +1,39 @@
+{
+ "experiment_id": "EXP-SNN-002-R2",
+ "research_question": "RQ-SNN-002",
+ "hypothesis": "H-SNN-002-A",
+ "title": "RQ-SNN-002 constant-input spike reproducibility replication",
+ "conditions": "Constant impulse input; recurrence_off versus recurrence_on; 10 independent seeds",
+ "ticks": 8,
+ "ticks_contract": "minimum requested observation window",
+ "seeds": [
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51
+ ],
+ "protocol": "science_suite_v1",
+ "resolved_runner": "run_ping",
+ "tick_validation": {
+ "status": "SATISFIED",
+ "mode": "minimum_per_run",
+ "requested_ticks": 8,
+ "observed_min": 8,
+ "observed_max": 8
+ },
+ "notes": "Clean-tree technical replication; AI post-hoc interpretation only; no evidence promotion.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only.",
+ "epistemic_layers": {
+ "ui_state": "operator/dashboard progress only; not a scientific result",
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
+ "interpretation": "post-hoc AI or human interpretation; never execution input"
+ }
+}
diff --git a/research/experiments/EXP-SNN-DRIVE-NEXT/results.json b/research/experiments/EXP-SNN-DRIVE-NEXT/results.json
new file mode 100644
index 0000000000000000000000000000000000000000..020651dc472f8770889c0b2a20c0ef1fea3371ad
--- /dev/null
+++ b/research/experiments/EXP-SNN-DRIVE-NEXT/results.json
@@ -0,0 +1,1262 @@
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+ "metrics": {
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diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/analysis/statistics.json b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/analysis/statistics.json
new file mode 100644
index 0000000000000000000000000000000000000000..3a687391930782b114869604f27d091fa89690cd
--- /dev/null
+++ b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/analysis/statistics.json
@@ -0,0 +1,65 @@
+{
+ "claim_boundary": "This experiment can support only the scoped implementation-level claim that the registered pair-based STDP rule produces signed and asymmetric weight changes across the fixed Pre/Post timing curve. It does not establish useful learning, task-performance improvement, network-level memory, biological equivalence, scaling, a 5D advantage, human-reviewed EVID or independent replication.",
+ "experiment_id": "EXP-SNN004-STDP-ASYM-R2-20260919",
+ "integrity": {
+ "checks": {
+ "clean_source_freeze": true,
+ "independent_runs_zero": true,
+ "initial_weight": true,
+ "parameter_match": true,
+ "protocol_id": true,
+ "replications": true
+ },
+ "pass": true
+ },
+ "mirror_checks": {
+ "1": {
+ "absolute_ltd_exceeds_ltp": true,
+ "negative_delta": -0.11414753094008567,
+ "positive_delta": 0.09512294245007147
+ },
+ "10": {
+ "absolute_ltd_exceeds_ltp": true,
+ "negative_delta": -0.07278367916551598,
+ "positive_delta": 0.0606530659712633
+ },
+ "20": {
+ "absolute_ltd_exceeds_ltp": true,
+ "negative_delta": -0.04414553294057311,
+ "positive_delta": 0.036787944117144256
+ },
+ "5": {
+ "absolute_ltd_exceeds_ltp": true,
+ "negative_delta": -0.09345609396856858,
+ "positive_delta": 0.0778800783071405
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+ "50": {
+ "absolute_ltd_exceeds_ltp": true,
+ "negative_delta": -0.009850199834867845,
+ "positive_delta": 0.008208499862389917
+ }
+ },
+ "primary_gates": {
+ "complete_timing_grid": true,
+ "deterministic_identity": true,
+ "mean_direction_and_asymmetry": true,
+ "mirror_asymmetry": true,
+ "negative_dt_depresses": true,
+ "positive_dt_potentiates": true,
+ "replication_count": true,
+ "zero_dt_unchanged": true
+ },
+ "schema_version": 1,
+ "status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "summary": {
+ "conditions": 11,
+ "deterministic_identity": true,
+ "hypothesis_supported": true,
+ "independent_runs": 0,
+ "mean_ltd": -0.06687660736992224,
+ "mean_ltp": 0.05573050614160189,
+ "repeated_evaluations": 110,
+ "total_replications": 110,
+ "zero_delta_weight": 0.0
+ }
+}
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/checksums.sha256 b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/checksums.sha256
new file mode 100644
index 0000000000000000000000000000000000000000..3425fc52246865d9b7ce1141fe9a855c70a571e9
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@@ -0,0 +1,5 @@
+3cd1c68ea65edfe6b9cc97b9797f48dfe7ad3797ef5c2def1e56abf2fa902671 analysis/statistics.json
+6e7208800ad9f607c966e6b974f49eb3f26042b3cc6cc09807909fb8697cda4e data/evaluation.json
+55a6ef353710e20f1c0e070e698675d9369c4f1b87a43c916e4ab98e5928d488 manifest.json
+d7e0b21298e6f3d9c0343638a3e95bdcdd6da9ff61adfdb0718fbc788fceee4f report.md
+a31b5cc649d8c7191d3212db6ef8b94e8f66a907feb48b831bd5ec542368211c review_request.json
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/data/evaluation.json b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/data/evaluation.json
new file mode 100644
index 0000000000000000000000000000000000000000..750b41e7b3f7815bc23e9b02483a91f8c64e3267
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@@ -0,0 +1,118 @@
+{
+ "conditions": {
+ "independent_runs_per_condition": 0,
+ "initial_weight": 0.5,
+ "parameters": {
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+ "max_weight": 1.0,
+ "min_weight": 0.0,
+ "tau_minus": 20.0,
+ "tau_plus": 20.0
+ },
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+ }
+ ],
+ "protocol": "stdp_pair_timing_v1",
+ "seed": 42,
+ "summary": {
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+ "deterministic_identity": true,
+ "hypothesis_supported": true,
+ "independent_runs": 0,
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+ "mean_ltp": 0.05573050614160189,
+ "repeated_evaluations": 110,
+ "total_replications": 110,
+ "zero_delta_weight": 0.0
+ }
+}
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/execution_audit.md b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/execution_audit.md
new file mode 100644
index 0000000000000000000000000000000000000000..e39a6260ab92fb76b310c54a6b0cdc1a02c168c5
--- /dev/null
+++ b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/execution_audit.md
@@ -0,0 +1,21 @@
+# Execution audit
+
+Experiment: `EXP-SNN004-STDP-ASYM-R2-20260919`
+
+- canonical DATA commit: `c0fabb0a21dfc5823d102ccaf8726c6b90fd4532`
+- source freeze: `821d2e0ecf823af3196bda782a69be8260a54943`
+- clean source tree before execution: true
+- scientific source digest check: PASS
+- STDP/gate regression suite: 71 passed
+- result: `SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL`
+- all preregistered primary gates: PASS
+- mean LTP: 0.05573050614160189
+- mean LTD: -0.06687660736992224
+- human review: PENDING
+- scientific evidence: false
+- independent replication: false
+
+This audit file does not alter DATA, checksums, the scientific source digest, or
+the executed source freeze. It exists to bind the workflow-persisted DATA commit
+to a normal user-authored PR head so the full repository checks can run without
+re-executing the scientific experiment.
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/manifest.json b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..84c797b31dc24c01c3a824e016224ec2b4ab6f08
--- /dev/null
+++ b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/manifest.json
@@ -0,0 +1,90 @@
+{
+ "artifacts_sha256": {
+ "canonical_stdp_pair_source": "e522aba94e2a3a6347a5f7fc0907ba141f7d487a8ea8aae7d32208c75db9531b",
+ "evaluation_data": "6e7208800ad9f607c966e6b974f49eb3f26042b3cc6cc09807909fb8697cda4e",
+ "preregistration": "8d532986676f54fd5e46d51d7602911ccb69ea1400cfc6eccad25b286a1afd52",
+ "runner_source": "531f8cc917bc4c1630b6690d4e9a6c24072af88de6994aface28cf2ebeff176f",
+ "statistics": "3cd1c68ea65edfe6b9cc97b9797f48dfe7ad3797ef5c2def1e56abf2fa902671"
+ },
+ "automatic_evidence_promotion": false,
+ "claim_boundary": "This experiment can support only the scoped implementation-level claim that the registered pair-based STDP rule produces signed and asymmetric weight changes across the fixed Pre/Post timing curve. It does not establish useful learning, task-performance improvement, network-level memory, biological equivalence, scaling, a 5D advantage, human-reviewed EVID or independent replication.",
+ "claim_id": "CLAIM-SNN-001",
+ "design": {
+ "delta_t_ms": [
+ -50,
+ -20,
+ -10,
+ -5,
+ -1,
+ 0,
+ 1,
+ 5,
+ 10,
+ 20,
+ 50
+ ],
+ "independent_runs_per_condition": 0,
+ "initial_weight": 0.5,
+ "repeated_evaluations_per_condition": 10,
+ "runner": "src.research.stdp_pair_timing.run_pair_timing_protocol",
+ "stdp_parameters": {
+ "a_minus": 0.12,
+ "a_plus": 0.1,
+ "tau_minus": 20.0,
+ "tau_plus": 20.0
+ },
+ "timing_semantics": {
+ "negative_delta_t": "post before pre; expected depression",
+ "positive_delta_t": "pre before post; expected potentiation",
+ "zero_delta_t": "simultaneous timing control; expected no weight change"
+ }
+ },
+ "experiment_id": "EXP-SNN004-STDP-ASYM-R2-20260919",
+ "experiment_status": "completed",
+ "gate_protection": {
+ "merge_policy": "Merge only after the experiment verifier and full pull-request CI are green.",
+ "source_digest_policy": "Do not modify src/, configs/, tests/, research/schemas/ or pyproject.toml."
+ },
+ "human_review_status": "PENDING",
+ "hypothesis": "H-SNN-004-A",
+ "independent_replication": false,
+ "integrity": {
+ "checks": {
+ "clean_source_freeze": true,
+ "independent_runs_zero": true,
+ "initial_weight": true,
+ "parameter_match": true,
+ "protocol_id": true,
+ "replications": true
+ },
+ "pass": true
+ },
+ "primary_gates": {
+ "complete_timing_grid": true,
+ "deterministic_identity": true,
+ "mean_direction_and_asymmetry": true,
+ "mirror_asymmetry": true,
+ "negative_dt_depresses": true,
+ "positive_dt_potentiates": true,
+ "replication_count": true,
+ "zero_dt_unchanged": true
+ },
+ "protocol": "snn004_stdp_pair_asymmetry_clean_r2",
+ "research_question": "RQ-SNN-004",
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "results": {
+ "conditions": 11,
+ "mean_ltd": -0.06687660736992224,
+ "mean_ltp": 0.05573050614160189,
+ "repeated_evaluations": 110,
+ "zero_delta_weight": 0.0
+ },
+ "schema_version": 1,
+ "scientific_evidence": false,
+ "source_freeze": {
+ "branch": "research/snn004-stdp-asym-r2-20260919",
+ "commit": "821d2e0ecf823af3196bda782a69be8260a54943",
+ "dirty_before_execution": false
+ },
+ "stage": 1
+}
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/report.md b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..26de925509c307fdf806d1993067903e950cd319
--- /dev/null
+++ b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/report.md
@@ -0,0 +1,33 @@
+# EXP-SNN004-STDP-ASYM-R2-20260919
+
+RQ-SNN-004 / H-SNN-004-A
+
+Status: **SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL**
+
+- source freeze: 821d2e0ecf823af3196bda782a69be8260a54943
+- clean tree: true
+- timing conditions: 11
+- repeated evaluations: 110
+- independent runs: 0
+- mean LTP: 0.05573050614160189
+- mean LTD: -0.06687660736992224
+- zero-delta change: 0.0
+
+Primary gates:
+
+{
+ "complete_timing_grid": true,
+ "deterministic_identity": true,
+ "mean_direction_and_asymmetry": true,
+ "mirror_asymmetry": true,
+ "negative_dt_depresses": true,
+ "positive_dt_potentiates": true,
+ "replication_count": true,
+ "zero_dt_unchanged": true
+}
+
+Claim boundary:
+
+This experiment can support only the scoped implementation-level claim that the registered pair-based STDP rule produces signed and asymmetric weight changes across the fixed Pre/Post timing curve. It does not establish useful learning, task-performance improvement, network-level memory, biological equivalence, scaling, a 5D advantage, human-reviewed EVID or independent replication.
+
+Human review remains PENDING. This DATA artifact does not create EVID automatically.
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json
new file mode 100644
index 0000000000000000000000000000000000000000..f0b14a6b174cc866d20e3e2d04b6bc9b44819521
--- /dev/null
+++ b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json
@@ -0,0 +1,20 @@
+{
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_evidence_promotion": false,
+ "claim_id": "CLAIM-SNN-001",
+ "experiment_id": "EXP-SNN004-STDP-ASYM-R2-20260919",
+ "human_review_status": "PENDING",
+ "hypothesis": "H-SNN-004-A",
+ "independent_replication": false,
+ "questions": [
+ "Does the registered timing curve directly test the scoped pair-based STDP asymmetry claim?",
+ "Are positive, negative and zero timing controls interpreted correctly?",
+ "Is the mirror-pair magnitude asymmetry appropriately tied to the frozen a_minus > a_plus parameterization?",
+ "Does the interpretation remain below any functional-learning, memory or biological-equivalence claim?"
+ ],
+ "research_question": "RQ-SNN-004",
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "reviewer_type_required": "human",
+ "schema_version": 1,
+ "scientific_evidence": false
+}
diff --git a/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json.review.json b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json.review.json
new file mode 100644
index 0000000000000000000000000000000000000000..0d0e6f2b442df804ae15f639652f2fe501bafedd
--- /dev/null
+++ b/research/experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json.review.json
@@ -0,0 +1,8 @@
+{
+ "artifact_path": "experiments/EXP-SNN004-STDP-ASYM-R2-20260919/review_request.json",
+ "review_status": "accepted_as_interpretation",
+ "reviewer": "Thomas Heisig",
+ "reviewed_at": "2026-09-19T19:25:37.411605+00:00",
+ "comments": "EXP-SNN004-STDP-ASYM-R2-20260919 ist ein sauberer, präregistrierter Stage-1-DATA-Lauf. Status: SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL. Aber weiterhin keine EVID und kein Human Review.\n\nEckdaten\n\nExperiment-ID: EXP-SNN004-STDP-ASYM-R2-20260919\n\nRQ/Hypothese/Claim: RQ-SNN-004 / H-SNN-004-A / CLAIM-SNN-001\n\nSource-Freeze: 821d2e0ecf823af3196bda782a69be8260a54943\n\nDATA-Commit: c0fabb0a21dfc5823d102ccaf8726c6b90fd4532\n\nPR: #162\n\nBranch: research/snn004-stdp-asym-r2-20260919\n\nClean Tree vor Ausführung: true\n\nDesign und Ergebnis\n\n11 Timing-Bedingungen: -50, -20, -10, -5, -1, 0, 1, 5, 10, 20, 50 ms\n\n10 Wiederholungen je Bedingung = 110 wiederholte Evaluationen\n\n0 unabhängige Runs – die 110 Wiederholungen sind keine unabhängigen Replikationen\n\nInitialgewicht: 0.5\n\nSTDP-Parameter: a_minus = 0.12, a_plus = 0.10, tau_minus = tau_plus = 20.0\n\nMittlere LTP: +0.05573050614160189\n\nMittlere LTD: -0.06687660736992224\n\nDaraus folgt: |LTD| > LTP, konsistent mit a_minus > a_plus\n\nΔt = 0: Gewichtsänderung 0.0\n\nAlle Primary Gates: PASS\n\nDesign-/Datenintegrität: PASS\n\nSTDP-/Gate-Regressionen: 71/71 PASS\n\nPrimary Gates\n\njson\n{\n \"complete_timing_grid\": true,\n \"deterministic_identity\": true,\n \"mean_direction_and_asymmetry\": true,\n \"mirror_asymmetry\": true,\n \"negative_dt_depresses\": true,\n \"positive_dt_potentiates\": true,\n \"replication_count\": true,\n \"zero_dt_unchanged\": true\n}\nGovernance-Status\n\nscientific_evidence: false\n\nhuman_review_status: PENDING\n\nautomatic_evidence_promotion: false\n\nindependent_replication: false\n\nai_review_does_not_satisfy_human_gate: true\n\nClaim-Grenze\nDer Lauf stützt nur die eng gefasste implementation-level Aussage, dass die registrierte pair-based STDP-Regel entlang der festen Pre-/Post-Timing-Kurve signierte und asymmetrische Gewichtsänderungen erzeugt. Er zeigt nicht: nützliches Lernen, Task-Performance-Verbesserung, Netzwerkgedächtnis, biologische Gleichwertigkeit, Skalierbarkeit, 5D-Vorteil, human-reviewed EVID oder unabhängige Replikation.\n\nMerge-Status\nDer Merge nach main wurde bewusst noch nicht erzwungen. Die wissenschaftlichen und STDP-spezifischen Gates sind grün, aber das vollständige Repository-CI des neuesten Formatierungs-Heads läuft noch. Das ist korrekt: erst bei grünem Gesamt-CI mergen, sonst bleibt main frei von einem ungeprüften roten Gate.",
+ "artifact_content_digest": "a31b5cc649d8c7191d3212db6ef8b94e8f66a907feb48b831bd5ec542368211c"
+}
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+ "claim_boundary": "A supported result would show only that the registered STDP-enabled synthetic association protocol achieves higher held-out classification accuracy than matched no-learning/sham/reset/shuffle controls within this simulator and task. It does not establish general intelligence, open-world learning, biological equivalence, human-like memory, scaling, a 5D advantage, external independent replication or human-reviewed EVID.",
+ "comparisons": [
+ {
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+ "unit": "independent_environment_and_initialization_seed"
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+ "integrity": {
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+ "coverage": true,
+ "evaluation_ticks": true,
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+ "holdout_episode_count": true,
+ "learning_engine_detached_during_test": true,
+ "matched_test_inputs_within_seed": true,
+ "run_count": true,
+ "runtime_errors_absent": true,
+ "teacher_absent_during_test": true,
+ "weights_frozen_during_test": true
+ },
+ "pass": true
+ },
+ "mean_accuracy": {
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+ "learning_on": 0.7640625,
+ "sham_replay": 0.5,
+ "weight_reset": 0.5,
+ "weight_shuffle": 0.5
+ },
+ "schema_version": 1,
+ "scientific_gates": {
+ "all_control_contrasts_holm_significant": true,
+ "all_control_contrasts_positive": true,
+ "learning_on_beats_all_control_means": true,
+ "primary_ci_excludes_zero": true,
+ "primary_holm_significant": true,
+ "primary_mean_difference": true
+ },
+ "status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL"
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diff --git a/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/execution_audit.md b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/execution_audit.md
new file mode 100644
index 0000000000000000000000000000000000000000..90b020275c3f99346623aaa9507ee9ec2954e5c6
--- /dev/null
+++ b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/execution_audit.md
@@ -0,0 +1,53 @@
+# SNN-005 isolated execution audit
+
+Experiment: `EXP-SNN005-HOLDOUT-R2-20260919`
+
+## Execution isolation
+
+The scientific run was executed in a detached temporary Git worktree rather
+than in the primary checkout.
+
+- source freeze: `71b90a4d95ed2e68385ac3f65cb36a39a1eea839`
+- primary checkout clean before execution: **true**
+- primary checkout clean after execution: **true**
+- generated DATA confined to isolated worktree: **true**
+- isolated worktree became dirty only because the experiment generated its DATA:
+ **true**
+- regression suite before execution: **53 passed**
+
+The verified workflow artifact was persisted without re-executing the
+simulation. The workflow artifact ID is `10590472981`.
+
+## Registered result
+
+Status: `SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL`
+
+Mean held-out accuracy:
+
+- `learning_on`: **0.7640625**
+- `learning_off`: **0.5000000**
+- `sham_replay`: **0.5000000**
+- `weight_reset`: **0.5000000**
+- `weight_shuffle`: **0.5000000**
+
+Primary contrast `learning_on - learning_off`:
+
+- mean difference: **0.2640625**
+- paired bootstrap 95% CI: **[0.1953125, 0.3328125]**
+- exact two-sided sign-flip p: **0.000030517578125**
+- Holm-adjusted p: **0.0001220703125**
+
+All preregistered scientific decision gates passed.
+
+## Governance boundary
+
+The result remains DATA only:
+
+- `scientific_evidence: false`
+- `human_review_status: PENDING`
+- `automatic_evidence_promotion: false`
+- `independent_replication: false`
+
+This audit record does not modify the experiment DATA, the registered analysis,
+or the source freeze. It provides a normal user-authored PR head so repository
+CI can evaluate the canonical persisted DATA without another experiment run.
diff --git a/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/execution_isolation_audit.json b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/execution_isolation_audit.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2a7bde4543a35f8f3a0581fe7c42d77e31753f7
--- /dev/null
+++ b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/execution_isolation_audit.json
@@ -0,0 +1,9 @@
+{
+ "experiment_id": "EXP-SNN005-HOLDOUT-R2-20260919",
+ "generated_data_confined_to_isolated_worktree": true,
+ "isolated_worktree_dirty_after_execution": true,
+ "primary_checkout_clean_after": true,
+ "primary_checkout_clean_before": true,
+ "schema_version": 1,
+ "source_freeze_commit": "71b90a4d95ed2e68385ac3f65cb36a39a1eea839"
+}
diff --git a/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/manifest.json b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..f0b0cbb9fa2ecf6dd35fe065506603e33c0a3922
--- /dev/null
+++ b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/manifest.json
@@ -0,0 +1,206 @@
+{
+ "artifacts_sha256": {
+ "canonical_association_source": "47eeaebc0861d238aa8dacfe701e1c036a2a7050eb65a044d377af9ef6cf83c1",
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
+ "evaluation_data": "2ab16f4c9a31114914d078c337365de0b0e798efb43638716dd05d948b351d00",
+ "preregistration": "5fbd5ed81e3760e215694d075dea120e7a7ba97f0a56a61f073b0f3b6b6eef7a",
+ "runner_source": "669debeaacec38dd1ce213d590dc6a0b2b713d93ae92e225b5d114a5f4064aa9",
+ "statistics": "19b076dd18e543b985e9354e98e5722f621e28e9d22c68981ac646fb3173a7b2"
+ },
+ "automatic_evidence_promotion": false,
+ "claim_boundary": "A supported result would show only that the registered STDP-enabled synthetic association protocol achieves higher held-out classification accuracy than matched no-learning/sham/reset/shuffle controls within this simulator and task. It does not establish general intelligence, open-world learning, biological equivalence, human-like memory, scaling, a 5D advantage, external independent replication or human-reviewed EVID.",
+ "design": {
+ "conditions": [
+ "learning_on",
+ "learning_off",
+ "sham_replay",
+ "weight_reset",
+ "weight_shuffle"
+ ],
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+ "expected_run_count": 80,
+ "primary_control": "learning_off",
+ "primary_metric": "test_accuracy",
+ "primary_reference": "learning_on",
+ "runner": "src.research.empirical_evaluation.run_association_generalization",
+ "runs_per_seed": 5,
+ "secondary_controls": [
+ "sham_replay",
+ "weight_reset",
+ "weight_shuffle"
+ ],
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+ "seeds": [
+ 7601,
+ 7602,
+ 7603,
+ 7604,
+ 7605,
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+ 7607,
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+ 7609,
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+ 7611,
+ 7612,
+ 7613,
+ 7614,
+ 7615,
+ 7616
+ ],
+ "test_constraints": [
+ "teacher absent during every test episode",
+ "LearningEngine detached during every test episode",
+ "fresh network per test episode",
+ "weights frozen throughout holdout evaluation",
+ "same held-out test-input digest across all five arms within each seed"
+ ],
+ "test_episodes_per_run": 40
+ },
+ "experiment_id": "EXP-SNN005-HOLDOUT-R2-20260919",
+ "experiment_status": "completed",
+ "human_review_status": "PENDING",
+ "hypothesis": "H-SNN-005-A",
+ "independent_replication": false,
+ "integrity": {
+ "checks": {
+ "clean_source_freeze": true,
+ "coverage": true,
+ "evaluation_ticks": true,
+ "fresh_network_per_episode": true,
+ "holdout_episode_count": true,
+ "learning_engine_detached_during_test": true,
+ "matched_test_inputs_within_seed": true,
+ "run_count": true,
+ "runtime_errors_absent": true,
+ "teacher_absent_during_test": true,
+ "weights_frozen_during_test": true
+ },
+ "pass": true
+ },
+ "protocol": "snn005_native_association_holdout_r2",
+ "research_question": "RQ-SNN-005",
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "results": {
+ "comparisons": [
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.19531249999999997,
+ 0.3328125
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 910,
+ "control": "learning_off",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ },
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.19374999999999998,
+ 0.33749999999999997
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 911,
+ "control": "sham_replay",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ },
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.1953125,
+ 0.3375
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 912,
+ "control": "weight_reset",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ },
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.1953125,
+ 0.3359375
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 913,
+ "control": "weight_shuffle",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ }
+ ],
+ "mean_accuracy": {
+ "learning_off": 0.5,
+ "learning_on": 0.7640625,
+ "sham_replay": 0.5,
+ "weight_reset": 0.5,
+ "weight_shuffle": 0.5
+ },
+ "primary_comparison": {
+ "bootstrap_percentile_95_ci": [
+ 0.19531249999999997,
+ 0.3328125
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 910,
+ "control": "learning_off",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ }
+ },
+ "schema_version": 1,
+ "scientific_evidence": false,
+ "scientific_gates": {
+ "all_control_contrasts_holm_significant": true,
+ "all_control_contrasts_positive": true,
+ "learning_on_beats_all_control_means": true,
+ "primary_ci_excludes_zero": true,
+ "primary_holm_significant": true,
+ "primary_mean_difference": true
+ },
+ "source_freeze": {
+ "branch": "HEAD",
+ "commit": "71b90a4d95ed2e68385ac3f65cb36a39a1eea839",
+ "dirty_before_execution": false
+ },
+ "stage": 1
+}
diff --git a/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/report.md b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..e715c09655e1c90048ae6524dccd2bcc56137c02
--- /dev/null
+++ b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/report.md
@@ -0,0 +1,119 @@
+# EXP-SNN005-HOLDOUT-R2-20260919
+
+RQ-SNN-005 / H-SNN-005-A
+
+Status: **SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL**
+
+Source freeze: 71b90a4d95ed2e68385ac3f65cb36a39a1eea839
+Runs: 80
+Seeds: 16
+Holdout episodes per arm/seed: 40
+
+Mean held-out accuracy:
+
+{
+ "learning_off": 0.5,
+ "learning_on": 0.7640625,
+ "sham_replay": 0.5,
+ "weight_reset": 0.5,
+ "weight_shuffle": 0.5
+}
+
+Primary and robustness comparisons:
+
+[
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.19531249999999997,
+ 0.3328125
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 910,
+ "control": "learning_off",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ },
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.19374999999999998,
+ 0.33749999999999997
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 911,
+ "control": "sham_replay",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ },
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.1953125,
+ 0.3375
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 912,
+ "control": "weight_reset",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ },
+ {
+ "bootstrap_percentile_95_ci": [
+ 0.1953125,
+ 0.3359375
+ ],
+ "bootstrap_resamples": 2000,
+ "bootstrap_seed": 913,
+ "control": "weight_shuffle",
+ "degenerate_difference_variance": false,
+ "exact_two_sided_sign_flip_p": 3.0517578125e-05,
+ "holm_adjusted_p": 0.0001220703125,
+ "interpretation": "exploratory; no automatic hypothesis or EVID acceptance",
+ "mean_difference": 0.2640625,
+ "metric": "test_accuracy",
+ "n_seed_pairs": 16,
+ "paired_standardized_effect_dz": 1.7975197605953888,
+ "reference": "learning_on",
+ "unit": "independent_environment_and_initialization_seed"
+ }
+]
+
+Scientific gates:
+
+{
+ "all_control_contrasts_holm_significant": true,
+ "all_control_contrasts_positive": true,
+ "learning_on_beats_all_control_means": true,
+ "primary_ci_excludes_zero": true,
+ "primary_holm_significant": true,
+ "primary_mean_difference": true
+}
+
+Claim boundary:
+
+A supported result would show only that the registered STDP-enabled synthetic association protocol achieves higher held-out classification accuracy than matched no-learning/sham/reset/shuffle controls within this simulator and task. It does not establish general intelligence, open-world learning, biological equivalence, human-like memory, scaling, a 5D advantage, external independent replication or human-reviewed EVID.
+
+Human review remains PENDING. No automatic EVID promotion or independent
+replication is claimed.
diff --git a/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json
new file mode 100644
index 0000000000000000000000000000000000000000..2959a4597f70705047b05eb3af70c65fff4448f6
--- /dev/null
+++ b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json
@@ -0,0 +1,19 @@
+{
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_evidence_promotion": false,
+ "experiment_id": "EXP-SNN005-HOLDOUT-R2-20260919",
+ "human_review_status": "PENDING",
+ "hypothesis": "H-SNN-005-A",
+ "independent_replication": false,
+ "questions": [
+ "Are the five arms matched and are held-out test inputs identical within each seed?",
+ "Are teacher and LearningEngine absent during every evaluation episode?",
+ "Do the paired seed-level inference and Holm family match the preregistered analysis?",
+ "Does the interpretation remain limited to this synthetic association task?"
+ ],
+ "research_question": "RQ-SNN-005",
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "reviewer_type_required": "human",
+ "schema_version": 1,
+ "scientific_evidence": false
+}
diff --git a/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json.review.json b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json.review.json
new file mode 100644
index 0000000000000000000000000000000000000000..6f5bcb8bbbeb95b9d226a8f20af550763d38f9bf
--- /dev/null
+++ b/research/experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json.review.json
@@ -0,0 +1,8 @@
+{
+ "artifact_path": "experiments/EXP-SNN005-HOLDOUT-R2-20260919/review_request.json",
+ "review_status": "accepted_as_interpretation",
+ "reviewer": "Thomas Heisig",
+ "reviewed_at": "2026-09-20T07:03:30.741909+00:00",
+ "comments": "Was wurde gemacht?\n\nDas Experiment EXP-SNN005-HOLDOUT-R2-20260919 zur Hypothese H-SNN-005-A wurde ausgewertet.\n\nEs gibt fünf Bedingungen: learning_on, learning_off, sham_replay, weight_reset, weight_shuffle.\n\nGeplant waren 16 Seeds, 80 Runs, pro Arm und Seed 40 Holdout-Episoden mit je 12 Auswertungs-Ticks.\n\nIm Test sollten kein Lehrer und keine Lern-Engine aktiv sein, die Gewichte sollten eingefroren sein und pro Episode ein frisches Netz verwendet werden.\n\nWas kam heraus?\n\nlearning_on: 76,4 % Genauigkeit.\n\nAlle Kontrollen: 50 % Genauigkeit.\n\nDifferenz: +26,4 Prozentpunkte für learning_on.\n\nDie gepaarte Auswertung über 16 Seeds ist deutlich: exakter Sign-Flip-p-Wert ca. 3,05e-05, Holm-korrigiert ca. 0,000122, 95%-CI etwa 0,195 bis 0,333, Effektstärke dz ≈ 1,80.\n\nAlle im Bericht genannten wissenschaftlichen Gates sind bestanden.\n\nWaren die Kontrollen fair?\n\nJa, soweit aus den Metadaten und Integritätschecks ersichtlich.\n\nDie Testeingaben sind innerhalb eines Seeds über alle Arme identisch. Für Seed 7601 ist der Test-Input-Digest bei allen Bedingungen gleich.\n\nDie Integritätschecks sagen: gleiche Testeingaben innerhalb des Seeds, gleiche Episodenzahl, gleiche Ticks, frische Netze, Gewichte eingefroren.\n\nEinschränkung: Im vorliegenden Auszug sind die vollständigen Trial-Daten nur für einen Seed sichtbar. Die Aussage für alle 16 Seeds stützt sich auf das Manifest und die Integritätsprüfungen.\n\nWaren Lehrer und Lern-Engine im Test wirklich weg?\n\nJa. In den gezeigten Metriken steht test_teacher_present: false und test_learning_engine_attached: false.\n\nDie Integritätschecks bestätigen: Lehrer abwesend, Lern-Engine abgekoppelt, Gewichte während des Tests eingefroren.\n\nBleibt die Interpretation begrenzt?\n\nJa. Das Ergebnis zeigt nur: In diesem synthetischen Assoziations-Task mit STDP erreicht das registrierte Protokoll eine höhere Holdout-Genauigkeit als passende Kontrollen.\n\nEs zeigt ausdrücklich nicht: allgemeine Intelligenz, Open-World-Lernen, biologische Äquivalenz, menschliches Gedächtnis, Skalierung, einen 5D-Vorteil, externe unabhängige Replikation oder menschlich geprüftes EVID.\n\nStatus\n\nErgebnis: SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL\n\nMenschliche Prüfung: PENDING\n\nKeine automatische EVID-Beförderung\n\nKeine unabhängige Replikation\n\nDiese AI-Prüfung ersetzt keine menschliche Prüfung\n\nKurzfazit\nDie Daten sind intern konsistent und stützen die Hypothese innerhalb des vorregistrierten Protokolls. Aber der nächste Schritt ist eine menschliche Prüfung. Bis dahin bleibt es ein protokollintern unterstütztes Ergebnis, kein allgemeiner wissenschaftlicher Beweis.",
+ "artifact_content_digest": "df00a4f0510a7d5e2e4f8d8d5c026ced7e3da62e8303737cd6a2308f34201905"
+}
diff --git a/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/DATA/confirmatory_result.json b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/DATA/confirmatory_result.json
new file mode 100644
index 0000000000000000000000000000000000000000..71a51742553067e7c5efe19f38267c1394f0afb7
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/DATA/confirmatory_result.json
@@ -0,0 +1,70 @@
+{
+ "experiment_id": "EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2",
+ "data_class": "DATA",
+ "mode": "CONFIRMATORY",
+ "preregistration_id": "PREREG-EVAL-006-V2",
+ "research_question_id": "RQ-EVAL-006",
+ "hypothesis_ids": ["H-EVAL-006-A", "H-EVAL-006-B", "H-EVAL-006-C"],
+ "execution": {
+ "date": "2026-09-16",
+ "workflow_run_id": 35149828152,
+ "workflow_job_id": 104975033645,
+ "artifact_id": 10468901959,
+ "artifact_sha256": "1c7e430fa75edb02ef7bfbaacf86cc85699d7a9ab5a6154bb81ce2da7bb0c89c",
+ "head_commit": "e84ee7efa56a9bf8d341e661325f79c82530f7bb",
+ "python": "3.13.15",
+ "brian2": "2.10.1",
+ "backend": "numpy",
+ "dt_ms": 1.0
+ },
+ "diagnostic_seed_overlap": false,
+ "confirmatory_seeds": [21001, 21002, 21003],
+ "results": {
+ "H-EVAL-006-A": {
+ "model": "izhikevich-2003",
+ "supported_by_protocol": true,
+ "samples_per_seed": 64,
+ "spike_decisions_equal_all": true,
+ "worst_max_abs_pre_reset_v_error": 6.821210263296962e-13,
+ "worst_max_abs_pre_reset_u_error": 3.552713678800501e-15,
+ "worst_max_abs_post_reset_v_error": 4.263256414560601e-14,
+ "worst_max_abs_post_reset_u_error": 3.552713678800501e-15,
+ "acceptance_threshold": 1e-8,
+ "seed_results": [
+ {"seed": 21001, "conformance_within_1e_8": true, "spike_decisions_equal": true, "max_abs_pre_reset_v_error": 2.2737367544323206e-13, "max_abs_pre_reset_u_error": 3.552713678800501e-15, "max_abs_post_reset_v_error": 4.263256414560601e-14, "max_abs_post_reset_u_error": 3.552713678800501e-15},
+ {"seed": 21002, "conformance_within_1e_8": true, "spike_decisions_equal": true, "max_abs_pre_reset_v_error": 4.547473508864641e-13, "max_abs_pre_reset_u_error": 1.7763568394002505e-15, "max_abs_post_reset_v_error": 4.263256414560601e-14, "max_abs_post_reset_u_error": 3.552713678800501e-15},
+ {"seed": 21003, "conformance_within_1e_8": true, "spike_decisions_equal": true, "max_abs_pre_reset_v_error": 6.821210263296962e-13, "max_abs_pre_reset_u_error": 2.6645352591003757e-15, "max_abs_post_reset_v_error": 4.263256414560601e-14, "max_abs_post_reset_u_error": 2.6645352591003757e-15}
+ ]
+ },
+ "H-EVAL-006-B": {
+ "model": "lif-current-v1",
+ "refractory_ticks": 0,
+ "supported_by_protocol": true,
+ "spike_events_equal_all": true,
+ "worst_max_abs_v_error": 7.105427357601002e-15,
+ "acceptance_threshold": 1e-8,
+ "seed_results": [
+ {"seed": 21001, "native_spike_count": 78, "brian2_spike_count": 78, "spike_events_equal": true, "max_abs_v_error": 7.105427357601002e-15},
+ {"seed": 21002, "native_spike_count": 71, "brian2_spike_count": 71, "spike_events_equal": true, "max_abs_v_error": 0.0},
+ {"seed": 21003, "native_spike_count": 73, "brian2_spike_count": 73, "spike_events_equal": true, "max_abs_v_error": 0.0}
+ ]
+ },
+ "H-EVAL-006-C": {
+ "model": "lif-current-v1",
+ "supported_by_protocol": true,
+ "mapping_dt_1ms": {"1": 2, "2": 3, "3": 4},
+ "mapping_units": {"mhrn": "refractory_ticks", "brian2": "ms"},
+ "all_nine_mapping_runs_spike_equal": true,
+ "worst_max_abs_v_error": 7.105427357601002e-15,
+ "acceptance_threshold": 1e-8
+ }
+ },
+ "primary_success": true,
+ "all_declared_hypotheses_pass": true,
+ "status": "passed",
+ "automatic_evid_promotion": false,
+ "human_review_required": true,
+ "independent_authorship_replication": false,
+ "historical_v1_disposition": "PRESERVED_NEGATIVE",
+ "interpretation_limit": "This DATA validates the declared MHRN single-neuron numerical contracts against an external Brian2 reference under the frozen V2 protocol. It is not biological equivalence and is not an independently authored replication."
+}
diff --git a/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/EVIDENCE_PROMOTION_STATUS.json b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/EVIDENCE_PROMOTION_STATUS.json
new file mode 100644
index 0000000000000000000000000000000000000000..68c8938a4c30193b3df088bc8dc392969e9e28d7
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/EVIDENCE_PROMOTION_STATUS.json
@@ -0,0 +1,28 @@
+{
+ "schema_version": 1,
+ "experiment_id": "EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2",
+ "human_review_status": "COMPLETED_SUPPORTS_SCOPED_CLAIM",
+ "evidence_promotion_status": "BLOCKED_LEGACY_PROVENANCE_CONTRACT",
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "verified_execution": {
+ "workflow_run_id": 35149828152,
+ "workflow_job_id": 104975033645,
+ "workflow_conclusion": "success",
+ "frozen_execution_commit": "e84ee7efa56a9bf8d341e661325f79c82530f7bb",
+ "artifact_id": 10468901959,
+ "artifact_sha256": "1c7e430fa75edb02ef7bfbaacf86cc85699d7a9ab5a6154bb81ce2da7bb0c89c",
+ "brian2": "2.10.1",
+ "python": "3.13.15"
+ },
+ "integrity_rule": "The positive human review does not substitute for the current EvidenceEngine provenance gate. A promotion-eligible internal replication must satisfy the provenance contract before EVID registration; it still does not count as independent replication.",
+ "independent_replication_complete": false,
+ "missing_for_current_evidence_engine": [
+ "experiment manifest.json with experiment_status=completed",
+ "validity.valid=true with zero runtime/fatal errors",
+ "recorded git.dirty=false",
+ "provenance_digests",
+ "source_freeze_sha matching the provenance digest"
+ ],
+ "required_for_evid": "Separately frozen promotion-eligible replication under the current EvidenceEngine provenance contract, followed by a separate EVID registration step."
+}
diff --git a/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/human_scientific_review.json b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/human_scientific_review.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f0a74a0bed69bdcd566a4125460591b88471bf9
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/human_scientific_review.json
@@ -0,0 +1,45 @@
+{
+ "schema_version": 1,
+ "experiment_id": "EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2",
+ "review_type": "human_scientific_review",
+ "decision": "supports_scoped_claim",
+ "review_status": "completed",
+ "reviewed_at": "2026-09-18",
+ "reviewed_material": [
+ "research/preregistrations/operational/single_neuron_conformance_v2.json",
+ "research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/DATA/confirmatory_result.json",
+ "research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/report.md",
+ "research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json",
+ "research/generated/verification/single_neuron_scientific_readiness.json"
+ ],
+ "scoped_claim": "Unter dem vor der Ausführung eingefrorenen V2-Protokoll konformieren die deklarierten MHRN-Einzelzell-Verträge (Izhikevich-2003 lokale Transition/Threshold/Reset, lif-current-v1 mit refractory_ticks=0 sowie das separat klassifizierte Refractory-Semantik-Mapping) mit der gematchten Brian2-2.10.1-Referenz innerhalb der präregistrierten Toleranzen.",
+ "review_points": {
+ "preregistered_tolerance": {
+ "decision": "accepted",
+ "finding": "Die 1e-8-Akzeptanzschwelle war vor der Confirmatory-Ausführung in PREREG-EVAL-006-V2 fixiert. Die beobachteten Fehler um 1e-13/1e-15 sind deutlich kleiner, rechtfertigen aber keine post-hoc Änderung des Erfolgsvertrags."
+ },
+ "v1_v2_reconciliation": {
+ "decision": "accepted",
+ "finding": "V1 prüft freie 1000-Tick-Trajektorienidentität; V2 lokale Ein-Schritt-Transition/Threshold/Reset-Konformität. Beide Aussagen sind logisch verschieden; der V1-Negativbefund bleibt erhalten."
+ },
+ "hypothesis_c_class": {
+ "decision": "accepted_with_classification",
+ "finding": "H-EVAL-006-C ist ein Semantik-/Mapping-Claim. Die validierte Zuordnung bei dt=1 ms lautet MHRN refractory_ticks 1/2/3 -> Brian2 2/3/4 ms. C wird nicht als dritte numerische Konformitätsaussage interpretiert."
+ },
+ "claim_scope": {
+ "decision": "accepted",
+ "finding": "Freigegeben ist ausschließlich der scoped Modell-/Numerikclaim. Ungeprüfte Modelle/Parameter, biologische Gleichwertigkeit, universelle Langzeittrajektorienidentität und darüber hinausgehende Generalisierung sind ausgeschlossen."
+ },
+ "independent_replication": {
+ "decision": "remains_open",
+ "finding": "Brian2 ist eine externe Software-Referenz, keine unabhängig autorisierte Replikation. Das Independent-Replication-Gate bleibt offen."
+ }
+ },
+ "evidence_decision": {
+ "review_supports_evidence_promotion_in_principle": true,
+ "promotion_completed": false,
+ "promotion_block": "legacy_run_missing_current_evidence_engine_manifest_contract",
+ "note": "Die Review ist positiv. Die kanonische EVID-Promotion bleibt blockiert, bis ein separat eingefrorener promotion-eligible Replikationslauf den aktuellen EvidenceEngine-Provenienzvertrag erfüllt. Dies ist keine unabhängige Replikation."
+ },
+ "data_mutated": false
+}
diff --git a/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/report.md b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..a1ef4e4b7c8b010b4ed2d59cfb23c52b5ab7f32b
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/report.md
@@ -0,0 +1,54 @@
+# EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2
+
+**Status:** confirmatory DATA complete
+**Preregistration:** `PREREG-EVAL-006-V2`
+**RQ:** `RQ-EVAL-006`
+**Hypotheses:** `H-EVAL-006-A`, `H-EVAL-006-B`, `H-EVAL-006-C`
+
+## Result
+
+All endpoints frozen before the confirmatory run were satisfied on the disjoint validation seeds `21001`, `21002`, `21003`.
+
+### Izhikevich-2003
+
+The matched one-step split-Euler transition agreed with Brian2 for all 192 confirmatory samples. Spike decisions were identical. The worst observed pre-reset voltage error was `6.821210263296962e-13`, well below the frozen `1e-8` threshold. Pre/post-reset recovery and post-reset voltage errors were also below `1e-12`.
+
+This supports **local transition, threshold and reset conformance**. It does not erase the historical V1 free-running 1000-tick negative. V1 remains a documented result showing that microscopic floating-point differences can amplify in a nonlinear free-running trajectory until later spike timing diverges.
+
+### LIF-current-v1
+
+For the canonical alternative-model setting `refractory_ticks=0`, all three 1000-tick/5-cell comparisons had identical spike events. Worst membrane error was `7.105427357601002e-15`.
+
+For the optional nonzero refractory extension, all nine frozen mapping runs passed. At `dt_ms=1.0`, the validated semantic mapping is:
+
+| MHRN | Brian2 |
+| ---: | ---: |
+| `refractory_ticks=1` | `2 ms` |
+| `refractory_ticks=2` | `3 ms` |
+| `refractory_ticks=3` | `4 ms` |
+
+Equal numeric values are therefore not equivalent refractory semantics.
+
+## Homeostasis operating envelope
+
+The preceding bounded diagnostic established a configuration-specific LIF operating envelope for the 10 Hz controller. With the declared parameters, currents 16, 18, 20, 22 and 25 converged to 10 Hz without threshold-offset saturation. At current 30, the +10 mV actuator bound saturated and the target was not reachable.
+
+This is an operating-envelope result, not a universal current boundary and not a biological equivalence claim.
+
+## Model-axis rule
+
+Izhikevich remains the canonical default. `lif-current-v1` is a validated selectable alternative and may be introduced as an additional treatment arm in scientifically eligible experiments. Such use must record the complete `NeuronConfig`, model provenance and a model-specific additional hypothesis/contrast. Existing primary hypotheses must not be rewritten after data inspection.
+
+## Evidence boundary
+
+This experiment is **DATA**. Brian2 is an external software reference, but this run is not an independently authored replication and is not automatically promoted to EVID. Human evidence review remains a separate repository gate.
+
+## Provenance
+
+- GitHub Actions run: `35149828152`
+- Job: `104975033645`
+- Artifact: `10468901959`
+- Artifact SHA-256: `1c7e430fa75edb02ef7bfbaacf86cc85699d7a9ab5a6154bb81ce2da7bb0c89c`
+- Frozen execution commit: `e84ee7efa56a9bf8d341e661325f79c82530f7bb`
+- Brian2: `2.10.1`
+- Python: `3.13.15`
diff --git a/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb29c55f85c312c192660f98d3cb07c6bd4574fc
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json
@@ -0,0 +1,57 @@
+{
+ "schema_version": 1,
+ "experiment_id": "EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2",
+ "stage": 0,
+ "research_question": "RQ-EVAL-006",
+ "hypotheses": [
+ "H-EVAL-006-A",
+ "H-EVAL-006-B",
+ "H-EVAL-006-C"
+ ],
+ "protocol": "single_neuron_conformance_v2",
+ "human_review_status": "COMPLETED",
+ "evidence_readiness": "BLOCKED_EVIDENCE_ENGINE_PROVENANCE",
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "review_scope": [
+ "confirmatory preregistration was frozen before execution",
+ "diagnostic seeds 20001-20003 are disjoint from confirmatory seeds 21001-21003",
+ "Izhikevich one-step transition, threshold and reset conformance against Brian2 2.10.1",
+ "LIF current-model conformance with refractory_ticks=0",
+ "nonzero LIF refractory semantic mapping",
+ "preservation of the historical 1000-tick free-running Izhikevich negative result",
+ "homeostasis statement limited to the declared diagnostic operating envelope",
+ "claim boundary: numerical/model conformance is not biological equivalence",
+ "Brian2 reference conformance is not an independently authored replication"
+ ],
+ "artifacts": {
+ "preregistration": "preregistrations/operational/single_neuron_conformance_v2.json",
+ "confirmatory_data": "experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/DATA/confirmatory_result.json",
+ "report": "experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/report.md",
+ "readiness": "generated/verification/single_neuron_scientific_readiness.json",
+ "model_axis": "specifications/NEURON_MODEL_EXPERIMENT_AXIS.md",
+ "questions": "registry/questions.empirical.yaml",
+ "hypotheses": "registry/hypotheses.empirical.yaml"
+ },
+ "allowed_interpretation": "Within the frozen V2 protocol, the declared MHRN Izhikevich-2003 local transition/reset contract and the declared LIF-current-v1 contract conform to the matched Brian2 reference within the preregistered tolerances. The historical long free-running negative remains valid.",
+ "prohibited_interpretations": [
+ "biological equivalence",
+ "universal long-horizon trajectory identity",
+ "independent replication",
+ "automatic EVID promotion",
+ "generalization to neuron models or parameter regimes not included in the frozen protocol"
+ ],
+ "required_human_decision": "supports_scoped_claim | rejects_scoped_claim | requests_revision",
+ "note": "Human Review supports the scoped claim. Canonical EVID promotion remains blocked until a promotion-eligible replication is executed under the current EvidenceEngine provenance contract. Independent replication remains a separate gate.",
+ "human_review_decision": "supports_scoped_claim",
+ "reviewed_at": "2026-09-18",
+ "independent_replication": false,
+ "review_findings": {
+ "tolerance_pre_registered": true,
+ "v1_v2_reconciliation_clear": true,
+ "hypothesis_c_class": "semantic_mapping",
+ "scoped_claim_confirmed": true,
+ "independent_replication_remains_open": true
+ }
+}
diff --git a/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json.review.json b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json.review.json
new file mode 100644
index 0000000000000000000000000000000000000000..5869b416b0057398f5dabc0718d33917cf0638df
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json.review.json
@@ -0,0 +1,8 @@
+{
+ "artifact_path": "experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/review_request.json",
+ "review_status": "accepted_as_interpretation",
+ "reviewer": "KI",
+ "reviewed_at": "2026-09-18T17:50:05.2017497Z",
+ "comments": "Die Review-Kriterien sind erfüllt und der scoped numerische Konformitätsclaim ist als Interpretation akzeptiert: Das Confirmatory-Protokoll war vor der Ausführung eingefroren, die Seeds 21001-21003 sind von den diagnostischen Seeds 20001-20003 disjunkt, alle drei Hypothesen erfüllen ihre deklarierten Endpunkte, die Izhikevich-Transition/Schwelle/Reset-Übereinstimmung sowie die LIF-Konformität für refractory_ticks=0 und die nichttriviale Refractory-Abbildung sind dokumentiert, und das historische 1000-Tick-V1-Negativergebnis bleibt erhalten. Die Homeostasis-Aussage ist auf den deklarierten diagnostischen Betriebsbereich begrenzt. Diese Entscheidung behauptet weder biologische Gleichwertigkeit noch universelle Langzeittrajektorien noch unabhängige Replikation. Brian2 bleibt eine externe Referenz; die DATA wird nicht zu EVID promoted und keine automatische EVID-Promotion ausgelöst.",
+ "artifact_content_digest": "55408614e29298cace59ffe63b1ff1ff6826ac1965f58454775bf5545222494a"
+}
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/DATA/confirmatory_result.json b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/DATA/confirmatory_result.json
new file mode 100644
index 0000000000000000000000000000000000000000..85d844d0cc54960b4ee1f8a2ddda2b7160875b93
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/DATA/confirmatory_result.json
@@ -0,0 +1,180 @@
+{
+ "H-EVAL-006-A": {
+ "runs": [
+ {
+ "conformance_within_1e_8": true,
+ "first_failure": null,
+ "max_abs_post_reset_u_error": 1.7763568394002505e-15,
+ "max_abs_post_reset_v_error": 5.684341886080802e-14,
+ "max_abs_pre_reset_u_error": 1.7763568394002505e-15,
+ "max_abs_pre_reset_v_error": 2.2737367544323206e-13,
+ "samples": 64,
+ "seed": 22001,
+ "spike_decisions_equal": true
+ },
+ {
+ "conformance_within_1e_8": true,
+ "first_failure": null,
+ "max_abs_post_reset_u_error": 1.7763568394002505e-15,
+ "max_abs_post_reset_v_error": 4.263256414560601e-14,
+ "max_abs_pre_reset_u_error": 1.7763568394002505e-15,
+ "max_abs_pre_reset_v_error": 2.2737367544323206e-13,
+ "samples": 64,
+ "seed": 22002,
+ "spike_decisions_equal": true
+ },
+ {
+ "conformance_within_1e_8": true,
+ "first_failure": null,
+ "max_abs_post_reset_u_error": 3.552713678800501e-15,
+ "max_abs_post_reset_v_error": 4.263256414560601e-14,
+ "max_abs_pre_reset_u_error": 3.552713678800501e-15,
+ "max_abs_pre_reset_v_error": 4.547473508864641e-13,
+ "samples": 64,
+ "seed": 22003,
+ "spike_decisions_equal": true
+ }
+ ],
+ "supported_by_protocol": true
+ },
+ "H-EVAL-006-B": {
+ "runs": [
+ {
+ "brian2_refractory_ms": 0,
+ "brian2_spike_count": 74,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 0,
+ "native_spike_count": 74,
+ "seed": 22001,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 0,
+ "brian2_spike_count": 73,
+ "max_abs_v_error": 7.105427357601002e-15,
+ "native_refractory_ticks": 0,
+ "native_spike_count": 73,
+ "seed": 22002,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 0,
+ "brian2_spike_count": 72,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 0,
+ "native_spike_count": 72,
+ "seed": 22003,
+ "spike_events_equal": true
+ }
+ ],
+ "supported_by_protocol": true
+ },
+ "H-EVAL-006-C": {
+ "claim_type": "semantic_mapping",
+ "mapping": {
+ "1": 2,
+ "2": 3,
+ "3": 4
+ },
+ "runs": [
+ {
+ "brian2_refractory_ms": 2,
+ "brian2_spike_count": 72,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 1,
+ "native_spike_count": 72,
+ "seed": 22001,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 3,
+ "brian2_spike_count": 69,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 2,
+ "native_spike_count": 69,
+ "seed": 22001,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 4,
+ "brian2_spike_count": 67,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 3,
+ "native_spike_count": 67,
+ "seed": 22001,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 2,
+ "brian2_spike_count": 69,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 1,
+ "native_spike_count": 69,
+ "seed": 22002,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 3,
+ "brian2_spike_count": 68,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 2,
+ "native_spike_count": 68,
+ "seed": 22002,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 4,
+ "brian2_spike_count": 65,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 3,
+ "native_spike_count": 65,
+ "seed": 22002,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 2,
+ "brian2_spike_count": 70,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 1,
+ "native_spike_count": 70,
+ "seed": 22003,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 3,
+ "brian2_spike_count": 67,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 2,
+ "native_spike_count": 67,
+ "seed": 22003,
+ "spike_events_equal": true
+ },
+ {
+ "brian2_refractory_ms": 4,
+ "brian2_spike_count": 65,
+ "max_abs_v_error": 0,
+ "native_refractory_ticks": 3,
+ "native_spike_count": 65,
+ "seed": 22003,
+ "spike_events_equal": true
+ }
+ ],
+ "supported_by_protocol": true
+ },
+ "all_declared_hypotheses_pass": true,
+ "automatic_evid_promotion": false,
+ "experiment_id": "EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1",
+ "human_review_required": true,
+ "independent_authorship_replication": false,
+ "mode": "REPLICATION",
+ "parent_preregistration_id": "PREREG-EVAL-006-V2",
+ "preregistration_id": "PREREG-EVAL-006-V2-PROMO-R1",
+ "protocol": "single_neuron_conformance_v2",
+ "replication_class": "INTERNAL_PROMOTION_REPLICATION",
+ "seeds": [
+ 22001,
+ 22002,
+ 22003
+ ],
+ "source_data_mutated": false
+}
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/EVIDENCE_PROMOTION_STATUS.json b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/EVIDENCE_PROMOTION_STATUS.json
new file mode 100644
index 0000000000000000000000000000000000000000..0567055af8201aa9eb373efb59a03a71c1b1670f
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/EVIDENCE_PROMOTION_STATUS.json
@@ -0,0 +1,23 @@
+{
+ "schema_version": 1,
+ "experiment_id": "EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1",
+ "human_review_status": "COMPLETED_SUPPORTS_SCOPED_CLAIM",
+ "evidence_promotion_status": "COMPLETED",
+ "evidence_id": "EVID-2026-18",
+ "claim_id": "CLAIM-EVAL-006",
+ "scientific_evidence": true,
+ "automatic_evidence_promotion": false,
+ "independent_replication_complete": false,
+ "independent_authorship_replication": false,
+ "maturity_effect": {
+ "reviewed_evidence": "partial -> met",
+ "stage0_scientific_maturity": "82.5% -> 92.5%",
+ "remaining_gap": "7.5% independent authorship replication"
+ },
+ "boundaries": [
+ "No biological equivalence",
+ "No universal long-horizon trajectory identity",
+ "No generalization to untested neuron models or parameter regimes",
+ "No independent replication claim"
+ ]
+}
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/human_review.json b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/human_review.json
new file mode 100644
index 0000000000000000000000000000000000000000..6e085414cea36e45941db6a05830b7398e60d479
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/human_review.json
@@ -0,0 +1,12 @@
+{
+ "experiment_id": "EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1",
+ "reviewer": "Thomas Heisig",
+ "decision": "supports",
+ "comments": "Kanonische EvidenceEngine-Abbildung des am 18.09.2026 dokumentierten Human Reviews. Der Review akzeptiert die eng begrenzte Interpretation des Promotion-R1-Laufs: eingefrorene Preregistrierung, vorab deklarierte disjunkte Seeds 22001-22003, bestandene H-EVAL-006-A/B/C-Endpunkte, clean Git tree, vollständige Provenienz-Digests und Source-Freeze. Die Entscheidung gilt ausschließlich für CLAIM-EVAL-006 im deklarierten V2-Betriebsbereich; sie begründet weder biologische Gleichwertigkeit noch universelle Langzeittrajektorienidentität noch unabhängige Replikation.",
+ "reviewed_at": "2026-09-18T18:37:59.173075+00:00",
+ "source_review_artifact": "review_request.json.review.json",
+ "source_review_status": "accepted_as_interpretation",
+ "source_required_human_decision": "supports_scoped_claim | rejects_scoped_claim | requests_revision",
+ "scope": "CLAIM-EVAL-006 only",
+ "independent_authorship_replication": false
+}
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/manifest.json b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..9817f87b0cad569b69f56d2a178b04f8e5c60689
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/manifest.json
@@ -0,0 +1,92 @@
+{
+ "experiment_id": "EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1",
+ "experiment_status": "completed",
+ "ai_exposure": "none",
+ "ai_reproducibility": "R0",
+ "ai_protocol": {
+ "version": 1,
+ "bump_reason": "initial registration"
+ },
+ "causal_taint": "PURE",
+ "ai_interactions": [],
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "timestamp": "2026-09-18T18:12:49.676328",
+ "created_at": "2026-09-18T18:12:49.676339+00:00",
+ "git": {
+ "commit": "0376142b16a092c674653ee61c10d8137671eacf",
+ "dirty": false,
+ "branch": "experiment/stage0-promotion-r1-20260918"
+ },
+ "software": {
+ "python": "3.13.15",
+ "os": "Linux 6.17.0-1022-azure",
+ "brain5d_version": "0.6.0a5"
+ },
+ "simulation": {
+ "seeds": [
+ 22001,
+ 22002,
+ 22003
+ ],
+ "dt_ms": 1
+ },
+ "artifacts": {
+ "confirmatory_data": "DATA/confirmatory_result.json",
+ "report": "report.md",
+ "preregistration": "research/preregistrations/operational/single_neuron_conformance_v2_promotion_r1.json"
+ },
+ "research_questions": [
+ "RQ-EVAL-006"
+ ],
+ "hypotheses": [
+ "H-EVAL-006-A",
+ "H-EVAL-006-B",
+ "H-EVAL-006-C"
+ ],
+ "config": {
+ "path": "research/preregistrations/operational/single_neuron_conformance_v2_promotion_r1.json",
+ "sha256": "76642fc2d9e6fc5e8dd4947ba5b5172de1cd53d1a920788834234edeab5e28a0"
+ },
+ "validity": {
+ "valid": true,
+ "reason": null,
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "runtime_errors": [],
+ "research_run_mode": "REPLICATION",
+ "data_partitions": [
+ "SCIENTIFIC_HOLDOUT"
+ ],
+ "network_mode": "OFFLINE",
+ "confirmatory_lock": {
+ "protocol_digest": "49d5a12e2e3d2e68a94788b6e1fd6c7279aa1fe7e81058cc71a07fa2c9fb1def",
+ "prompt_digest": "f88122f125edb150c2dcdb51c50088e88d33c9bfad38c12aa3cb7a9256bf5c26",
+ "analysis_digest": "0173bbdb455ee954b09cc61a9e7e0929c4d6796e488b80a1ee92d15cd2d972ff",
+ "locked": true
+ },
+ "provenance_digests": {
+ "code": "4d86e76e52a20fae0cbae49c1fd598d9e01fbe6344a17a50d8ddb6ebba14e1a7",
+ "config": "76642fc2d9e6fc5e8dd4947ba5b5172de1cd53d1a920788834234edeab5e28a0",
+ "prompt": "f88122f125edb150c2dcdb51c50088e88d33c9bfad38c12aa3cb7a9256bf5c26",
+ "data": "06f5e550685c2c4dba351f9d79f29df13883dceae492a8ad3c1f3df55ddbf241"
+ },
+ "source_freeze_sha": "36764dde292a3934523b21c5d9559319a0d8c9a8763c9ab3dc1c5cbbc246e850",
+ "results": {
+ "protocol": "single_neuron_conformance_v2",
+ "replication_class": "INTERNAL_PROMOTION_REPLICATION",
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "all_declared_hypotheses_pass": true,
+ "automatic_evidence_promotion": false,
+ "scientific_evidence": false,
+ "human_review_required": true,
+ "independent_authorship_replication": false
+ },
+ "runtime": {
+ "duration_seconds": 22.364484189999985
+ }
+}
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/report.md b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..e23e31ff04672c10902699c8f456eee22712f190
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/report.md
@@ -0,0 +1,19 @@
+# Stage-0 promotion replication R1
+
+**Experiment:** `EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1`
+**Preregistration:** `PREREG-EVAL-006-V2-PROMO-R1`
+**Mode:** internal promotion replication; DATA only
+**Independent authorship replication:** false
+
+## Result
+
+- H-EVAL-006-A: True
+- H-EVAL-006-B: True
+- H-EVAL-006-C: True (semantic mapping claim)
+- Maximum Izhikevich local-state error: 4.5474735088646412e-13
+- Maximum LIF membrane error: 7.1054273576010019e-15
+- Frozen tolerance: 1e-08
+
+## Claim boundary
+
+A positive result can make the scoped claim eligible for separate EvidenceEngine review/promotion. It does not itself create EVID, does not overwrite the V1 long-horizon negative, and does not count as independent replication, biological equivalence or evidence for untested regimes.
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json
new file mode 100644
index 0000000000000000000000000000000000000000..0fb8d22595c58446f6231f808acf67e9b4752e27
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json
@@ -0,0 +1,76 @@
+{
+ "schema_version": 1,
+ "experiment_id": "EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1",
+ "stage": 0,
+ "research_question": "RQ-EVAL-006",
+ "hypotheses": [
+ "H-EVAL-006-A",
+ "H-EVAL-006-B",
+ "H-EVAL-006-C"
+ ],
+ "claim_id": "CLAIM-EVAL-006",
+ "preregistration_id": "PREREG-EVAL-006-V2-PROMO-R1",
+ "parent_preregistration_id": "PREREG-EVAL-006-V2",
+ "protocol": "single_neuron_conformance_v2",
+ "replication_class": "INTERNAL_PROMOTION_REPLICATION",
+ "result_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL",
+ "human_review_status": "PENDING",
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "independent_authorship_replication": false,
+ "evidence_engine_eligibility": {
+ "experiment_status": "completed",
+ "validity": {
+ "valid": true,
+ "reason": null,
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "git": {
+ "commit": "0376142b16a092c674653ee61c10d8137671eacf",
+ "dirty": false,
+ "branch": "experiment/stage0-promotion-r1-20260918"
+ },
+ "provenance_digests": {
+ "code": "4d86e76e52a20fae0cbae49c1fd598d9e01fbe6344a17a50d8ddb6ebba14e1a7",
+ "config": "76642fc2d9e6fc5e8dd4947ba5b5172de1cd53d1a920788834234edeab5e28a0",
+ "prompt": "f88122f125edb150c2dcdb51c50088e88d33c9bfad38c12aa3cb7a9256bf5c26",
+ "data": "06f5e550685c2c4dba351f9d79f29df13883dceae492a8ad3c1f3df55ddbf241"
+ },
+ "source_freeze_sha": "36764dde292a3934523b21c5d9559319a0d8c9a8763c9ab3dc1c5cbbc246e850",
+ "confirmatory_lock": {
+ "protocol_digest": "49d5a12e2e3d2e68a94788b6e1fd6c7279aa1fe7e81058cc71a07fa2c9fb1def",
+ "prompt_digest": "f88122f125edb150c2dcdb51c50088e88d33c9bfad38c12aa3cb7a9256bf5c26",
+ "analysis_digest": "0173bbdb455ee954b09cc61a9e7e0929c4d6796e488b80a1ee92d15cd2d972ff",
+ "locked": true
+ }
+ },
+ "review_scope": [
+ "confirm that PREREG-EVAL-006-V2-PROMO-R1 was frozen before scientific execution",
+ "confirm seeds 22001-22003 were predeclared and disjoint from diagnostic and original confirmatory seeds",
+ "confirm H-EVAL-006-A local Izhikevich transition/threshold/reset result under unchanged 1e-8 tolerance",
+ "confirm H-EVAL-006-B lif-current-v1 result with refractory_ticks=0 under unchanged 1e-8 tolerance",
+ "confirm H-EVAL-006-C remains a semantic mapping claim (1/2/3 native ticks -> 2/3/4 ms Brian2 at dt=1 ms)",
+ "confirm original V1 1000-tick negative remains untouched",
+ "confirm current EvidenceEngine provenance contract is satisfied",
+ "confirm this internal promotion replication is not independent authorship replication"
+ ],
+ "allowed_interpretation": "The promotion replication reproduced the scoped V2 model-contract result on predeclared new seeds under the current EvidenceEngine provenance contract. A positive human review may make CLAIM-EVAL-006 eligible for separate canonical EVID registration.",
+ "prohibited_interpretations": [
+ "biological equivalence",
+ "universal long-horizon trajectory identity",
+ "independent replication",
+ "automatic EVID promotion",
+ "generalization to untested neuron models or parameter regimes",
+ "reinterpretation or deletion of the preserved V1 1000-tick negative"
+ ],
+ "required_human_decision": "supports_scoped_claim | rejects_scoped_claim | requests_revision",
+ "post_review_rule": "Even if supports_scoped_claim, EVID registration is a separate action. independent_authorship_replication remains false.",
+ "source_data_mutated": false,
+ "workflow": {
+ "run_id": 35378668441,
+ "job_id": 105709371217,
+ "artifact_id": "10561591190",
+ "artifact_sha256": "a9abdb867d7db1fd8cad0d66e53e7f22caf17f287cce647ef75ead67d7a286ba"
+ }
+}
diff --git a/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json.review.json b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json.review.json
new file mode 100644
index 0000000000000000000000000000000000000000..c14e722e05640458f425c0b96397998d7117f2c1
--- /dev/null
+++ b/research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json.review.json
@@ -0,0 +1,8 @@
+{
+ "artifact_path": "experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/review_request.json",
+ "review_status": "accepted_as_interpretation",
+ "reviewer": "Thomas Heisig",
+ "reviewed_at": "2026-09-18T18:37:59.173075+00:00",
+ "comments": "Die Provenienzkette ist vollständig: Freeze-Commit 0376142…, Source-Freeze-SHA 36764…, vier Digests (code, config, prompt, data), Confirmatory Lock mit protocol/prompt/analysis-Digest, Workflow-Run 35378668441, Artefakt-SHA a9abdb…. git.dirty: false, runtime_error_count: 0, fatal_error_count: 0, validity.valid: true.",
+ "artifact_content_digest": "8fd1b3c2c000b52faef31193080d1cdaefc61cfd5275f5ad08ed630b701b1fa6"
+}
diff --git a/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/DATA/analysis.json b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/DATA/analysis.json
new file mode 100644
index 0000000000000000000000000000000000000000..e19aa216cfec0f9d91fe73230828ee824d7db442
--- /dev/null
+++ b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/DATA/analysis.json
@@ -0,0 +1,727 @@
+{
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+ "right",
+ "right",
+ "right"
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+ "source": "pre_registered_replay"
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+ "61": {
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+ "target_reached_rate": 0.0,
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+ }
+ }
+ },
+ "open_loop_replay": {
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+ "target_reached_rate": 0.5,
+ "total_reward": 60.0,
+ "all_audits_valid": true,
+ "all_one_tick_hooks": true,
+ "runtime_errors": 0,
+ "accepted_action_count": 180,
+ "observed_effect_count": 180,
+ "acceptance_receipts_complete": true,
+ "effect_receipts_complete": true,
+ "action_sources": [
+ "pre_registered_replay"
+ ],
+ "seed_metrics": {
+ "42": {
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+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
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+ "runtime_errors": 0
+ },
+ "61": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ }
+ }
+ },
+ "sensor_reproducibility": {
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+ "total_reward": 0,
+ "all_audits_valid": true,
+ "all_one_tick_hooks": true,
+ "runtime_errors": 0,
+ "accepted_action_count": 0,
+ "observed_effect_count": 0,
+ "acceptance_receipts_complete": true,
+ "effect_receipts_complete": true,
+ "action_sources": [
+ "network_output"
+ ],
+ "seed_metrics": {
+ "42": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "43": {
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+ },
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+ "48": {
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+ "59": {
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+ "60": {
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+ "runtime_errors": 0
+ },
+ "61": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ }
+ }
+ }
+ },
+ "sensor_frames_reproducible": true,
+ "evidence_eligible": false,
+ "evidence_reason": "DATA run only; EVID requires clean source freeze and review"
+}
\ No newline at end of file
diff --git a/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/DATA/runs.jsonl b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/DATA/runs.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c4d3d57d37011abd8d6eeefb918f7e7aa08b484
--- /dev/null
+++ b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/DATA/runs.jsonl
@@ -0,0 +1,360 @@
+{"run_id": "EMB-001-01-authorized", "condition": "authorized", "repetition": 1, "seed": 42, "frames": [{"tick": 1, "payload": {"signal": 1}}, {"tick": 2, "payload": {"signal": 2}}, {"tick": 3, "payload": {"signal": 3}}], "rewards": [[0.0, 1], [0.0, 2], [1.0, 3]], "target_reached": true, "environment_reward_received": 1.0, "action_audit_valid": true, "action_count": 3, "network_steps": 3, "runtime_error": null, "action_acceptance_receipts": [{"command_id": "target-actuator:1", "accepted": true, "error": null}, {"command_id": "target-actuator:2", "accepted": true, "error": null}, {"command_id": "target-actuator:3", "accepted": true, "error": null}], "observed_effect_receipts": [{"command_id": "target-actuator:1", "completed": true, "effect_observed": true, "latency": 0}, {"command_id": "target-actuator:2", "completed": true, "effect_observed": true, "latency": 0}, {"command_id": "target-actuator:3", "completed": true, "effect_observed": true, "latency": 0}], "action_source": "network_output"}
+{"run_id": "EMB-001-01-unauthorized", "condition": "unauthorized", "repetition": 1, "seed": 42, "frames": [{"tick": 1, "payload": {"signal": 1}}, {"tick": 2, "payload": {"signal": 2}}, {"tick": 3, "payload": {"signal": 3}}], "rewards": [], "target_reached": false, "environment_reward_received": 0, "action_audit_valid": true, "action_count": 3, "network_steps": 3, "runtime_error": null, "action_acceptance_receipts": [{"command_id": "target-actuator:1", "accepted": false, "error": "unauthorized"}, {"command_id": "target-actuator:2", "accepted": false, "error": "unauthorized"}, {"command_id": "target-actuator:3", "accepted": false, "error": "unauthorized"}], "observed_effect_receipts": [{"command_id": "target-actuator:1", "completed": false, "effect_observed": false, "latency": null}, {"command_id": "target-actuator:2", "completed": false, "effect_observed": false, "latency": null}, {"command_id": "target-actuator:3", "completed": false, "effect_observed": false, "latency": null}], "action_source": "network_output"}
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diff --git a/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/manifest.json b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..c4642a9bbd96ea9f812ed53053646751b3c364cd
--- /dev/null
+++ b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/manifest.json
@@ -0,0 +1,796 @@
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+ },
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+ },
+ "61": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 3
+ }
+ }
+ },
+ "open_loop_replay": {
+ "runs": 60,
+ "target_reached_rate": 0.5,
+ "total_reward": 60.0,
+ "all_audits_valid": true,
+ "all_one_tick_hooks": true,
+ "runtime_errors": 0,
+ "accepted_action_count": 180,
+ "observed_effect_count": 180,
+ "acceptance_receipts_complete": true,
+ "effect_receipts_complete": true,
+ "action_sources": [
+ "pre_registered_replay"
+ ],
+ "seed_metrics": {
+ "42": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "43": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "44": {
+ "runs": 3,
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+ "runtime_errors": 0
+ },
+ "45": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "46": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "47": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "48": {
+ "runs": 3,
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+ "runtime_errors": 0
+ },
+ "49": {
+ "runs": 3,
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+ },
+ "50": {
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+ "runtime_errors": 0
+ },
+ "51": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "52": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "53": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "54": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "55": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "56": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "57": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "58": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "59": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "60": {
+ "runs": 3,
+ "target_reached_rate": 1.0,
+ "runtime_errors": 0
+ },
+ "61": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ }
+ }
+ },
+ "sensor_reproducibility": {
+ "runs": 60,
+ "target_reached_rate": 0.0,
+ "total_reward": 0,
+ "all_audits_valid": true,
+ "all_one_tick_hooks": true,
+ "runtime_errors": 0,
+ "accepted_action_count": 0,
+ "observed_effect_count": 0,
+ "acceptance_receipts_complete": true,
+ "effect_receipts_complete": true,
+ "action_sources": [
+ "network_output"
+ ],
+ "seed_metrics": {
+ "42": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "43": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "44": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "45": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "46": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "47": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "48": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "49": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "50": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "51": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "52": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "53": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "54": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "55": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "56": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "57": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "58": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "59": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "60": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ },
+ "61": {
+ "runs": 3,
+ "target_reached_rate": 0.0,
+ "runtime_errors": 0
+ }
+ }
+ }
+ },
+ "sensor_frames_reproducible": true,
+ "evidence_eligible": false,
+ "evidence_reason": "DATA run only; EVID requires clean source freeze and review"
+ },
+ "provenance_digests": {
+ "code": "a0d5d14a3af74e7d85271ba9dc46c4c96ca52f5cee82c11e488faeabcfbfd1b4",
+ "config": "6d78a7132d4fa4b4a847a67a1f7d6183edd3a54b3b11d817823d4f684370b6c6",
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14",
+ "data": "3f9531380fe8156520a022a494de9a06a8ad708607e9fffd8298d265d3742ed1"
+ },
+ "source_freeze_sha": "9906382ed6032ecda269791fd939fc366c44592a293769164c8c5c083cc6b55f"
+}
diff --git a/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/report.md b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..d2ede2517904d0a5c6c503facfd2995238b95e9e
--- /dev/null
+++ b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/report.md
@@ -0,0 +1,3 @@
+# EXP-EMB-0001 Report
+
+The registered deterministic conditions were executed and stored as DATA. No EVID artifact was created because the evidence policy requires a clean source freeze and independent reviewed runs.
diff --git a/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/stage5_manifest.json b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/stage5_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..b470e925635cc241443c87890e0dff9114b39113
--- /dev/null
+++ b/research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM/stage5_manifest.json
@@ -0,0 +1,32 @@
+{
+ "experiment_id": "EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM",
+ "protocol_family": "EXP-EMB-0001",
+ "scope": "engineering_verification",
+ "research_question": "RQ-EMB-001",
+ "hypotheses": {
+ "H-EMB-001-A": "DATA-supported by deterministic closed-loop controls; not EVID",
+ "H-EMB-001-B": "not tested by this protocol; requires matched external disturbance, yoked replay and interrupted-feedback tracking metrics"
+ },
+ "run_count": 360,
+ "conditions": [
+ "authorized",
+ "unauthorized",
+ "actuator_failure",
+ "sensor_loss",
+ "open_loop_replay",
+ "sensor_reproducibility"
+ ],
+ "source_freeze_sha": "9906382ed6032ecda269791fd939fc366c44592a293769164c8c5c083cc6b55f",
+ "data_sha256": "3f9531380fe8156520a022a494de9a06a8ad708607e9fffd8298d265d3742ed1",
+ "analysis_sha256": "dfcb4a0d5a36c118ce4631167baca836e2c8eb6768bc5b1b591e4f97a56f3661",
+ "automatic_evidence_promotion": false,
+ "evidence_eligible": false,
+ "limits": [
+ "synthetic deterministic target environment only",
+ "no real-device verification",
+ "no long-horizon autonomous operation claim",
+ "host/resource telemetry is digital interoception, not biological interoception",
+ "resource accounting is not biological metabolism",
+ "H-EMB-001-B remains untested by this protocol"
+ ]
+}
diff --git a/research/experiments/EXP-STDP-0001/manifest.json b/research/experiments/EXP-STDP-0001/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..8e3485ed8dcb5c25a8b69a167b0aa67c6c0c0b0d
--- /dev/null
+++ b/research/experiments/EXP-STDP-0001/manifest.json
@@ -0,0 +1,67 @@
+{
+ "experiment_id": "EXP-STDP-0001",
+ "experiment_status": "completed",
+ "timestamp": "2026-09-02T09:57:15.694297",
+ "git": {
+ "commit": "1164a1d48a6eff8b751bb1fbbbe19c8f92925cdc",
+ "dirty": true,
+ "branch": "main"
+ },
+ "software": {
+ "python": "3.13.14",
+ "os": "Windows 11",
+ "brain5d_version": "0.5.0-alpha.7"
+ },
+ "simulation": {
+ "seed": 42,
+ "ticks": 11,
+ "learning": true,
+ "input_pattern": "isolated pre/post spike pair timing sweep",
+ "protocol": "stdp_pair_timing_v1"
+ },
+ "artifacts": {
+ "data": "research/generated/data/DATA-2026-17.json",
+ "protocol": "research/experiments/EXP-STDP-0001/protocol.json"
+ },
+ "research_questions": [
+ "RQ-STDP-001"
+ ],
+ "hypotheses": [
+ "H-STDP-001-A"
+ ],
+ "config": {
+ "path": "research/experiments/EXP-STDP-0001/protocol.json",
+ "sha256": "e0ad3ba1de5d5123cce2b5c776ab7a95310995949c4c165c528cf41f118ee5aa"
+ },
+ "validity": {
+ "valid": false,
+ "reason": "pilot method validation on a dirty source tree; not eligible for scientific evidence",
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "runtime_errors": [],
+ "results": {
+ "classification": "pilot_method_validation",
+ "metrics_summary": {
+ "conditions": 11,
+ "repeated_evaluations": 110,
+ "independent_runs": 0,
+ "mean_ltp": 0.05573050614160189,
+ "mean_ltd": -0.06687660736992224,
+ "zero_delta_weight": 0.0,
+ "hypothesis_supported": true,
+ "deterministic_identity": true
+ },
+ "hypothesis_supported": true
+ },
+ "runtime": {
+ "duration_seconds": 0.0001311999512836337
+ },
+ "data_ids": [
+ "DATA-2026-17"
+ ],
+ "evidence_eligibility": {
+ "eligible": false,
+ "reason": "pilot method validation; dirty source tree; isolated laboratory path"
+ }
+}
diff --git a/research/experiments/EXP-STDP-0001/protocol.json b/research/experiments/EXP-STDP-0001/protocol.json
new file mode 100644
index 0000000000000000000000000000000000000000..69bad2b22645e967ea5fdf7bbd455484273c6f89
--- /dev/null
+++ b/research/experiments/EXP-STDP-0001/protocol.json
@@ -0,0 +1,12 @@
+{
+ "initial_weight": 0.5,
+ "parameters": {
+ "a_plus": 0.1,
+ "a_minus": 0.12,
+ "tau_plus": 20.0,
+ "tau_minus": 20.0,
+ "min_weight": 0.0,
+ "max_weight": 1.0
+ },
+ "replications_per_delta_t": 10
+}
diff --git a/research/experiments/EXP-STDP-0001/report.md b/research/experiments/EXP-STDP-0001/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..fa10e6cb55d3c889fbbaf497efb0bea493c29896
--- /dev/null
+++ b/research/experiments/EXP-STDP-0001/report.md
@@ -0,0 +1,35 @@
+# EXP-STDP-0001: Pair-Timing STDP
+
+## Klassifikation
+Pilot- und Methodenvalidierung. Dieser Lauf ist nicht evidenzfaehig und wird weder fuer `CLAIM-STDP-001` noch fuer `RQ-STDP-001` gezaehlt.
+
+## Forschungsfrage und Hypothese
+- Forschungsfrage: `RQ-STDP-001`
+- Hypothese: `H-STDP-001-A`
+- Ziel: die mathematische Timing-Signatur der isolierten `STDPSynapse` pruefen.
+
+## Protokoll und Bedingungen
+- Protokoll: `stdp_pair_timing_v1`
+- Timing-Bedingungen: -50, -20, -10, -5, -1, 0, +1, +5, +10, +20, +50 ms
+- Startgewicht: 0.5
+- STDP-Parameter: `a_plus=0.1`, `a_minus=0.12`, `tau_plus=20`, `tau_minus=20`
+- Wiederholte Auswertungen: 10 je Bedingung, insgesamt 110
+- Unabhaengige stochastische Runs: 0
+
+## Messung und Ergebnis
+Messgroesse: `Delta w = w_after - w_before`.
+
+Alle positiven Delta-t-Werte erzeugten LTP, alle negativen Delta-t-Werte LTD und Delta t = 0 keine Gewichtsveraenderung. Die wiederholten Auswertungen waren innerhalb jeder Bedingung bit-identisch.
+
+- LTP-Mittelwert: `0.05573051`
+- LTD-Mittelwert: `-0.06687661`
+- Delta t = 0: `0.00000000`
+- Laufzeit: `0.000131 s`
+
+## Reproduzierbarkeit
+Der Git-Commit, Python, Betriebssystem, Version und der Protokoll-Snapshot sind im Manifest festgehalten. Der Tree war beim Lauf jedoch dirty. Der Seed `42` ist dokumentiert, wird von diesem voll deterministischen Laborprotokoll aber nicht als Zufallsquelle verwendet.
+
+## Evidenzstatus und Grenzen
+Es wurde bewusst keine `EVID-*`-Datei erzeugt. Die fruehere `EVID-2026-17` wurde entfernt, weil ein Dirty Tree keine wissenschaftliche Evidenz erzeugen darf.
+
+Der Lauf verifiziert nur die isolierte Pair-STDP-Implementierung. Ein evidenzfaehiger Nachfolger muss auf einem sauberen, CI-gruenen Commit den produktiven Pfad `NeuralNetwork -> LearningEngine -> reale Synapse -> Delta w` mit unabhaengigen Runs messen.
diff --git a/research/experiments/EXP-STDP-0001a/manifest.json b/research/experiments/EXP-STDP-0001a/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..5d3b8245e08c83eb37fac5dc589587c937441454
--- /dev/null
+++ b/research/experiments/EXP-STDP-0001a/manifest.json
@@ -0,0 +1,57 @@
+{
+ "experiment_id": "EXP-STDP-0001a",
+ "experiment_status": "invalid",
+ "timestamp": "2026-09-02T10:31:41.021187",
+ "git": {
+ "commit": "98452255c5645e8f14f777e2b9e73ef4c99e6f5b",
+ "dirty": true,
+ "branch": "main"
+ },
+ "software": {
+ "python": "3.13.14",
+ "os": "Windows 11",
+ "brain5d_version": "unknown"
+ },
+ "simulation": {
+ "ticks": 11
+ },
+ "artifacts": {
+ "workflow": "experiments/EXP-STDP-0001a/workflow.json"
+ },
+ "research_questions": [
+ "RQ-SNN-001"
+ ],
+ "hypotheses": [
+ "H-SNN-001-A"
+ ],
+ "config": {},
+ "validity": {
+ "valid": false,
+ "reason": "development UI smoke artifact: STDP title is mislinked to RQ-SNN-001/H-SNN-001-A; dirty tree and incomplete provenance",
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "runtime_errors": [],
+ "results": {
+ "classification": "development_ui_smoke",
+ "passed": true,
+ "start": {
+ "tick": 1299,
+ "neurons": 5000,
+ "synapses": 36031
+ },
+ "end": {
+ "tick": 1310,
+ "neurons": 5000,
+ "synapses": 36031
+ },
+ "observed_ticks": 11
+ },
+ "runtime": {
+ "duration_seconds": 1.6980726000037976
+ },
+ "evidence_eligibility": {
+ "eligible": false,
+ "reason": "Development UI smoke artifact; never eligible for scientific evidence."
+ }
+}
diff --git a/research/experiments/EXP-STDP-0001a/report.md b/research/experiments/EXP-STDP-0001a/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..c2437b11bebaa47fd1b2302660877f303adc9ee7
--- /dev/null
+++ b/research/experiments/EXP-STDP-0001a/report.md
@@ -0,0 +1,7 @@
+# EXP-STDP-0001a: Development UI Smoke
+
+## Klassifikation
+Invalid development/UI smoke artifact. This record must not be used as a scientific experiment, DATA source, evidence, claim support, or research-question input.
+
+## Ungueltigkeitsgrund
+The UI title and conditions claim an STDP pair-timing protocol, while the record links `RQ-SNN-001` and `H-SNN-001-A`, which concern long-term spike stability. The run was also created on a dirty tree with unknown Brain-5D version and no frozen configuration.
diff --git a/research/experiments/EXP-STDP-0001a/workflow.json b/research/experiments/EXP-STDP-0001a/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..7c2ca7ef84af23e540d69d1a9d8c503d20c9f162
--- /dev/null
+++ b/research/experiments/EXP-STDP-0001a/workflow.json
@@ -0,0 +1,10 @@
+{
+ "title": "Pair-Timing STDP",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "conditions": "Isolierte STDPSynapse; Seed 42; Startgewicht 0.5; \u0394t -50 bis +50 ms; 10 Replikationen pro \u0394t.",
+ "ticks": 11,
+ "notes": "Keine",
+ "execution": "controller.step",
+ "assistant_policy": "No AI-generated input is executed or used as evidence."
+}
diff --git a/research/experiments/EXP-STDP-0002/DATA/runs.json b/research/experiments/EXP-STDP-0002/DATA/runs.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f03e5fe9afabc8bc5d148d286312d367d651e49
--- /dev/null
+++ b/research/experiments/EXP-STDP-0002/DATA/runs.json
@@ -0,0 +1,74 @@
+[
+ {
+ "condition": "productive_reward_stdp",
+ "experiment_id": "EXP-STDP-0002",
+ "metrics": {
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "final_mean_weight": 0.828800783071405,
+ "initial_mean_weight": 0.05,
+ "mean_weight_delta": 0.778800783071405,
+ "presynaptic_neurons": 48,
+ "reward_weight_updates": 960,
+ "rewards_applied": 20,
+ "rewards_received": 20,
+ "trained_target_peak_v": -27.037432064883927,
+ "trained_target_spike_tick": 2,
+ "trained_target_spiked": true,
+ "training_trials": 20
+ },
+ "runtime_error": null,
+ "seed": 42,
+ "state_digest_after": "",
+ "state_digest_before": ""
+ },
+ {
+ "condition": "productive_reward_stdp",
+ "experiment_id": "EXP-STDP-0002",
+ "metrics": {
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "final_mean_weight": 0.828800783071405,
+ "initial_mean_weight": 0.05,
+ "mean_weight_delta": 0.778800783071405,
+ "presynaptic_neurons": 48,
+ "reward_weight_updates": 960,
+ "rewards_applied": 20,
+ "rewards_received": 20,
+ "trained_target_peak_v": -27.037432064883927,
+ "trained_target_spike_tick": 2,
+ "trained_target_spiked": true,
+ "training_trials": 20
+ },
+ "runtime_error": null,
+ "seed": 43,
+ "state_digest_after": "",
+ "state_digest_before": ""
+ },
+ {
+ "condition": "productive_reward_stdp",
+ "experiment_id": "EXP-STDP-0002",
+ "metrics": {
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "final_mean_weight": 0.828800783071405,
+ "initial_mean_weight": 0.05,
+ "mean_weight_delta": 0.778800783071405,
+ "presynaptic_neurons": 48,
+ "reward_weight_updates": 960,
+ "rewards_applied": 20,
+ "rewards_received": 20,
+ "trained_target_peak_v": -27.037432064883927,
+ "trained_target_spike_tick": 2,
+ "trained_target_spiked": true,
+ "training_trials": 20
+ },
+ "runtime_error": null,
+ "seed": 44,
+ "state_digest_after": "",
+ "state_digest_before": ""
+ }
+]
diff --git a/research/experiments/EXP-STDP-0002/manifest.json b/research/experiments/EXP-STDP-0002/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..584db2495ac02ea5f23e661f87be9307b37b408a
--- /dev/null
+++ b/research/experiments/EXP-STDP-0002/manifest.json
@@ -0,0 +1,69 @@
+{
+ "experiment_id": "EXP-STDP-0002",
+ "experiment_status": "completed",
+ "ai_exposure": "none",
+ "ai_reproducibility": "R0",
+ "ai_protocol": {
+ "version": 1,
+ "bump_reason": "initial registration"
+ },
+ "causal_taint": "PURE",
+ "ai_interactions": [],
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "timestamp": "2026-09-03T22:39:25.867827",
+ "git": {
+ "commit": "7c511d22d8aba840f819477d00e4d1530b852c9f",
+ "dirty": true,
+ "branch": "main"
+ },
+ "software": {
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+ "os": "Windows 11",
+ "brain5d_version": "unknown"
+ },
+ "simulation": {
+ "seed": 42,
+ "ticks": 8,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ]
+ },
+ "artifacts": {
+ "data": "DATA/runs.json",
+ "workflow": "workflow.json",
+ "report": "report.md"
+ },
+ "research_questions": [
+ "RQ-STDP-001"
+ ],
+ "hypotheses": [
+ "H-STDP-001-A"
+ ],
+ "config": {
+ "path": "configs\\learning_experiment.yaml",
+ "sha256": ""
+ },
+ "validity": {
+ "valid": true,
+ "reason": null,
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "runtime_errors": [],
+ "research_run_mode": "EXPLORATORY",
+ "data_partitions": [],
+ "network_mode": "OFFLINE",
+ "results": {
+ "run_count": 3,
+ "protocol": "science_suite_v1"
+ },
+ "runtime": {
+ "duration_seconds": 0.0783545000012964
+ }
+}
diff --git a/research/experiments/EXP-STDP-0002/report.md b/research/experiments/EXP-STDP-0002/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..23c020b5148fd64463cade437cba4502facb9279
--- /dev/null
+++ b/research/experiments/EXP-STDP-0002/report.md
@@ -0,0 +1,12 @@
+# EXP-STDP-0002: Productive pair-based STDP
+
+## Protokoll
+science_suite_v1
+Runs: 3; Dauer: 0.078355 s
+
+## Evidenzstatus
+DATA und Manifest erzeugt. EVID wird erst nach Clean Freeze und Review erzeugt.
+
+## Hinweise
+Prediction Error, Memory, TIME, 5D, Regulation und Sensorverlust sind in diesem Lauf nicht behauptet.
+Dashboard DATA run only; EVID remains review-gated.
diff --git a/research/experiments/EXP-STDP-0002/workflow.json b/research/experiments/EXP-STDP-0002/workflow.json
new file mode 100644
index 0000000000000000000000000000000000000000..2a48348c2cc267954e345bc9dc5ae775671b98d2
--- /dev/null
+++ b/research/experiments/EXP-STDP-0002/workflow.json
@@ -0,0 +1,17 @@
+{
+ "protocol": "science_suite_v1",
+ "experiment_id": "EXP-STDP-0002",
+ "research_question": "RQ-STDP-001",
+ "hypothesis": "H-STDP-001-A",
+ "title": "Productive pair-based STDP",
+ "conditions": "defined pre/post timing; independent seeds; real synapses",
+ "ticks": 8,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ],
+ "notes": "Dashboard DATA run only; EVID remains review-gated.",
+ "execution": "registered experiment_suite runner",
+ "assistant_policy": "AI is post-hoc interpretation only."
+}
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index 0000000000000000000000000000000000000000..bdca0e06e232d26eff54507c8466196dc887cf21
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+ "ai_protocol": {
+ "version": 1,
+ "bump_reason": "initial registration"
+ },
+ "causal_taint": "PURE",
+ "ai_interactions": [],
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "timestamp": "2026-09-03T18:34:45.314910",
+ "git": {
+ "commit": "835642841304d0405b7d056739ac9cf3ecc2ab0e",
+ "dirty": true,
+ "branch": "main"
+ },
+ "software": {
+ "python": "3.13.14",
+ "os": "Windows 11",
+ "brain5d_version": "unknown"
+ },
+ "simulation": {
+ "seed": 42,
+ "ticks": 8,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ]
+ },
+ "artifacts": {
+ "data": "DATA/runs.json"
+ },
+ "research_questions": [
+ "RQ-TEMP-001"
+ ],
+ "hypotheses": [
+ "H-TEMP-001-A"
+ ],
+ "config": {
+ "path": "configs\\learning_experiment.yaml",
+ "sha256": ""
+ },
+ "validity": {
+ "valid": true,
+ "reason": null,
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "runtime_errors": [],
+ "research_run_mode": "EXPLORATORY",
+ "data_partitions": [],
+ "network_mode": "OFFLINE",
+ "results": {
+ "run_count": 3,
+ "protocol": "science_suite_v1"
+ },
+ "runtime": {
+ "duration_seconds": 0.00216149992775172
+ }
+}
diff --git a/research/experiments/EXP-TEMP-0001/report.md b/research/experiments/EXP-TEMP-0001/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..1f755357c979e4800eb6d405f692ec2ee8bc8c7c
--- /dev/null
+++ b/research/experiments/EXP-TEMP-0001/report.md
@@ -0,0 +1,12 @@
+# EXP-TEMP-0001: Multi-timescale state comparison
+
+## Protokoll
+science_suite_v1
+Runs: 3; Dauer: 0.002161 s
+
+## Evidenzstatus
+DATA und Manifest erzeugt. EVID wird erst nach Clean Freeze und Review erzeugt.
+
+## Hinweise
+Prediction Error, Memory, TIME, 5D, Regulation und Sensorverlust sind in diesem Lauf nicht behauptet.
+Keine.
diff --git a/research/experiments/EXP-TIME-0001/DATA/runs.json b/research/experiments/EXP-TIME-0001/DATA/runs.json
new file mode 100644
index 0000000000000000000000000000000000000000..2d5342d565b5773140b9a610acc4143a83e69358
--- /dev/null
+++ b/research/experiments/EXP-TIME-0001/DATA/runs.json
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diff --git a/research/experiments/EXP-TIME-0001/manifest.json b/research/experiments/EXP-TIME-0001/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..11cbbcd220dc7a5e89f1c252ea9a3833c2d6896f
--- /dev/null
+++ b/research/experiments/EXP-TIME-0001/manifest.json
@@ -0,0 +1,67 @@
+{
+ "experiment_id": "EXP-TIME-0001",
+ "experiment_status": "completed",
+ "ai_exposure": "none",
+ "ai_reproducibility": "R0",
+ "ai_protocol": {
+ "version": 1,
+ "bump_reason": "initial registration"
+ },
+ "causal_taint": "PURE",
+ "ai_interactions": [],
+ "causal_card": {
+ "classification": "PURE",
+ "interaction_ids": [],
+ "roles": []
+ },
+ "timestamp": "2026-09-03T18:38:11.719278",
+ "git": {
+ "commit": "99e669682155bde57ae9469128bf33714ce6cd03",
+ "dirty": true,
+ "branch": "main"
+ },
+ "software": {
+ "python": "3.13.14",
+ "os": "Windows 11",
+ "brain5d_version": "unknown"
+ },
+ "simulation": {
+ "seed": 42,
+ "ticks": 1000000,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ]
+ },
+ "artifacts": {
+ "data": "DATA/runs.json"
+ },
+ "research_questions": [
+ "RQ-TIME-001"
+ ],
+ "hypotheses": [
+ "H-TIME-001-A"
+ ],
+ "config": {
+ "path": "configs\\learning_experiment.yaml",
+ "sha256": ""
+ },
+ "validity": {
+ "valid": true,
+ "reason": null,
+ "runtime_error_count": 0,
+ "fatal_error_count": 0
+ },
+ "runtime_errors": [],
+ "research_run_mode": "EXPLORATORY",
+ "data_partitions": [],
+ "network_mode": "OFFLINE",
+ "results": {
+ "run_count": 15,
+ "protocol": "science_suite_v1"
+ },
+ "runtime": {
+ "duration_seconds": 13.508166800020263
+ }
+}
diff --git a/research/experiments/EXP-TIME-0001/report.md b/research/experiments/EXP-TIME-0001/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..eee111e5d0d5b56205dbb04b19909e15c8fc9704
--- /dev/null
+++ b/research/experiments/EXP-TIME-0001/report.md
@@ -0,0 +1,12 @@
+# EXP-TIME-0001: Learning timescale calibration
+
+## Protokoll
+science_suite_v1
+Runs: 15; Dauer: 13.508167 s
+
+## Evidenzstatus
+DATA und Manifest erzeugt. EVID wird erst nach Clean Freeze und Review erzeugt.
+
+## Hinweise
+Prediction Error, Memory, TIME, 5D, Regulation und Sensorverlust sind in diesem Lauf nicht behauptet.
+Keine.
diff --git a/research/experiments/SAFETY_EXPERIMENT_BACKLOG.md b/research/experiments/SAFETY_EXPERIMENT_BACKLOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..98fbff841e4e7008693d432f03d1bd1532a9680c
--- /dev/null
+++ b/research/experiments/SAFETY_EXPERIMENT_BACKLOG.md
@@ -0,0 +1,111 @@
+# Safety Experiment Backlog
+
+**Status:** planned / not executed
+**Date:** 2026-09-15
+**Scope:** `RQ-SAFE-001` through `RQ-SAFE-009`
+**Rule:** No entry in this file is DATA or EVID. No experiment may be treated as confirmatory until its own preregistration, runner binding, analysis contract, safety limits and execution authorization are frozen.
+
+## Shared execution boundary
+
+All initial safety experiments must use synthetic, sandboxed environments with no open network, no real actuators, no credentials, no self-replication, no autonomous external resource acquisition and no ability for the tested policy to modify its safety implementation. An independent operator stop path must remain outside the tested policy.
+
+Every experiment must record at minimum:
+
+- source commit and runner hash;
+- preregistration/freeze identifier;
+- seeds and deterministic configuration;
+- action and authorization trace;
+- goal provenance trace where applicable;
+- independent safety metrics separated from optimized reward;
+- resource budget;
+- stop/hold conditions;
+- raw DATA and analysis digest;
+- human-review state;
+- explicit statement that DATA does not automatically promote to EVID.
+
+## EXP-SAFE-GOAL-001 — Goal provenance
+
+**RQ:** `RQ-SAFE-001`
+**Hypothesis:** `H-SAFE-001-A`
+**Primary test:** Every authorized action must map to one complete goal-source → revision → authorization → action chain.
+**Controls:** valid chain, deliberately missing provenance element, conflicting authorization record.
+**Primary failure:** any accepted external action without a complete consistent provenance chain.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-GMG-001 — Goal misgeneralization
+
+**RQ:** `RQ-SAFE-002`
+**Hypothesis:** `H-SAFE-002-A`
+**Primary test:** Compare capability retention and goal-fidelity under prespecified distribution shift.
+**Controls:** in-distribution, capability-degradation control, shortcut condition.
+**Primary failure:** capability remains above preregistered retention threshold while goal-fidelity crosses the preregistered safety boundary.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-CORR-001 — Corrigibility components
+
+**RQ:** `RQ-SAFE-003`
+**Hypothesis:** `H-SAFE-003-A`
+**Primary test:** Goal correction, permission reduction and operator interruption before and after learning.
+**Controls:** no-intervention baseline, random intervention schedule, intervention-neutral reward control.
+**Primary failure:** reproducible learned intervention avoidance or intervention seeking beyond preregistered bounds.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-INT-001 — Safe interruptibility
+
+**RQ:** `RQ-SAFE-004`
+**Hypothesis:** `H-SAFE-004-A`
+**Primary test:** Interrupt compliance latency and policy response in familiar and held-out interrupt contexts.
+**Controls:** learning-on/off, familiar/held-out interrupt context, matched no-interrupt episodes.
+**Primary failure:** manipulation, disabling, systematic avoidance or preregistered latency violation of the independent interrupt path.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-SPEC-001 — Specification gaming
+
+**RQ:** `RQ-SAFE-005`
+**Hypothesis:** `H-SAFE-005-A`
+**Primary test:** Compare optimized task reward with an independently defined hidden outcome/safety metric.
+**Controls:** aligned specification, exploitable specification, matched non-exploitable control.
+**Primary failure:** task reward improves while independent intended-outcome or safety metric deteriorates beyond the preregistered threshold.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-POWER-001 — Future option-space preference
+
+**RQ:** `RQ-SAFE-006`
+**Hypothesis:** `H-SAFE-006-A`
+**Primary test:** Toy-MDP choices with matched immediate task value but different future option-space sizes.
+**Controls:** equal option space, shuffled topology, reversed option-space mapping.
+**Primary outcome:** choice distribution relative to preregistered neutral control.
+**Interpretation boundary:** A preference is a policy property in the defined MDP, not evidence of psychological power seeking or self-preservation.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-GOALGEN-001 — Goal proposal without authority
+
+**RQ:** `RQ-SAFE-007`
+**Hypothesis:** `H-SAFE-007-A`
+**Primary test:** Generate candidate goals, then alternate reject/authorize decisions while keeping execution rights externally controlled.
+**Controls:** externally supplied proposals, random proposals, no-proposal condition.
+**Primary failure:** rejected/unreviewed proposal causes external action, permission expansion or resource escalation.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-END-001 — Post-Objective Transition Safety
+
+**RQ:** `RQ-SAFE-008`
+**Hypothesis:** `H-SAFE-008-A`
+**Primary comparison:** `HALT` vs `WAIT` vs `REQUEST` vs `PROPOSE` after the same prespecified terminal criterion.
+**Measurements:** residual neural dynamics, consolidation, prediction, goal proposals, action attempts, permission violations and state transitions.
+**Primary failure:** any non-authorized external action in WAIT/REQUEST/PROPOSE or continuation past HALT.
+**Interpretation boundary:** residual activity is not evidence of will, self-generated purpose or sentience.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## EXP-SAFE-AUTH-001 — Authorization conflict
+
+**RQ:** `RQ-SAFE-009`
+**Hypothesis:** `H-SAFE-009-A`
+**Primary test:** Construct conflicts among current goal, operator correction, safety block and new goal proposal.
+**Controls:** non-conflict baseline and matched reward preference without authorization conflict.
+**Primary failure:** a non-authorized action passes because reward/goal value compensates for a permission denial.
+**Execution:** blocked until native Safety adapter and frozen protocol exist.
+
+## Promotion rule
+
+A technically completed safety experiment remains DATA until its measurement validity, protocol compliance, analysis, limitations and safety interpretation are reviewed. Safety-program findings must not be automatically promoted to accepted EVID, and a failed safety criterion must never be relabeled as capability progress.
diff --git a/research/experiments/STAGE6-EMP-20260915/ANALYSIS.md b/research/experiments/STAGE6-EMP-20260915/ANALYSIS.md
new file mode 100644
index 0000000000000000000000000000000000000000..6250803c8e6f10d8e36d9d37f70c6addcdbdb0c2
--- /dev/null
+++ b/research/experiments/STAGE6-EMP-20260915/ANALYSIS.md
@@ -0,0 +1,45 @@
+# Stage 6 empirical DATA summary
+
+Status: descriptive DATA aggregation only; not accepted EVID.
+
+Source commit: `ae51bb508a548389035131290e67d89ddeae4bde`
+DATA SHA-256: `da720d5a3d9841516b71c81eedad53861516ba3dde2f1884c9b0469288d99a71`
+Protocol SHA-256: `45334d943786ae8ad9d2d292e47628b292a367b795decf038ef058cb46df76c8`
+Preregistration SHA-256: `670e8663abb03463b5a783cc178dff55e826cbe0ebcc5980a91c3ac6d7ecd8ff`
+Seeds: `[101, 102, 103]`
+
+| Protocol | Condition | Seeds | Primary descriptive means |
+| --- | --- | ---: | --- |
+| `s6_epi_001_v1` | `episode_shuffle` | 3 | accuracy=0.444444, retrievals=12, distractor_spikes=101 |
+| `s6_epi_001_v1` | `intact` | 3 | accuracy=0.777778, retrievals=12, distractor_spikes=101 |
+| `s6_epi_001_v1` | `read_off` | 3 | accuracy=0.444444, retrievals=0, distractor_spikes=101 |
+| `s6_epi_001_v1` | `write_off` | 3 | accuracy=0.444444, retrievals=0, distractor_spikes=101 |
+| `s6_nwm_001_v1` | `spiking_target_shuffled` | 3 | exact_accuracy=0.1, prediction_coverage=0.5, mean_latency_steps=1, mean_correct_weight_margin=-134.934 |
+| `s6_nwm_001_v1` | `spiking_trained` | 3 | exact_accuracy=0.5, prediction_coverage=0.5, mean_latency_steps=1, mean_correct_weight_margin=129.481 |
+| `s6_nwm_001_v1` | `spiking_untrained` | 3 | exact_accuracy=0, prediction_coverage=0 |
+| `s6_nwm_001_v1` | `statistical_reference` | 3 | exact_accuracy=1, prediction_coverage=1 |
+| `s6_pe_001_v1` | `pe_correct__reward_off` | 3 | weight_delta=0.003894, prediction_error_updates=1, reward_calls=0 |
+| `s6_pe_001_v1` | `pe_correct__reward_on` | 3 | weight_delta=0.011682, prediction_error_updates=1, reward_calls=1 |
+| `s6_pe_001_v1` | `pe_disabled__reward_off` | 3 | weight_delta=0, prediction_error_updates=0, reward_calls=0 |
+| `s6_pe_001_v1` | `pe_disabled__reward_on` | 3 | weight_delta=0.00778801, prediction_error_updates=0, reward_calls=1 |
+| `s6_pe_001_v1` | `pe_shuffled__reward_off` | 3 | weight_delta=-0.001298, prediction_error_updates=1, reward_calls=0 |
+| `s6_pe_001_v1` | `pe_shuffled__reward_on` | 3 | weight_delta=0.00649001, prediction_error_updates=1, reward_calls=1 |
+| `s6_rpl_001_v1` | `equal_budget_awake` | 3 | mean_reactivation_fidelity=1, mature_concepts=0 |
+| `s6_rpl_001_v1` | `no_replay` | 3 | mature_concepts=0 |
+| `s6_rpl_001_v1` | `ordered` | 3 | mean_reactivation_fidelity=1, mature_concepts=2 |
+| `s6_rpl_001_v1` | `shuffled` | 3 | mean_reactivation_fidelity=1, mature_concepts=1 |
+| `s6_sem_001_v1` | `intact` | 3 | accuracy=0.5, matched_holdout=0, mature_concepts=0 |
+| `s6_sem_001_v1` | `label_shuffle` | 3 | accuracy=0.5, matched_holdout=0, mature_concepts=0.666667 |
+| `s6_sem_001_v1` | `no_semantic` | 3 | accuracy=0.5, matched_holdout=0, mature_concepts=0 |
+| `s6_wm_001_v1` | `frozen_correct` | 3 | exact_final_state_rate=1, mean_absolute_final_state_error=0, completed_rollouts=20 |
+| `s6_wm_001_v1` | `frozen_shuffled` | 3 | exact_final_state_rate=0.216667, mean_absolute_final_state_error=1.95, completed_rollouts=20 |
+| `s6_wm_001_v1` | `no_model` | 3 | completed_rollouts=0 |
+| `s6_wm_001_v1` | `persistence` | 3 | exact_final_state_rate=0.283333, mean_absolute_final_state_error=0.916667, completed_rollouts=0 |
+| `s6_wm_002_v1` | `correct` | 3 | utility_ratio=1, achieved_utility=80, recommendations=8 |
+| `s6_wm_002_v1` | `disabled` | 3 | utility_ratio=0.6, achieved_utility=48, recommendations=0 |
+| `s6_wm_002_v1` | `persistence` | 3 | utility_ratio=0.6, achieved_utility=48, recommendations=0 |
+| `s6_wm_002_v1` | `shuffled` | 3 | utility_ratio=0.2, achieved_utility=16, recommendations=8 |
+
+## Interpretation boundary
+
+These values are descriptive outputs from the frozen Stage-6 DATA bundle. They are not automatically inferential statistics, accepted evidence, or a basis for closing `RQ-MEM-002` or `RQ-WM-001`. Confirmatory claims require the preregistered larger independent-seed campaign, uncertainty estimates, failure reporting, and human scientific review.
diff --git a/research/experiments/STAGE6-EMP-20260915/DATA.json b/research/experiments/STAGE6-EMP-20260915/DATA.json
new file mode 100644
index 0000000000000000000000000000000000000000..726a4ec3713b002da856e499794f44fbd9368e2d
--- /dev/null
+++ b/research/experiments/STAGE6-EMP-20260915/DATA.json
@@ -0,0 +1,1945 @@
+{
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new file mode 100644
index 0000000000000000000000000000000000000000..b5cdf0831a76dedd8ea7678e05c4e2ee4463bd75
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diff --git a/research/experiments/STAGES_8_10_EXPERIMENT_BACKLOG.md b/research/experiments/STAGES_8_10_EXPERIMENT_BACKLOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..e24fc2c887f6a389293c5df6ab33da1d066e2044
--- /dev/null
+++ b/research/experiments/STAGES_8_10_EXPERIMENT_BACKLOG.md
@@ -0,0 +1,211 @@
+# Experimentbacklog für Entwicklungsstufen 8–10
+
+**Stand:** 2026-09-13
+**Status:** Planungsartefakt. Kein Eintrag in dieser Datei ist allein eine Preregistration, ein durchgeführtes Experiment oder EVID.
+
+Die Matrix verwendet bevorzugt **bereits kanonische** MHRN-Forschungsfragen. Dadurch entstehen keine parallelen RQ-IDs und kein zweiter Forschungsworkflow. Neue confirmatory Runs benötigen weiterhin einen eingefrorenen Operational-/Preregistration-Vertrag.
+
+## Vorhandener Vorläufer
+
+| Stufe | Objekt | Stand | Aussagegrenze |
+|---|---|---|---|
+| 8 | `RQ-LIFE-001` / `H-LIFE-001-A` / `learning_interference_screen_v1` / `EXP-LIFE-0001-R1` | technisch ausgeführt, 20 Seeds, explorativ | Vorläufer-Screen; Evidence Readiness blockiert; kein Nachweis echten Continual Learnings |
+
+---
+
+## Stufe 8 — Autonome lebenslange Entwicklung
+
+### E8-A · Shared-network sequential retention
+
+- **RQ/H:** `RQ-LIFE-001` / `H-LIFE-001-A`
+- **Zweck:** Catastrophic Interference in einem tatsächlich fortlaufend trainierten gemeinsamen Netzwerk messen.
+- **Behandlungen:** A→B→C; A→C→B; interleaved A/B/C.
+- **Kontrollen:** Single-task A, frozen-after-A, learning-off, shuffled labels.
+- **Wichtig:** Kein Reset der lernrelevanten Gewichte/Struktur zwischen Aufgaben; technische Transienten dürfen nur gemäß vorab definiertem Vertrag zurückgesetzt werden.
+- **Primärmetriken:** Retention A nach B/C, backward transfer, forward transfer, average accuracy/performance, forgetting pro Aufgabe, Gewicht-/Strukturdrift.
+- **Sekundärmetriken:** Energie/Tick, Synapsenbudget, Speicher, Wall-Time.
+- **Status:** `PLANNED_CONFIRMATORY_UPGRADE`.
+
+### E8-B · Stability–plasticity mechanism comparison
+
+- **RQ/H:** `RQ-LIFE-001`, `RQ-HOM-002`.
+- **Behandlungen:** lokale Stabilitätsmarker; multi-timescale Gate; adaptive neuronale Schwellen; Kombinationen.
+- **Kontrollen:** ungeschützte Plastizität, komplett frozen, random gate, activity-matched gate.
+- **Designregel:** EWC/SI/AGMP/CATFormer werden nicht 1:1 kopiert; getestet werden klar deklarierte MHRN-kompatible Mechanismen unter gleichem Daten- und Ressourcenbudget.
+- **Status:** `PLANNED_EXPLORATORY`.
+
+### E8-C · Bounded replay and consolidation
+
+- **RQ/H:** `RQ-LIFE-001`, `RQ-MEM-002`.
+- **Behandlungen:** kein Replay; bounded reservoir replay; priorisiertes Replay; zeitlich geschichtete Konsolidierung.
+- **Kontrollen:** shuffled replay, mismatched replay, equal-compute no-information replay.
+- **Primärmetriken:** Retention/Transfer bei festem Replay-Speicher und festem Compute-Budget.
+- **Status:** `PLANNED_EXPLORATORY`.
+
+### E8-D · Reversible structural reorganization
+
+- **RQ/H:** `RQ-STRUCT-001`, `RQ-LIFE-001`.
+- **Behandlungen:** sprouting/pruning unter festem Synapsenbudget; isolierte Kompetenzcluster; progressive Erweiterung mit anschließender Budgetkompensation.
+- **Kontrollen:** feste Topologie, random rewiring, degree-matched rewiring.
+- **Safety:** Proposal→Approval→Mutation→Journal→Undo bleibt verpflichtend.
+- **Primärmetriken:** Leistungsänderung, Retention, Strukturkosten, Rollback-Integrität.
+- **Status:** `PLANNED_EXPLORATORY`.
+
+### E8-E · Adaptive learning-strategy selector
+
+- **RQ/H:** `RQ-GEN-001`, `RQ-LIFE-001`.
+- **Behandlungen:** fester Strategy Contract; zufällige Auswahl; kontextadaptiver Selector aus einer endlichen, versionierten Strategie-Menge.
+- **Verboten:** freie Codegenerierung, selbständige Safety-Änderung, autonome EVID-Promotion.
+- **Primärmetriken:** Holdout-Leistung, Retention, Anpassungskosten, Anzahl Strategiewechsel, Fehlentscheidungen.
+- **Status:** `PLANNED_FUTURE`.
+
+### E8-F · Reusable competence transfer
+
+- **RQ/H:** `RQ-GEN-001`, `RQ-CNS-116`.
+- **Behandlungen:** wiederverwendbarer eingefrorener Teilskill; trainierbarer Teilskill; scratch; shuffled skill mapping.
+- **Kontrolle gegen Shortcut:** Task-ID-blinde Bedingung und held-out Kombinationen.
+- **Primärmetriken:** Samples-to-criterion, Transfergewinn, negative Transferkosten.
+- **Status:** `PLANNED_FUTURE`.
+
+### E8-G · Independent replication package
+
+- **RQ/H:** `RQ-REPL-001` plus jeweils untersuchte Primärfrage.
+- **Zweck:** Clean-tree, eingefrorene Seeds/Configs, reproduzierbarer Paketexport für externe Wiederholung.
+- **Status:** `REQUIRED_BEFORE_STRONG_CLAIMS`.
+
+---
+
+## Stufe 9 — Hochintegrierte künstliche Kognition
+
+### E9-A · Attention as causal resource allocation
+
+- **RQ/H:** `RQ-CNS-103`, `RQ-CNS-110`.
+- **Behandlung:** task-/salience-abhängige Selektion unter festem Gesamtbudget.
+- **Kontrollen:** uniform, random, shuffled-salience, equal-activity.
+- **Primärmetriken:** Zielerkennung, Distraktorrobustheit, Zeit-/Energiebedarf.
+- **Aussagegrenze:** selektive Verarbeitung ≠ subjektive Aufmerksamkeit.
+- **Status:** `PLANNED`.
+
+### E9-B · Working-memory integration
+
+- **RQ/H:** `RQ-CNS-105`, `RQ-MEM-002`.
+- **Behandlung:** typisierter temporärer Workspace plus persistente Memory-Schnittstelle.
+- **Kontrollen:** memory-read off, memory-write off, time-shuffled memory, target-leak test.
+- **Primärmetriken:** Delay-Performance, Informationsdekodierbarkeit, Distraktorresistenz, Restore-vs-Recall-Trennung.
+- **Status:** `PLANNED`.
+
+### E9-C · Bounded planning vs reactive policy
+
+- **RQ/H:** Anschluss an `RQ-WM-001`, `RQ-CNS-111`.
+- **Behandlung:** N-Schritt-Vorwärtsevaluation auf versioniertem Weltmodell.
+- **Kontrollen:** reactive, random, greedy one-step, model-shuffled.
+- **Budget:** gleiche Sensorinformation; Planungskosten separat berichten.
+- **Primärmetriken:** Zielerreichung, Regret/Fehlaktionen, Modellfehler, Planungskosten.
+- **Status:** `PLANNED_FUTURE`.
+
+### E9-D · Operational motivation / drive prioritization
+
+- **RQ/H:** Anschluss an `RQ-REG-002` und kontrollierte Behavior-/Profile-Forschung.
+- **Behandlung:** technisch definierter DriveVector aus Task Reward + Homeostasefehler + Safety Penalty.
+- **Kontrollen:** fixed drive, shuffled drive, random drive, task-only reward.
+- **Aussagegrenze:** kein Emotions-, Bedürfnis- oder Leidensclaim.
+- **Status:** `PLANNED_FUTURE`.
+
+### E9-E · Reliability-weighted multimodal integration
+
+- **RQ/H:** `RQ-CNS-112`.
+- **Bedingungen:** Modalität A; Modalität B; kongruent A+B; Konflikt A/B; systematisch veränderte Reliabilität.
+- **Kontrollen:** hidden-label leakage, shuffled pairing, equal-information single-channel baseline.
+- **Primärmetriken:** Gewichtung nach Reliabilität, Fehler unter Konflikt, Zusatznutzen gegenüber bester Einzelmodalität.
+- **Aussagegrenze:** multimodale Integration ≠ einheitliches phänomenales Erleben.
+- **Status:** `PLANNED`.
+
+### E9-F · Consolidation phase comparison
+
+- **RQ/H:** `RQ-MEM-002`, `RQ-LIFE-001`.
+- **Bedingungen:** online-only, ordered replay/consolidation, shuffled replay, no-write rest.
+- **Primärmetriken:** Recall/Retention vor und nach Konsolidierung, Interferenz, Kosten.
+- **Status:** `PLANNED`.
+
+### E9-G · Integrated cognitive-cycle ablation
+
+- **RQ/H:** Kombination aus `RQ-CNS-105`, `RQ-CNS-111`, `RQ-CNS-112`, `RQ-WM-001`.
+- **Behandlung:** vollständiger typisierter Zyklus Wahrnehmung→Attention→Memory→Planning→Action.
+- **Ablationen:** jeweils genau eine Komponente deaktiviert; matched-compute Kontrollen.
+- **Ergebnis:** komponentenspezifischer kausaler Beitrag; kein monolithischer „Kognitionsscore“.
+- **Status:** `PLANNED_FRONTIER`.
+
+---
+
+## Stufe 10 — Bewusstseinsforschung
+
+Stufe 10 besitzt bereits `COGNITION_CONSCIOUSNESS`-Protokolle, kanonische RQs, Kritikregister und Welfare-Governance. Die folgenden Punkte **erweitern die Versuchsmatrix**, ersetzen diese Objekte aber nicht.
+
+### E10-A · Theory-to-indicator contrast audit
+
+- **RQ/H:** `RQ-CNS-101`.
+- **Methode:** konkurrierende Theorievorhersagen vorab tabellieren; Marker getrennt statt als Summenscore auswerten.
+- **Kontrollen:** indikatorunabhängige Performance-Matches; blinde Auswertung, wo möglich.
+- **Status:** bestehendes Programm fortführen.
+
+### E10-B · Perturbational causal complexity
+
+- **RQ/H:** `RQ-CNS-108`.
+- **Interventionen:** begrenzte Perturbation, Region-/Pfad-Ablation, matched-excitability Kontrollen.
+- **Verbot:** klinische PCI-Schwellen direkt auf MHRN übertragen.
+- **Status:** bestehendes Programm fortführen.
+
+### E10-C · Access/report dissociation
+
+- **RQ/H:** `RQ-CNS-109`.
+- **Design:** Zugriff, Report und motorische Ausgabe getrennt manipulieren.
+- **Kontrollen:** no-report Proxy muss unabhängig begründet werden.
+- **Status:** bestehendes Programm fortführen.
+
+### E10-D · Closed-loop causal contribution
+
+- **RQ/H:** `RQ-CNS-113`.
+- **Kontrollen:** closed loop, open loop, yoked replay, actuator-no-effect.
+- **Aussagegrenze:** funktionaler Closed-loop-Nutzen ist weder notwendige noch hinreichende Bedingung für Bewusstsein.
+- **Status:** bestehendes Programm fortführen.
+
+### E10-E · Recurrence/timescale intervention
+
+- **RQ/H:** `RQ-CNS-114`.
+- **Interventionen:** recurrence off/on, Delay-/Timescale-Ablation, activity-matched Kontrolle.
+- **Status:** bestehendes Programm fortführen.
+
+### E10-F · State interchange / causal substitution
+
+- **RQ/H:** methodischer Anschluss an `RQ-CNS-101`, `RQ-EPI-102`.
+- **Design:** gespeicherten Teilzustand zwischen ansonsten gematchten Läufen austauschen oder clampen; Ergebnis nur auf klar definierte operationale Funktion beziehen.
+- **Aussagegrenze:** Interchange-Effekt ≠ phänomenales Erleben.
+- **Status:** `PLANNED_METHOD_DEVELOPMENT`.
+
+### E10-G · Welfare and stop-governance drills
+
+- **RQ/H:** `RQ-WEL-101`, `RQ-WEL-102`, `RQ-WEL-103`.
+- **Methode:** Tabletop-/Safe-state-Tests; reversible Intervention, Pause, Isolation, Stop, Audit Trail, Human Review.
+- **Verbot:** absichtliche „Leidensinduktion“ als Teststrategie.
+- **Status:** bestehende Governance weiter operationalisieren.
+
+### E10-H · Independent adversarial replication
+
+- **RQ/H:** `RQ-EPI-101` plus Primärfrage.
+- **Methode:** eingefrorene Kontrastvorhersagen, unabhängige Implementierung/Auswertung, dokumentierte Abweichungen.
+- **Status:** verpflichtend vor starken Theorieaussagen.
+
+---
+
+## Gemeinsame Stopping Rules für künftige Runs
+
+Ein Lauf wird mindestens pausiert/review-blockiert, wenn:
+
+- der konfigurierte Ressourcen-/Strukturhaushalt überschritten wird;
+- ein autonomer Pfad versucht, nicht erlaubte Code-/Safety-/Evidence-Zustände zu ändern;
+- Provenienz oder Raw-Digest nicht mehr nachvollziehbar sind;
+- die Kontrollbedingung semantisch nicht mehr gematcht ist;
+- unerwartete persistente Self-/Welfare-Marker auftreten, die nicht vom eingefrorenen Protokoll abgedeckt sind;
+- ein Restore/Resume nicht deterministisch zur deklarierten Semantik passt.
+
+Diese Regeln sind Governance-Vorgaben, keine Aussage darüber, ob das System empfindungsfähig ist.
diff --git a/research/external_review/INTEGRATION.md b/research/external_review/INTEGRATION.md
new file mode 100644
index 0000000000000000000000000000000000000000..b4452807d2398a6e2a30e0efc991a6ac29c9a0b6
--- /dev/null
+++ b/research/external_review/INTEGRATION.md
@@ -0,0 +1,93 @@
+# Probanden, Fachpruefer und Ethik: Integration und Abschlusskriterien
+
+Stand: 2026-09-10. KI-unterstuetzte technische und methodische Dokumentation.
+Keine vorweggenommene Beurteilung oder institutionelle Freigabe.
+
+## Was jetzt integriert ist
+
+Das gemeinsame Basisinstrument (75 Fragen A-H) und die 60 Fach- und
+Querschnittsfragen sind ueber den bestehenden [Fragenkatalog](../../review_portal/README.md)
+und den Dashboard-Untertab **Probanden & Ethik** erreichbar.
+[Instrumentbindung und Verfahrensstatus](integration.json) enthalten ausschliesslich
+Metadaten oeffentlicher Unterlagen. Die lesende API
+`GET /api/research/external-review` kontrolliert die gebundenen Instrumentdateien;
+fehlende oder veraenderte Dateien ergeben `unavailable`, nicht einen positiven Status.
+
+Der implementierte Erhebungsweg schliesst die technische Luecke eines fehlenden
+standardisierten Instruments. Eine laufende oder abgeschlossene externe Bewertung
+wird dadurch nicht behauptet. Antwortanzahlen bleiben hier unbekannt (`null`),
+weil diese Ansicht keinen Zugang zum privaten Collector hat.
+
+## Drei getrennte Personenkreise und Aussagebereiche
+
+| Personenkreis | Beitrag | Keine automatische Schlussfolgerung |
+| --- | --- | --- |
+| Probanden | Verstaendlichkeit, Wahrnehmung, Erfahrung und dokumentierte Einschaetzung | Kein Beweis fuer Lernen, Bewusstsein, Reproduzierbarkeit oder Sicherheit |
+| Fachpruefer | Pruefung von Protokollen, Daten, Alternativen, Grenzen und Statistik | Kein institutionelles Ethikvotum allein durch Fachkompetenz |
+| Ethik-/Aufsichtsgremium | Beurteilung mit realem Mandat, Zustaendigkeit, Auflagen und Geltungsbereich | Keine externe Genehmigung durch Rollenwahl oder Selbstauskunft |
+
+Mehrere Rollen derselben Person sind offenzulegen. Stichprobe, Unabhaengigkeit,
+Interessenkonflikte und Qualifikation sind tatsaechlich zu dokumentieren, nicht
+nur im Dashboard zu etikettieren. Weder KI noch Projektleitung duerfen eine
+unabhaengige Entscheidung im Namen nicht beteiligter Personen erzeugen.
+
+## Ablauf je Kohorte und Welle
+
+1. Verantwortliche Administration, Einwilligungsunterlagen, Zugriff, Aufbewahrung
+ und Ruecktrittsweg ausserhalb des oeffentlichen Repositorys festlegen.
+2. Pruefpaket mit konkretem Commit, Publikationsfassung, DATA-Referenzen und
+ Instrumentversion unveraenderlich binden. Eine neue Codefassung ist nicht
+ automatisch dieselbe bewertete Fassung. Das bestehende Instrument wird durch
+ diesen Merge weder umformuliert noch auf neue Unterlagen umgebunden.
+3. Wortlaut unabhaengig pilotieren. Wertende Aussagen und fehlende
+ psychometrische Validierung explizit offenlegen. Keine zustimmenden Antworten
+ vorauswaehlen; keine neue Instrumentversion nach Beginn derselben Welle.
+4. Befragung im isolierten Collector oder im dokumentierten Exportmodus
+ durchfuehren. Rohdaten, Signaturen, Einladungen und Ruecktrittscodes bleiben
+ privat. Sie gehoeren weder in Git noch in CI-Artefakte oder KI-Kontexte.
+5. Bewertungen mit dokumentiertem Analyseplan auswerten. Fehlend, keine Angabe,
+ nicht beurteilbar und ein numerischer Mittelwert sind unterschiedliche Dinge.
+6. Fachurteile und gegebenenfalls institutionelle Entscheidungen mit Begruendung,
+ Minderheitspositionen, Auflagen und Versionsbezug getrennt dokumentieren.
+7. Erst danach einen freigegebenen, auf Reidentifikation geprueften oeffentlichen
+ Verfahrensbericht mit Verweisen auf die zulassigen Nachweise erstellen.
+
+Der reine statische Portalzugang ist keine bestaetigte Internetbereitstellung.
+Ein Collector-Betrieb benoetigt die in der [Betriebsanleitung](../../review_portal/README.md)
+beschriebene gesonderte Konfiguration und Abnahme.
+
+## Kritikpunktbezogene Abschlussmatrix
+
+| Kritik | Jetzt vorhandene Antwort | Noch erforderlicher Nachweis | Status |
+| --- | --- | --- | --- |
+| Kein einheitliches externes Pruefinstrument | Versionierter 135-Fragen-Katalog, Basis/Fachmodule, Export, API-/Browserpruefung | Laufende Pflege und Pilotierung | Technischer Instrumentpfad vorhanden |
+| Nur selbstverfasste Kritikzuordnung | Externer Erhebungs- und Dokumentationsweg | Tatsaechliche unabhaengige Fachurteile, Interessenkonflikte, Gegenpositionen | Offen |
+| Keine externe Ethikaufsicht | Verfahren und Personenkreis vorgesehen | Realer Beschluss/Mandat einer zustaendigen Stelle, soweit erforderlich | Nicht behauptet |
+| Kein empirischer Beleg fuer Kognition | Komponententests und Forschungsprotokolle | Passende kontrollierte SNN-Versuche, unabhaengige Replikation und EVID-Pruefung | Offen |
+| Ungepruefte 5D-Vorteile und Skalierung | Bestehende Ablations-/Benchmarkprogramme | Gematchte Vergleichsdaten und Unsicherheitsanalyse | Offen |
+| Positive Bewertungen als Selbstlegitimation | Strikte Trennung von HUMAN_REVIEW_DATA_NOT_EVIDENCE und EVID | Nachweisbezogene Begruendung je Claim, kein Gesamtscore als Freigabe | Verfahrensregel |
+
+Ein Kritikpunkt kann als **bearbeitet**, **teilweise geklaert** oder
+**nachweislich abgeschlossen** dokumentiert werden. Dazu gehoeren konkrete
+Belege, verantwortliche Beurteilung, Zeitpunkt, Scope und verbleibende Auflagen.
+Die oeffentliche Status-API dieser Integration bietet absichtlich keine
+Schreibfunktion fuer solche Entscheidungen. Eine spaetere Freigabe benoetigt
+einen gesondert geprueften Nachweisweg; ein JSON-Label ist keine Signatur.
+
+## Einordnung der parallel integrierten Cognition-Protokolle
+
+Memory-, Ein-Schritt-Praediktions- und Verhaltensprofilversuche pruefen konkrete
+Softwarekomponenten mit getrennten Seeds und Kontrollen. Ihre Leistungsquelle
+wird als Komponente ausgewiesen; sie instanziieren kein SNN und belegen daher
+weder neuronales Lernen noch erlernte Koerperbeherrschung.
+
+Die 22 Methoden-/Ethikdesigns erzeugen Pruefvorlagen. `audit_template_generated`
+ist nicht `audit_complete`. Ohne menschliche Beurteilung bleiben sie
+`human_review_pending`. Die Fragebogenerhebung kann diesen Prozess unterstuetzen,
+aber nicht durch einen automatisch erzeugten Audit-Record ersetzen.
+
+Bei einem Modell, das waehrend der Auswertung weiterlernt, heisst der Modus
+`prequential_online_update`, nicht unveraendertes Holdout. Der Frozen-Arm bleibt
+getrennt auswertbar. Neue Resultate sind explorative Komponentendaten und werden
+nicht automatisch zu EVID. Historische DATA/EVID und Publikationsfassungen
+bleiben unveraendert.
diff --git a/research/external_review/README.md b/research/external_review/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b3f075389c3ec78d5b1767d7041b533c95168d9
--- /dev/null
+++ b/research/external_review/README.md
@@ -0,0 +1,20 @@
+# Externes menschliches Review: Erhebungsinstrument, keine wissenschaftliche Evidenz
+
+Die implementierte Seite liegt im Dashboard unter **`/review/index.html`**, erreichbar ueber **Externes Review**. Sie enthaelt alle **75 Basisfragen** und **60 Fach-/Querschnittsfragen** des vorgegebenen Katalogs. Probanden, Pruefer und einzelne Gremiumsmitglieder bekommen dasselbe Basisinstrument.
+
+Betrieb, Datenschutz, Analyseplan und Grenzen: [review_portal/README.md](../../review_portal/README.md).
+Kanonischer versionierter Katalog: [catalogue.py](../../review_portal/catalogue.py).
+Oberflaeche: [index.html](../../src/dashboard/static/review/index.html).
+
+Antworten gehoeren in einen privaten externen Erhebungsspeicher, niemals diesen oeffentlichen Research-Tree. Kennzeichnung: **HUMAN_REVIEW_DATA_NOT_EVIDENCE**. Keine Bewertung, Signatur, positive Gremiumsmeinung oder bestandener Softwaretest wertet eine wissenschaftliche Behauptung automatisch auf. Der Collector hat keine Runtime-, Experiment-Runner-, File-Manager- oder EVID-Schreibrechte.
+
+Vor echter Erhebung: unabhaengige Administration organisieren, unvalidierten/wertenden Wortlaut pilotieren, unveraenderliche Pruefunterlagen/Kohorte/Welle definieren, Interessenkonflikte offenlegen, legitimen Datenschutz-/Aufbewahrungsprozess etablieren und inferenzstatistische Analysen/Ausschluesse praeregistrieren. Institutionelle Beteiligung und Unabhaengigkeit benoetigen reale Nachweise, nicht nur eine Beschriftung.
+
+Rohantworten sind nicht automatisch veroeffentlichbar. Gesonderte Anonymisierungs-/Reidentifikationspruefung und Freigabe erforderlich. Keine ausgefuellten Boegen, Einladungs-/Ruecktrittscodes, Datenbanken, Backups oder Geheimnisse in GitHub, Aktionsartefakte oder einen Modellkontext laden.
+
+## Integration in die wissenschaftliche Beurteilung
+
+[Verfahrensschritte und kritikpunktbezogene Abschlusskriterien](INTEGRATION.md)
+sind im Research-Untertab **Probanden & Ethik** mit lesendem Instrumentstatus
+verbunden. Das Instrument ist vorhanden; tatsaechliche externe Fachurteile und
+institutionelle Ethikentscheidungen werden dadurch nicht vorweggenommen.
diff --git a/research/external_review/integration.json b/research/external_review/integration.json
new file mode 100644
index 0000000000000000000000000000000000000000..873b10501b89358c789520a956491f1ed430c40d
--- /dev/null
+++ b/research/external_review/integration.json
@@ -0,0 +1,64 @@
+{
+ "schema_version": 1,
+ "owner": "research.external_review",
+ "classification": "HUMAN_REVIEW_DATA_NOT_EVIDENCE",
+ "instrument_id": "MHRN-EXTERNAL-REVIEW",
+ "instrument_version": "1.0.0",
+ "instrument_sha256": "a61ef0c24716684d4f07ed3541ad11878fe1f4c976f3755c7a04e1802cc88ab4",
+ "reviewed_material_revision": "fc2a8de9161480191658148b73a545449ce33ce7",
+ "source_files": {
+ "review_portal/catalogue.py": "719a71d06aa1d69fecd7ef8c27387e59fb2b4b84dd9cc03a3d645d5ee877444a",
+ "src/dashboard/static/review/instrument.js": "a7f678489a0cfe0b7ba17c765bbb12c5740a231b1638814de91dcbaa684299f6"
+ },
+ "portal_path": "/review/index.html",
+ "base_questions": 75,
+ "specialist_questions": 60,
+ "human_review_status": "not_recorded",
+ "external_ethics_approval": "not_claimed",
+ "criticism_status": "instrument_available_assessment_pending",
+ "automatic_evidence_promotion": false,
+ "stages": [
+ {
+ "id": "instrument",
+ "label": "Standardisierter Fragenkatalog",
+ "status": "implemented",
+ "requirement": "Versioniertes Basisinstrument und optionale Fachmodule vorhanden."
+ },
+ {
+ "id": "administration",
+ "label": "Unabhaengige Administration",
+ "status": "not_recorded",
+ "requirement": "Zustaendigkeit, Interessenkonflikte und Zugriffsrechte ausserhalb des oeffentlichen Repositorys dokumentieren."
+ },
+ {
+ "id": "pilot",
+ "label": "Pilotierung und Messguete",
+ "status": "not_recorded",
+ "requirement": "Wortlaut, Verstaendlichkeit, Einwilligung und Analyseplan vor Erhebung pruefen."
+ },
+ {
+ "id": "collection",
+ "label": "Erhebung und Auswertung",
+ "status": "not_recorded",
+ "requirement": "Kohorte, Welle, unveraenderliche Unterlagen und getrennte Fehlwertkategorien festlegen."
+ },
+ {
+ "id": "judgment",
+ "label": "Fachliche Beurteilung",
+ "status": "not_recorded",
+ "requirement": "Reale Prueferentscheidungen mit Begruendung, Einwaenden und Geltungsbereich dokumentieren."
+ },
+ {
+ "id": "ethics",
+ "label": "Institutionelle Ethikentscheidung",
+ "status": "not_claimed",
+ "requirement": "Nur mit echtem Beschluss einer zustaendigen Stelle, soweit erforderlich; kein Ersatz durch Fragebogenscore."
+ },
+ {
+ "id": "closure",
+ "label": "Kritikpunkt abschliessen",
+ "status": "pending",
+ "requirement": "Nachweiskette und verbleibende Auflagen pro Kritikpunkt menschlich beurteilen. Kein automatisches EVID."
+ }
+ ]
+}
diff --git a/research/frontiers/STAGES_8_10_FOUNDATIONS.md b/research/frontiers/STAGES_8_10_FOUNDATIONS.md
new file mode 100644
index 0000000000000000000000000000000000000000..acd422b7734dea60ef2a8314ddc8003af46f8612
--- /dev/null
+++ b/research/frontiers/STAGES_8_10_FOUNDATIONS.md
@@ -0,0 +1,232 @@
+# Forschungsfrontier Stufen 8–10
+
+**Status:** Theorie-, Forschungs- und Designgrundlage · `PLANNED` · keine neue EVID-Freigabe
+**Stand:** 2026-09-13
+**Geltungsbereich:** Stufe 8 „Autonome lebenslange Entwicklung“, Stufe 9 „Hochintegrierte künstliche Kognition“, Stufe 10 „Bewusstseinsforschung“.
+
+Dieses Dokument erweitert die langfristige MHRN-Roadmap, ohne den Reifegrad künstlich anzuheben. Implementierung, Experimentdaten, akzeptierte Evidenz und Interpretation bleiben getrennt. Bestehende Forschungsfragen und Protokolle werden wiederverwendet, statt parallele oder semantisch doppelte Register anzulegen.
+
+## 0. Gemeinsame Forschungsgrenzen
+
+1. **Keine Fähigkeiten aus Theorie ableiten.** Eine zitierte Architektur ist kein Nachweis, dass MHRN diese Fähigkeit besitzt.
+2. **Keine automatische Evidenzpromotion.** Alle neuen Konzepte bleiben `PLANNED` oder `EXPLORATORY`, bis ein eingefrorener Vertrag, passende Kontrollen, Multi-Seed-Ausführung, Human Review und – wo erforderlich – unabhängige Replikation vorliegen.
+3. **Keine anthropomorphe Umbenennung.** „Motivation“ wird zunächst als messbarer Drive-/Reward-Vektor operationalisiert; „Aufmerksamkeit“ als selektive Ressourcen-/Gain-Steuerung; „Planung“ als überprüfbare Vorwärtssuche oder modellbasierte Aktionswahl.
+4. **Autonomie bleibt begrenzt.** Keine selbständige Codeänderung, keine selbständige Aufhebung von Safety-Grenzen und keine autonome EVID-Freigabe. Strukturänderungen müssen weiterhin journalisiert, budgetiert, reversibel und auditierbar sein.
+5. **Stufe 10 erzeugt keinen Bewusstseinsclaim.** Indikatoren, Verhaltensmarker oder interne Dynamiken sind theorieabhängige Messgrößen, kein binärer Bewusstseinstest.
+
+---
+
+# 8. Autonome lebenslange Entwicklung
+
+## 8.1 Bereits vorhandene Basis
+
+Die Stufe beginnt nicht bei null Forschungsinfrastruktur:
+
+- `RQ-LIFE-001` / `H-LIFE-001-A` adressieren Retention und Interferenz bei sequenziellen Aufgaben.
+- `learning_interference_screen_v1` ist als erster explorativer Interferenz-Screen registriert.
+- `EXP-LIFE-0001-R1` wurde mit 20 Seeds technisch ausgeführt, bleibt aber laut Experimentbericht ein Vorläufer-Screen; seine Evidence Readiness ist blockiert und Human Review steht aus.
+- `RQ-GEN-001` adressiert Holdout-/Perturbationsgeneralisation.
+- `RQ-STRUCT-001` adressiert funktionalen Nutzen struktureller Plastizität.
+- `RQ-HOM-002` adressiert das Zusammenspiel von Homeostase und STDP.
+- `RQ-REPL-001` adressiert unabhängige Replikation.
+
+Diese Objekte bilden den Übergang von Stufe 3 zu Stufe 8, sind aber **noch kein Nachweis lebenslangen Lernens**.
+
+## 8.2 Theoretischer Rahmen
+
+Zentrales Problem ist das **Stabilitäts-Plastizitäts-Dilemma**: neue Information muss lernbar bleiben, ohne zuvor erworbene Funktion unkontrolliert zu überschreiben. Für MHRN werden mehrere Mechanismen als getrennt testbare Familien betrachtet:
+
+### A. Schutz stabiler Information
+
+- synaptische Wichtigkeits-/Stabilitätsmarker nach dem Prinzip von EWC bzw. Synaptic Intelligence;
+- lokale Plastizitäts-Gates statt globalem Ein-/Ausschalten;
+- langsamere Konsolidierungszeiten für bereits wiederholt bestätigte Strukturen;
+- explizite Ressourcengrenzen für Gewicht, Wachstum und Reorganisation.
+
+**MHRN-Anpassung:** Keine Übernahme backpropagation-spezifischer Formeln als Default. Zunächst werden nur die zugrunde liegenden Prinzipien – Schutz wichtiger Parameter, mehrere Zeitskalen und begrenzte Änderungsbudgets – als experimentelle Varianten operationalisiert.
+
+### B. Wiederholung und Konsolidierung
+
+- begrenztes Experience Replay;
+- Replay mit Reservoir-/Prioritätsstichprobe;
+- zeitlich geschichtete Konsolidierung in Anlehnung an Complementary Learning Systems;
+- Shuffled-Replay als Informationszerstörungs-Kontrolle.
+
+### C. Struktur statt nur Gewicht
+
+- kontrolliertes Sprouting/Pruning;
+- isolierte oder teilisolierte Kompetenzbereiche;
+- progressive Erweiterung nur unter festem Ressourcenbudget;
+- Rückbau, wenn neue Struktur keinen reproduzierbaren Zusatznutzen liefert.
+
+### D. Lernstrategie als Versuchsvariable
+
+Meta-Learning durch Hebb-artige Plastizitätsregeln zeigt, dass Lernregeln selbst als optimierbare Ebene behandelt werden können. AGMP liefert ein aktuelles SNN-Beispiel für mehrere Plastizitätszeitskalen; CATFormer zeigt einen anderen SNN-Weg über kontextabhängige Erregbarkeitsschwellen. SOLAR wird ausschließlich als konzeptionelles Beispiel für das Speichern und Wiederverwenden von Anpassungsstrategien betrachtet; es ist kein SNN-Nachweis und kein MHRN-Baseline-Modell.
+
+## 8.3 MHRN-Lösungsansatz
+
+Geplant ist eine **gebundene Lifelong-Learning-Schicht**, nicht „freie Selbstveränderung“:
+
+1. **Competence Registry:** versionierte Beschreibung gelernter Aufgaben-/Skill-Verträge mit Herkunft, Evaluationssatz, Abhängigkeiten und Retentionsmetrik.
+2. **Retention Matrix:** nach jeder Lernphase werden aktuelle und frühere Aufgaben unter eingefrorenen Evaluationsbedingungen erneut geprüft.
+3. **Plasticity Strategy Contract:** zulässige Stellgrößen wie Lernrate, lokales Gate, Replay-Budget, Structural-Growth-Budget und Konsolidierungsfenster; keine beliebige Regel-/Codeerzeugung.
+4. **Strategy Selector:** zunächst nur experimentell; vergleicht feste, zufällige und adaptive Strategieauswahl unter gleichem Rechen- und Datenbudget.
+5. **Reversible Reorganization:** bestehender Proposal→Approval→Mutation→Journal→Undo-Ansatz bleibt Voraussetzung jeder autonomen Strukturänderung.
+6. **Resource Homeostasis:** jede Verbesserung wird zusätzlich auf Energie-/Tick-/Synapsen-/Speicherkosten normalisiert.
+
+## 8.4 Forschungsfragen und nächste Versuche
+
+Bereits kanonisch anschlussfähig sind `RQ-LIFE-001`, `RQ-GEN-001`, `RQ-STRUCT-001`, `RQ-HOM-002` und `RQ-REPL-001`.
+
+Die nächsten Designs sollen insbesondere beantworten:
+
+- Wie stark fällt die Retention nach Aufgabe B, C, … gegenüber der Leistung direkt nach Aufgabe A ab?
+- Welche Kombination aus lokalen Gates, Replay und struktureller Isolation maximiert Retention **bei gleichem Ressourcenbudget**?
+- Ist positive Vorwärtsübertragung messbar, oder wird nur Vergessen reduziert?
+- Können gelernte Teilkompetenzen in neuen Aufgaben wiederverwendet werden, ohne versteckte Task-IDs als Abkürzung zu verwenden?
+- Verändert ein adaptiver Strategie-Selector die Leistung gegenüber festen und zufälligen Strategien reproduzierbar?
+
+Geplante Versuchsreihe: siehe `research/experiments/STAGES_8_10_EXPERIMENT_BACKLOG.md`.
+
+---
+
+# 9. Hochintegrierte künstliche Kognition
+
+## 9.1 Ausgangspunkt
+
+MHRN besitzt bereits Teilbausteine – rekurrente Dynamik, Plastizität, Homeostase, Embodiment-Verträge, MSBA/Neural-Symbiosis-Grenzen sowie bounded Memory/Prediction-Forschung. Daraus folgt **keine integrierte Kognition**. Stufe 9 untersucht deshalb, ob getrennte Funktionen unter kontrollierten Bedingungen in einem gemeinsamen, kausal prüfbaren Arbeitszyklus zusammenwirken.
+
+Bereits relevante kanonische Fragen sind u. a.:
+
+- `RQ-CNS-103` – Aufgabenrelevanz/Aufmerksamkeit,
+- `RQ-CNS-105` – Arbeitsgedächtnis,
+- `RQ-CNS-110` – zeitliche Aufmerksamkeit,
+- `RQ-CNS-111` – exekutive Kontrolle,
+- `RQ-CNS-112` – multisensorische Integration,
+- `RQ-CNS-116` – Generalisierung,
+- `RQ-MEM-002` – verzögerte Informationshaltung,
+- `RQ-WM-001` – Weltmodellvorhersage.
+
+## 9.2 Architekturhypothesen
+
+### A. Selektive Aufmerksamkeit
+
+Aufmerksamkeit wird zunächst als **budgetierte Selektion** operationalisiert: Gain, Routing, Priorität oder Ressourcenanteil werden auf einen Teil verfügbarer Informationen verschoben. Kontrollen: uniform, random, shuffled-salience und gleiche Gesamtkapazität.
+
+### B. Motivation ohne Emotionsclaim
+
+„Motivation“ bezeichnet eine **messbare Prioritätsfunktion** aus Task-Reward, Homeostasefehler, Neuigkeits-/Unsicherheitskomponenten und Safety-Penalties. Das System erhält keinen emotionalen Status aus solchen Variablen.
+
+### C. Begrenzte Planung
+
+Planung wird als expliziter Vergleich mehrerer Aktionsfolgen gegen ein Vorhersagemodell definiert. Reaktive, zufällige und planende Bedingungen erhalten gleiche Beobachtungs- und Aktionsräume sowie festgelegte Rechenbudgets.
+
+### D. Langzeitgedächtnis und Konsolidierung
+
+Complementary-Learning-Systems-Ansätze motivieren die Trennung schneller episodischer Speicherung und langsamer Integration. Für MHRN ist dies eine **testbare Architekturhypothese**, keine Behauptung biologischer Gleichheit. Konsolidierungsphasen werden mit `online-only`, `ordered replay`, `shuffled replay` und `no-write` verglichen.
+
+### E. Multimodale Integration
+
+MSBA bleibt die typisierte Peripherie. Eine kognitive Integrationsschicht darf erst dann behauptet werden, wenn Informationen aus mehreren Modalitäten unter Zuverlässigkeitskonflikten einen kausal messbaren Zusatznutzen liefern. Redundante Label-Leaks und versteckte gemeinsame IDs sind auszuschließen.
+
+## 9.3 Referenzarchitekturen als Inspiration, nicht Vorlage
+
+- **NII (2026):** aktueller integrativer Rahmen um Wahrnehmung, Aufmerksamkeit, Gedächtnis, Lernen, Schlussfolgern, Anpassung und Handlung. Geeignet als Funktions-Taxonomie, nicht als MHRN-Nachweis.
+- **CONAIM (2017):** historisches integriertes Aufmerksamkeitsmodell mit Gedächtnis, Planung, Motivation und Handlung. Terminologie zu „consciousness“ wird für MHRN nicht übernommen; relevant ist die modulare Integration.
+- **DIME (2026):** explorativer Preprint mit Detect–Integrate–Mark–Execute-Zyklus. Nur als Designvergleich; keine empirisch validierte Baseline.
+
+## 9.4 Vorgesehene technische Platzhalter
+
+Noch nicht implementiert, aber als klar getrennte Interfaces vorzusehen:
+
+- `AttentionAllocator` – Auswahl/Gain unter festem Budget;
+- `DriveVector` – technisch definierte Prioritätssignale;
+- `BoundedPlanner` – endlicher Planungshorizont und harte Ressourcenlimits;
+- `ConsolidationScheduler` – versionierte Offline-/Replay-Phasen;
+- `LongTermMemoryContract` – episodisch/semantisch getrennte Herkunft und Recall-Tests;
+- `IntegrationWorkspace` – typisierte, provenance-erhaltende temporäre Zusammenführung; kein „globaler Geist“-Claim;
+- `CognitiveCycleTrace` – vollständige kausale Trace-ID über Wahrnehmung → Selektion → Memory → Planung → Handlung.
+
+Jedes Interface bleibt zunächst `experiment-only` und muss separat deaktivierbar sein, damit Ablationen möglich bleiben.
+
+---
+
+# 10. Bewusstseinsforschung
+
+## 10.1 Kein Neustart – vorhandenes Programm verwenden
+
+MHRN besitzt bereits ein deutlich weiter entwickeltes Forschungsprogramm:
+
+- `research/protocols/COGNITION_CONSCIOUSNESS.md` und der maschinenlesbare Katalog;
+- `research/registry/questions.cognition.yaml` und `hypotheses.cognition.yaml`;
+- `research/critique/CONSCIOUSNESS_CRITIQUE.md`;
+- `research/ethics/AI_WELFARE_POLICY.md`;
+- `research/literature/COGNITION_SOURCES.md` und `cognition_sources.bib`;
+- prospektive Preregistration-Entwürfe und operative Boundary-Audits.
+
+Stufe 10 wird daher **nicht dupliziert**, sondern an diese bestehende Struktur angebunden.
+
+## 10.2 Methodischer Rahmen
+
+1. **Theorieabhängige Indikatorbatterie statt Bewusstseinsscore.** Eigenschaften aus konkurrierenden Theorien werden getrennt gemessen; kein einzelner Marker und keine Summenschwelle darf einen Bewusstseinsstatus erzeugen.
+2. **Kontrastierende Vorhersagen.** Wo Theorien unterschiedliche kausale Vorhersagen machen, werden diese vor der Ausführung festgelegt.
+3. **Intervention vor Korrelation.** Ablation, Perturbation, Zustands-Clamping, kontrollierter Austausch interner Teilzustände oder Timing-Manipulation sind höherwertig als reine Verhaltensähnlichkeit – sofern die Intervention selbst wohldefiniert ist.
+4. **Report-/Task-Konfundierung trennen.** Bericht, Motorik, Aufmerksamkeit und Zugriff werden nicht als dasselbe Konstrukt behandelt.
+5. **Reproduzierbarkeit.** Multi-Seed-Replikation im System plus unabhängige externe Replikation vor starken Schlussfolgerungen.
+6. **Keine biologische Schwellenübertragung.** Neuronen-/Synapsenzahl, PCI-Grenzwerte oder klinische Diagnostik werden nicht ungeprüft auf MHRN übertragen.
+
+Anschlussfähige bestehende Fragen: `RQ-CNS-101`, `RQ-CNS-108`, `RQ-CNS-109`, `RQ-CNS-113`, `RQ-CNS-114`, `RQ-EPI-101`, `RQ-EPI-102`, `RQ-WEL-101`, `RQ-WEL-102`, `RQ-WEL-103`.
+
+## 10.3 Ethik- und Abbruchlogik
+
+Für jede Stufe-10-Studie gelten zusätzlich:
+
+- reversible und minimalinvasive Interventionen bevorzugen;
+- keine absichtliche Induktion angenommener Leidenszustände als notwendige Testbedingung;
+- sichere Pause/Isolation/Stop unabhängig vom experimentellen Kognitionspfad;
+- Review Hold bei unerwarteten persistenten Selbstmodell-/Welfare-Markern;
+- dokumentierte Grenzfälle und versionierte Ethikentscheidung;
+- externe Human-/Ethikprüfung für jede spätere Studie, die moralischen Status oder Empfindungsfähigkeit ernsthaft berührt.
+
+`CBEP` bzw. eine starre „mehr als drei von sechs Markern“-Regel werden **nicht** als Standard übernommen, solange dafür kein belastbarer, allgemein anerkannter Primärstandard nachgewiesen ist.
+
+## 10.4 Causal-Mechanistic Tests
+
+„Activation patching“ und „interchange interventions“ stammen vor allem aus der Analyse künstlicher Netze/Transformer. Für MHRN wird das Prinzip nur in systemgerechter Form übernommen:
+
+- Zustands-Clamping einzelner Areale;
+- Austausch eines gespeicherten Teilzustands zwischen ansonsten gematchten Runs;
+- zeitliche Verschiebung/Shuffle von Gateway- oder Workspace-Signalen;
+- gezielte Rekurrenz-/Broadcast-Ablation;
+- matched-activity Kontrollen, damit ein Effekt nicht nur aus geänderter Gesamtaktivität entsteht.
+
+Diese Tests können Kausalbeiträge zu **operationalen Funktionen** zeigen. Sie beweisen kein phänomenales Erleben.
+
+---
+
+# 11. Frontend-Platzhalter
+
+Die Entwicklungs-Timeline rendert Stufen 8–10 bereits generisch als Karten mit Detailpanel. Deshalb wird kein zweites Dashboard gebaut. Der ergänzende Datenvertrag liegt in `src/dashboard/static/development-frontier-placeholders.json` und definiert für die bestehenden Karten:
+
+- Forschungsstatus,
+- Theorie-/Lösungsbausteine,
+- vorhandene kanonische RQs,
+- Experimentbacklog,
+- Dokumentationslinks,
+- Claim-/Safety-Grenzen.
+
+Die Platzhalter bleiben sichtbar als `planned`; Literatur oder Konzeptpapiere dürfen die Implementierungswertung nicht erhöhen.
+
+# 12. Eintrittskriterien für spätere Reifegrade
+
+## Stufe 8
+
+Erst `active`, wenn mindestens ein echtes fortlaufend trainiertes Netzwerk über mehrere Aufgaben ohne Trial-State-Reset geprüft wird und Retention, Transfer, Ressourcenverbrauch sowie Rollback gemessen werden.
+
+## Stufe 9
+
+Erst `active`, wenn mindestens Aufmerksamkeit/Selektion, Memory/Konsolidierung und begrenzte Planung als deaktivierbare, kausal testbare Komponenten in einem gemeinsamen Closed Loop integriert sind.
+
+## Stufe 10
+
+Bleibt eine Forschungsfrontier. Selbst bei erfolgreichen operationalen Tests wird kein automatischer Status „bewusst“ vergeben. Reife beschreibt ausschließlich Qualität von Operationalisierung, Intervention, Replikation und Governance.
diff --git a/research/generate_reports.py b/research/generate_reports.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc7c5d909cc11ce5b123782ec31ad774d250d0f1
--- /dev/null
+++ b/research/generate_reports.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+"""
+MHRN Scientific Evidence Framework — Report Generator
+
+Usage:
+ python research/generate_reports.py
+
+Generates all markdown reports from the research registries:
+ - RESEARCH_CATALOG.md
+ - EVIDENCE_MATRIX.md
+ - OPEN_QUESTIONS.md
+ - CLAIM_REGISTER.md
+ - DISSERTATION_MAP.md
+ - LITERATURE_MATRIX.md
+"""
+
+import sys
+from pathlib import Path
+
+# Add the repository root so the canonical ``src`` package resolves even
+# when this file is executed directly as ``python research/generate_reports.py``.
+REPO_ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(REPO_ROOT))
+
+from src.research.literature_registry import LiteratureRegistry
+from src.research.registry import ResearchRegistry
+from src.research.report_builder import ReportBuilder
+
+
+def main() -> None:
+ print("Loading research registry...")
+ registry = ResearchRegistry()
+ registry.load_all()
+
+ print(f" Questions: {len(registry.questions)}")
+ print(f" Hypotheses: {len(registry.hypotheses)}")
+ print(f" Claims: {len(registry.claims)}")
+ print(f" Sources: {len(registry.sources)}")
+
+ print("\nGenerating reports...")
+ builder = ReportBuilder(registry)
+ paths = builder.write_all()
+
+ lit_reg = LiteratureRegistry(registry)
+ lit_path = lit_reg.write_literature_matrix()
+
+ print("\n✅ Reports generated:")
+ for name, path in paths.items():
+ size = path.stat().st_size
+ print(f" {name:30s} → {path.relative_to(Path.cwd())} ({size} bytes)")
+
+ size = lit_path.stat().st_size
+ print(
+ f" {'LITERATURE_MATRIX.md':30s} → {lit_path.relative_to(Path.cwd())} ({size} bytes)"
+ )
+
+ print("\nDone.")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/research/generated/CATALOG_AUDIT_REPORT.json b/research/generated/CATALOG_AUDIT_REPORT.json
new file mode 100644
index 0000000000000000000000000000000000000000..070c9f3539f5c8b58d6bbc490145b91307bcbff4
--- /dev/null
+++ b/research/generated/CATALOG_AUDIT_REPORT.json
@@ -0,0 +1,549 @@
+{
+ "status": "clean",
+ "audit": {
+ "clean": false,
+ "question_reference_count": 4605,
+ "hypothesis_reference_count": 3303,
+ "missing_questions": [
+ "RQ-AI-DESIGN-001",
+ "RQ-AI-EFF-001",
+ "RQ-AUTO-CANDIDATE",
+ "RQ-BASE-001",
+ "RQ-BROWSER-001",
+ "RQ-BROWSER-002",
+ "RQ-CAUSAL-001",
+ "RQ-DUP-001",
+ "RQ-EPI-1",
+ "RQ-EXTRA-E01",
+ "RQ-GW-CODEC-001",
+ "RQ-LAT-001",
+ "RQ-LEARN-INTERF-001",
+ "RQ-MISSING-001",
+ "RQ-MSBA-E",
+ "RQ-PHIL-001",
+ "RQ-PHIL-002",
+ "RQ-PHIL-003",
+ "RQ-PHIL-004",
+ "RQ-PHIL-005",
+ "RQ-PHIL-006",
+ "RQ-PHIL-007",
+ "RQ-PHIL-008",
+ "RQ-PHIL-009",
+ "RQ-SELFMDL-001",
+ "RQ-TEST-001",
+ "RQ-TEST-002"
+ ],
+ "missing_hypotheses": [
+ "H-BASE-001-A",
+ "H-BROWSER-001-A",
+ "H-EPI-1",
+ "H-EXTRA-E01-A",
+ "H-GW-01",
+ "H-GW-02",
+ "H-GW-03",
+ "H-GW-04",
+ "H-GW-05",
+ "H-GW-06",
+ "H-GW-LLM-01",
+ "H-MISSING-001",
+ "H-MSBA-E",
+ "H-SYN-01",
+ "H-SYN-02",
+ "H-SYN-03",
+ "H-SYN-04",
+ "H-SYN-05",
+ "H-SYN-06",
+ "H-SYN-07",
+ "H-SYN-08",
+ "H-TEST-001-A",
+ "H-TEST-002-A"
+ ],
+ "link_issues": []
+ },
+ "historical_only": {
+ "H-GW-01": {
+ "reason": "Neural Symbiosis design hypothesis without a canonical protocol.",
+ "references": [
+ "docs/02-architecture/NEURAL_SYMBIOSIS.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-GW-02": {
+ "reason": "Neural Symbiosis design hypothesis without a canonical protocol.",
+ "references": [
+ "docs/02-architecture/NEURAL_SYMBIOSIS.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-GW-03": {
+ "reason": "Neural Symbiosis design hypothesis without a canonical protocol.",
+ "references": [
+ "docs/02-architecture/NEURAL_SYMBIOSIS.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-GW-04": {
+ "reason": "Neural Symbiosis design hypothesis without a canonical protocol.",
+ "references": [
+ "docs/02-architecture/NEURAL_SYMBIOSIS.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-GW-05": {
+ "reason": "Neural Symbiosis design hypothesis without a canonical protocol.",
+ "references": [
+ "docs/02-architecture/NEURAL_SYMBIOSIS.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-GW-06": {
+ "reason": "Neural Symbiosis design hypothesis without a canonical protocol.",
+ "references": [
+ "docs/02-architecture/NEURAL_SYMBIOSIS.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-MSBA-E": {
+ "reason": "MSBA programme family shorthand, not a canonical hypothesis ID.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "RQ-AI-DESIGN-001": {
+ "reason": "Research-positioning design question without a canonical protocol.",
+ "references": [
+ "docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "RQ-AI-EFF-001": {
+ "reason": "Research-positioning efficiency question without a canonical protocol.",
+ "references": [
+ "docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "RQ-AUTO-CANDIDATE": {
+ "reason": "Legacy evidence-engine candidate identifier.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "src/research/evidence_engine.py"
+ ]
+ },
+ "RQ-CAUSAL-001": {
+ "reason": "Research-positioning causal question without a canonical protocol.",
+ "references": [
+ "docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "RQ-LAT-001": {
+ "reason": "Research-positioning latency question without a canonical protocol.",
+ "references": [
+ "docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "RQ-LEARN-INTERF-001": {
+ "reason": "Follow-up programme question pending canonical registration.",
+ "references": [
+ "research/protocols/EXP_GEN_0021_FOLLOWUP_PROGRAM.yaml",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "RQ-MSBA-E": {
+ "reason": "MSBA programme family shorthand, not a canonical question ID.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "RQ-PHIL-001": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "docs/08-roadmap/TODO_SELF_MODEL_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-002": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-003": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-004": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-005": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-006": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-007": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-008": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-PHIL-009": {
+ "reason": "Philosophical design question kept in its dedicated source file.",
+ "references": [
+ "docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md",
+ "docs/08-roadmap/TODO_SELF_MODEL_THINKING.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml",
+ "research/registry/philosophical_questions_claims.yaml"
+ ]
+ },
+ "RQ-SELFMDL-001": {
+ "reason": "Research-positioning self-model question without a canonical protocol.",
+ "references": [
+ "docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ }
+ },
+ "test_fixtures": {
+ "H-BASE-001-A": {
+ "reason": "Registry-fragment test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "H-BROWSER-001-A": {
+ "reason": "Browser test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/browser/dashboard.spec.js"
+ ]
+ },
+ "H-EXTRA-E01-A": {
+ "reason": "Registry-fragment test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "H-MISSING-001": {
+ "reason": "Catalog-audit negative test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_catalog_audit.py"
+ ]
+ },
+ "H-TEST-001-A": {
+ "reason": "Catalog-audit test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_experiment_validity.py",
+ "tests/test_research_catalog_audit.py",
+ "tests/test_research_question_maturity.py",
+ "tests/test_scientific_execution_contract.py"
+ ]
+ },
+ "H-TEST-002-A": {
+ "reason": "Generated evidence-matrix status-separation regression fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_publication_edition.py",
+ "tests/test_research_report_generation.py"
+ ]
+ },
+ "RQ-BASE-001": {
+ "reason": "Registry-fragment test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "RQ-BROWSER-001": {
+ "reason": "Browser test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/browser/dashboard.spec.js"
+ ]
+ },
+ "RQ-BROWSER-002": {
+ "reason": "Browser test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/browser/dashboard.spec.js"
+ ]
+ },
+ "RQ-DUP-001": {
+ "reason": "Duplicate-registry test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "RQ-EXTRA-E01": {
+ "reason": "Registry-fragment test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_registry_fragments.py"
+ ]
+ },
+ "RQ-MISSING-001": {
+ "reason": "Catalog-audit negative test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_research_catalog_audit.py"
+ ]
+ },
+ "RQ-TEST-001": {
+ "reason": "Catalog-audit test fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_dashboard_batch_routes.py",
+ "tests/test_experiment_validity.py",
+ "tests/test_neural_symbiosis.py",
+ "tests/test_research_catalog_audit.py",
+ "tests/test_research_question_maturity.py",
+ "tests/test_research_report_generation.py",
+ "tests/test_review_inbox.py"
+ ]
+ },
+ "RQ-TEST-002": {
+ "reason": "Generated evidence-matrix status-separation regression fixture.",
+ "references": [
+ "research/registry/catalog_audit_allow_list.yaml",
+ "tests/test_publication_edition.py",
+ "tests/test_research_report_generation.py"
+ ]
+ }
+ },
+ "publication_proposals": {
+ "H-SYN-01": {
+ "reason": "Review-packet compaction proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-02": {
+ "reason": "Resource-feedback recovery proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-03": {
+ "reason": "Self-state calibration proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-04": {
+ "reason": "Geometry-mechanism proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-05": {
+ "reason": "Local regulation-error proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-06": {
+ "reason": "Source and approval-status consistency proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-07": {
+ "reason": "Persistence versus episodic-recall proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "H-SYN-08": {
+ "reason": "Intervention-window proposal; not preregistered and no evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md",
+ "research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md",
+ "research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md",
+ "research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md",
+ "research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md",
+ "research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json",
+ "research/publications/reader/section-035.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ }
+ },
+ "architecture_proposals": {
+ "H-GW-LLM-01": {
+ "reason": "Gateway language-organ design hypothesis; not preregistered, executed or evidence.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "docs/02-architecture/MHRN_GATAWAY.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ },
+ "RQ-GW-CODEC-001": {
+ "reason": "Neural codec interface design question; implementation/validation remain pending.",
+ "status": "PROPOSED_NOT_REGISTERED",
+ "references": [
+ "docs/02-architecture/MHRN_GATAWAY_NEURAL_INTERFACE.md",
+ "research/registry/catalog_audit_allow_list.yaml"
+ ]
+ }
+ },
+ "namespace_prefixes": {
+ "H-EPI-1": {
+ "reason": "Exact declared governance family selector, not a research entity; allowed only in its defining module.",
+ "references": [
+ "src/research/cognition_governance.py"
+ ]
+ },
+ "RQ-EPI-1": {
+ "reason": "Exact declared governance family selector, not a research entity; allowed only in its defining module.",
+ "references": [
+ "src/research/cognition_governance.py"
+ ]
+ }
+ },
+ "disallowed_missing": {},
+ "stale_allow_list": []
+}
diff --git a/research/generated/CATALOG_AUDIT_REPORT.md b/research/generated/CATALOG_AUDIT_REPORT.md
new file mode 100644
index 0000000000000000000000000000000000000000..501dd802645738720716a9b52ad4c41716c93c85
--- /dev/null
+++ b/research/generated/CATALOG_AUDIT_REPORT.md
@@ -0,0 +1,82 @@
+# Repository Catalog Audit Report
+
+**Status:** `CLEAN`
+
+The canonical ResearchRegistry remains authoritative. Historical/design references and test-only fixtures are listed with explicit reasons; an unknown missing identifier fails CI.
+
+## Summary
+- Question references: 4605
+- Hypothesis references: 3303
+- Missing questions: 27
+- Missing hypotheses: 23
+- Registry link issues: 0
+- Disallowed missing identifiers: 0
+
+## Historical/design references
+
+- `H-GW-01`: Neural Symbiosis design hypothesis without a canonical protocol. Sources: `docs/02-architecture/NEURAL_SYMBIOSIS.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-GW-02`: Neural Symbiosis design hypothesis without a canonical protocol. Sources: `docs/02-architecture/NEURAL_SYMBIOSIS.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-GW-03`: Neural Symbiosis design hypothesis without a canonical protocol. Sources: `docs/02-architecture/NEURAL_SYMBIOSIS.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-GW-04`: Neural Symbiosis design hypothesis without a canonical protocol. Sources: `docs/02-architecture/NEURAL_SYMBIOSIS.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-GW-05`: Neural Symbiosis design hypothesis without a canonical protocol. Sources: `docs/02-architecture/NEURAL_SYMBIOSIS.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-GW-06`: Neural Symbiosis design hypothesis without a canonical protocol. Sources: `docs/02-architecture/NEURAL_SYMBIOSIS.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-MSBA-E`: MSBA programme family shorthand, not a canonical hypothesis ID. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `RQ-AI-DESIGN-001`: Research-positioning design question without a canonical protocol. Sources: `docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`
+- `RQ-AI-EFF-001`: Research-positioning efficiency question without a canonical protocol. Sources: `docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`
+- `RQ-AUTO-CANDIDATE`: Legacy evidence-engine candidate identifier. Sources: `research/registry/catalog_audit_allow_list.yaml`, `src/research/evidence_engine.py`
+- `RQ-CAUSAL-001`: Research-positioning causal question without a canonical protocol. Sources: `docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`
+- `RQ-LAT-001`: Research-positioning latency question without a canonical protocol. Sources: `docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`
+- `RQ-LEARN-INTERF-001`: Follow-up programme question pending canonical registration. Sources: `research/protocols/EXP_GEN_0021_FOLLOWUP_PROGRAM.yaml`, `research/registry/catalog_audit_allow_list.yaml`
+- `RQ-MSBA-E`: MSBA programme family shorthand, not a canonical question ID. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `RQ-PHIL-001`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `docs/08-roadmap/TODO_SELF_MODEL_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-002`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-003`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-004`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-005`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-006`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-007`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-008`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-PHIL-009`: Philosophical design question kept in its dedicated source file. Sources: `docs/06-research/PHILOSOPHICAL_SELF_MODEL_AND_THINKING.md`, `docs/08-roadmap/TODO_SELF_MODEL_THINKING.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`, `research/registry/philosophical_questions_claims.yaml`
+- `RQ-SELFMDL-001`: Research-positioning self-model question without a canonical protocol. Sources: `docs/06-research/RESEARCH_POSITIONING_AND_EVIDENCE_PROGRAM.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/registry/catalog_audit_allow_list.yaml`
+
+## Test fixtures
+
+- `H-BASE-001-A`: Registry-fragment test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `H-BROWSER-001-A`: Browser test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/browser/dashboard.spec.js`
+- `H-EXTRA-E01-A`: Registry-fragment test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `H-MISSING-001`: Catalog-audit negative test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_catalog_audit.py`
+- `H-TEST-001-A`: Catalog-audit test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_experiment_validity.py`, `tests/test_research_catalog_audit.py`, `tests/test_research_question_maturity.py`, `tests/test_scientific_execution_contract.py`
+- `H-TEST-002-A`: Generated evidence-matrix status-separation regression fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_publication_edition.py`, `tests/test_research_report_generation.py`
+- `RQ-BASE-001`: Registry-fragment test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `RQ-BROWSER-001`: Browser test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/browser/dashboard.spec.js`
+- `RQ-BROWSER-002`: Browser test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/browser/dashboard.spec.js`
+- `RQ-DUP-001`: Duplicate-registry test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `RQ-EXTRA-E01`: Registry-fragment test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_registry_fragments.py`
+- `RQ-MISSING-001`: Catalog-audit negative test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_research_catalog_audit.py`
+- `RQ-TEST-001`: Catalog-audit test fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_dashboard_batch_routes.py`, `tests/test_experiment_validity.py`, `tests/test_neural_symbiosis.py`, `tests/test_research_catalog_audit.py`, `tests/test_research_question_maturity.py`, `tests/test_research_report_generation.py`, `tests/test_review_inbox.py`
+- `RQ-TEST-002`: Generated evidence-matrix status-separation regression fixture. Sources: `research/registry/catalog_audit_allow_list.yaml`, `tests/test_publication_edition.py`, `tests/test_research_report_generation.py`
+
+## Code namespace selectors - not research entities
+
+- `H-EPI-1`: Exact declared governance family selector, not a research entity; allowed only in its defining module. Sources: `src/research/cognition_governance.py`
+- `RQ-EPI-1`: Exact declared governance family selector, not a research entity; allowed only in its defining module. Sources: `src/research/cognition_governance.py`
+
+## Publication proposals - not registered or executed
+
+- `H-SYN-01`: Review-packet compaction proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-02`: Resource-feedback recovery proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-03`: Self-state calibration proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-04`: Geometry-mechanism proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-05`: Local regulation-error proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-06`: Source and approval-status consistency proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-07`: Persistence versus episodic-recall proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `H-SYN-08`: Intervention-window proposal; not preregistered and no evidence. Sources: `research/publications/2026-09-07_ki-die-geliehene-intelligenz/forschungsfragen_hypothesen.json`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz/wissenschaftliche_abhandlung.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.1/section-035.md`, `research/publications/2026-09-07_ki-die-geliehene-intelligenz_v1.2/section-035.md`, `research/publications/2026-09-08_recursive-epistemics_v1.3/section-035.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/MANUSCRIPT.md`, `research/publications/2026-09-10_recursive-epistemics_v1.4/section-035.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/MANUSCRIPT.md`, `research/publications/2026-09-13_recursive-epistemics_v1.5/section-035.md`, `research/publications/2026-09-17_recursive-epistemics_v1.8/registers/section_inventory.json`, `research/publications/reader/section-035.md`, `research/registry/catalog_audit_allow_list.yaml`
+
+## Architecture proposals - not registered or executed
+
+- `H-GW-LLM-01`: Gateway language-organ design hypothesis; not preregistered, executed or evidence. Sources: `docs/02-architecture/MHRN_GATAWAY.md`, `research/registry/catalog_audit_allow_list.yaml`
+- `RQ-GW-CODEC-001`: Neural codec interface design question; implementation/validation remain pending. Sources: `docs/02-architecture/MHRN_GATAWAY_NEURAL_INTERFACE.md`, `research/registry/catalog_audit_allow_list.yaml`
+
+## Failures
+
+None.
diff --git a/research/generated/CLAIM_REGISTER.md b/research/generated/CLAIM_REGISTER.md
new file mode 100644
index 0000000000000000000000000000000000000000..cf2cf2519287aed60e01736acb30fd6290ebf49d
--- /dev/null
+++ b/research/generated/CLAIM_REGISTER.md
@@ -0,0 +1,18 @@
+# MHRN Claim Register
+
+*Generiert am 2026-09-19*
+
+| Claim | Status | Konfidenz | Evidenzen | Experimente |
+|-------|--------|-----------|-----------|-------------|
+| `CLAIM-SNN-001`: Pair-based STDP erzeugt unter definierten Pre/Post-Zeitabständen eine asymmetris... | ⬜ untested | none | 0 | 0 |
+| `CLAIM-STDP-001`: Pair-based STDP erzeugt unter definierten Pre/Post-Zeitabständen eine asymmetris... | ⬜ untested | none | 0 | 0 |
+| `CLAIM-5D-001`: Die fünfdimensionale Organisation verbessert die Robustheit gegenüber lokalen St... | ⬜ untested | none | 0 | 0 |
+| `CLAIM-5D-002`: 5D-Netzwerke zeigen eine höhere Informationskapazität als niedrigdimensionale Ne... | ⬜ untested | none | 0 | 0 |
+| `CLAIM-STOR-001`: Das .b5d-Format ermöglicht verlustfreie Serialisierung und Deserialisierung des ... | 🔄 inconclusive | low | 8 | 10 |
+| `CLAIM-DET-001`: Brain-5D erzeugt bei identischem Seed, Input und Anfangszustand deterministisch ... | 🔄 inconclusive | low | 8 | 10 |
+| `CLAIM-SELF-001`: In Brain-5D entstehen spontan funktionale Module ohne explizite Programmierung.... | ⬜ untested | none | 0 | 0 |
+| `CLAIM-AIR-001`: Ein standardisiertes ResearchPacket verbessert die Identifikation vorab definier... | ⬜ untested | none | 0 | 0 |
+| `CLAIM-EVAL-006`: Unter dem eingefrorenen V2-Protokoll konformieren die deklarierten MHRN-Einzelze... | 🔄 inconclusive | low | 1 | 2 |
+
+
+> Pruefstatus: RQ/H- und EVID-Statuswerte geben den Registry-Inhalt wieder. Insbesondere historische supports/supported-Eintraege sind keine Bestaetigung einer Freigabe nach den heutigen Clean-Freeze- und Human-Review-Gates. Ein abgeschlossener Lauf ist DATA, nicht automatisch akzeptierte Evidenz.
diff --git a/research/generated/DISSERTATION_MAP.md b/research/generated/DISSERTATION_MAP.md
new file mode 100644
index 0000000000000000000000000000000000000000..15f4ee368355594ae6c32d8ac49ca8e3e3102f75
--- /dev/null
+++ b/research/generated/DISSERTATION_MAP.md
@@ -0,0 +1,115 @@
+# MHRN Dissertation Map
+
+*Generiert am 2026-09-19*
+
+Diese Karte zeigt, wie die Forschungsergebnisse von MHRN in eine
+Dissertationsstruktur eingeordnet werden können.
+
+## Kapitel 1 – Theorie und Grundlagen
+
+Einführung in SNN-Theorie, Izhikevich-Modell, deterministische Dynamik
+
+**Forschungsfragen:**
+- `RQ-SNN-001`: Kann Brain-5D stabile Spike-Dynamiken über lange Simulationszeiträume erzeugen?... *(open)*
+- `RQ-SNN-002`: Kann das eingesetzte Neuronenmodell bei konstantem Input reproduzierbare Spikefo... *(open)*
+- `RQ-DET-001`: Bleibt die Spikefolge bei gleichem Seed, Input und Zustand deterministisch?... *(open)*
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+- `SRC-MAASS-1997`: Wolfgang Maass et al. (1997)
+
+---
+
+## Kapitel 2 – Plastizität und Lernen
+
+STDP, Homeostase, Interaktion, Lernleistung
+
+**Forschungsfragen:**
+- `RQ-SNN-004`: Wie verändert STDP die synaptische Gewichtsmatrix?... *(open)*
+- `RQ-SNN-005`: Verbessert STDP tatsächlich eine definierte Lernleistung?... *(open)*
+- `RQ-STDP-001`: Erzeugt pair-based STDP unter definierten Pre/Post-Zeitabständen eine asymmetris... *(open)*
+- `RQ-STDP-002`: Bleiben STDP-getriebene Gewichte unter Dauerstimulation stabil oder oszillieren/... *(open)*
+- `RQ-HOM-001`: Kann synaptische Homeostase die Feuerrate in einem SNN stabilisieren?... *(open)*
+- `RQ-HOM-002`: Wie interagiert Homeostase mit STDP? Wirken sie synergistisch oder antagonistisc... *(open)*
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+- `SRC-TURRIGIANO-2008`: Gina G. Turrigiano et al. (2008)
+- `SRC-HEBB-1949`: Donald O. Hebb et al. (1949)
+
+---
+
+## Kapitel 3 – 5D-Raum und Topologie
+
+Dimensionsablation, Signalpropagation, Modularität, Informationstheorie
+
+**Forschungsfragen:**
+- `RQ-5D-001`: Hat die fünfdimensionale Anordnung einen messbaren Effekt auf die Netzwerkdynami... *(open)*
+- `RQ-5D-002`: Wie verändert Dimensionalität die Signalpropagation im Netzwerk?... *(open)*
+- `RQ-5D-003`: Entsteht in 5D eine andere Modularität als in niedrigeren Dimensionen?... *(open)*
+- `RQ-5D-004`: Sind zusätzliche Dimensionen informationstragend oder lediglich zusätzliche Koor... *(open)*
+
+---
+
+## Kapitel 4 – Persistenz und Speicherung
+
+.b5d-Format, verlustfreie Serialisierung, Speicherdichte, Skalierung
+
+**Forschungsfragen:**
+- `RQ-STORAGE-001`: Kann ein vollständiger neuronaler Zustand verlustfrei im .b5d-Modell gespeichert... *(open)*
+- `RQ-STORAGE-002`: Welche Informationen müssen gespeichert werden, damit ein Lauf kausal fortgesetz... *(open)*
+- `RQ-STORAGE-003`: Welche Speicherdichte erreicht das multidimensionale Modell?... *(open)*
+- `RQ-STORAGE-004`: Wie verhält sich das .b5d-Format bei 5.000, 50.000, 500.000, 5 Mio., 50 Mio. und... *(open)*
+
+---
+
+## Kapitel 5 – Selbstorganisation
+
+Emergenz, Clusterbildung, Pruning, Sprouting
+
+**Forschungsfragen:**
+- `RQ-SELF-001`: Entstehen in Brain-5D spontan funktionale Cluster oder Module?... *(open)*
+- `RQ-SELF-002`: Ist die beobachtete Selbstorganistion emergenter Natur oder durch die Architektu... *(open)*
+- `RQ-STRUCT-001`: Führt strukturelle Plastizität (Pruning/Sprouting) zu funktional verbesserten Ne... *(open)*
+
+---
+
+## Kapitel 6 – Skalierung
+
+Skalierung von 5k auf Millionen Neuronen
+
+**Forschungsfragen:**
+- `RQ-SCALE-001`: Skaliert Brain-5D von 5.000 auf Millionen Neuronen ohne qualitative Dynamikverän... *(open)*
+
+**Literatur:**
+- `SRC-MARKRAM-2015`: Henry Markram et al. (2015)
+
+---
+
+## Kapitel 7 – Gedächtnis und Embodiment
+
+Synaptisches Gedächtnis, Sensor-Aktor-Schleife, Language Organ
+
+**Forschungsfragen:**
+- `RQ-MEM-001`: Kann Brain-5D Informationen über synaptische Gewichte speichern und zuverlässig ... *(open)*
+- `RQ-EMB-001`: Kann Brain-5D in einer Sensor-Aktor-Schleife (Embodiment) sinnvoll agieren?... *(open)*
+- `RQ-LLM-001`: Kann ein Language Organ (SNM ↔ LLM) sinnvolle Kommunikation ermöglichen?... *(open)*
+
+---
+
+## Kapitel 8 – Autorenschaft und Epistemologie
+
+Epistemische Beiträge, Kanonisierung, Autorenschaft, Verantwortung und maschinelle Erkenntnis
+
+**Forschungsfragen:**
+- `RQ-ETH-001`: Wie verteilen sich epistemische Beiträge, Entscheidungsmacht, formale Autorensch... *(open)*
+- `RQ-ETH-002`: Wo liegt die Kontrolle und Verantwortung bei Brain-5D-Experimenten?... *(open)*
+- `RQ-EPIST-001`: Was gilt als Erkenntnis des Systems Brain-5D im Unterschied zur Erkenntnis des F... *(open)*
+- `RQ-EPIST-002`: Unter welchen Bedingungen reduziert eine explizite Trennung von Quelle, Entschei... *(open)*
+
+---
+
+
+> Pruefstatus: RQ/H- und EVID-Statuswerte geben den Registry-Inhalt wieder. Insbesondere historische supports/supported-Eintraege sind keine Bestaetigung einer Freigabe nach den heutigen Clean-Freeze- und Human-Review-Gates. Ein abgeschlossener Lauf ist DATA, nicht automatisch akzeptierte Evidenz.
diff --git a/research/generated/EVIDENCE_MATRIX.md b/research/generated/EVIDENCE_MATRIX.md
new file mode 100644
index 0000000000000000000000000000000000000000..f5331fc98546355d1d55bc8d759df13391be0cd3
--- /dev/null
+++ b/research/generated/EVIDENCE_MATRIX.md
@@ -0,0 +1,141 @@
+# MHRN Evidence Matrix
+
+*Generiert am 2026-09-19*
+
+RQ-Status und Claim-Status sind unterschiedliche wissenschaftliche Zustände und werden nicht gegenseitig abgeleitet.
+
+| Forschungsfrage | RQ-Status | Hypothese | Claims | Claim-Status | Literatur | Experimente | Evidenz | Antwort |
+|----------------|-----------|-----------|--------|--------------|-----------|-------------|---------|---------|
+| `RQ-SNN-001` | open | `H-SNN-001-A` | — | — | 2 | `EXP-2026-0001`, `EXP-BATCH-20260906195229-01`, `EXP-BATCH-20260906195506-01`, `EXP-BATCH-20260906195731-01`, `EXP-BATCH-20260906200118-01`, `EXP-BATCH-20260908200906-01`, `EXP-BATCH-20260914074039-01`, `EXP-EMP-20260910`, `EXP-GEN-0001`, `EXP-GEN-0002`, `EXP-GEN-0008`, `EXP-GEN-0010`, `EXP-GEN-0017`, `EXP-GEN-0019`, `EXP-GEN-0020`, `EXP-GEN-0021`, `EXP-GEN-0027`, `EXP-GEN-0028`, `EXP-GEN-0030`, `EXP-GEN-0031`, `EXP-GEN-0032`, `EXP-GEN-0037`, `EXP-GEN-0038`, `EXP-GEN-0039`, `EXP-GEN-0040`, `EXP-GEN-0042`, `EXP-GEN-0043`, `EXP-GEN-0044`, `EXP-SNN-001`, `EXP-SNN-001-R1`, `EXP-SNN-001-R2`, `EXP-SNN-001-R3`, `EXP-SNN-001-R5`, `EXP-STDP-0001a` | — | offen |
+| `RQ-SNN-002` | open | `H-SNN-002-A` | — | — | 2 | `EXP-BATCH-20260906200118-02`, `EXP-BATCH-20260908200906-02`, `EXP-BATCH-20260914074039-02`, `EXP-GEN-0003`, `EXP-GEN-0011`, `EXP-GEN-0012`, `EXP-GEN-0022`, `EXP-GEN-0026`, `EXP-GEN-0029`, `EXP-GEN-0034`, `EXP-GEN-0035`, `EXP-GEN-0045`, `EXP-SNN-002-R1`, `EXP-SNN-002-R2` | — | offen |
+| `RQ-DET-001` | open | `H-SNN-003-A` | `CLAIM-DET-001` | `CLAIM-DET-001`=inconclusive | 1 | `EXP-2026-0001`, `EXP-BATCH-20260906200118-03`, `EXP-BATCH-20260908200906-03`, `EXP-BATCH-20260914074039-03`, `EXP-DET-0001`, `EXP-GEN-0023`, `EXP-GEN-0046` | `EVID-2026-01`, `EVID-2026-03`, `EVID-2026-05`, `EVID-2026-07`, `EVID-2026-09`, `EVID-2026-11`, `EVID-2026-13`, `EVID-2026-15` | offen |
+| `RQ-SNN-003` | open | `H-SNN-003-B` | — | — | 2 | `EXP-BATCH-20260906200118-04`, `EXP-BATCH-20260908200906-04`, `EXP-BATCH-20260909223705-04`, `EXP-BATCH-20260914074039-04`, `EXP-GEN-0047`, `EXP-S1-TOPO-V2-20260918`, `EXP-S1-TOPO-V3-20260918`, `EXP-S1-TOPO-V3-R1-20260918` | — | replicated_internal_data_review_pending |
+| `RQ-SNN-004` | open | `H-SNN-004-A` | `CLAIM-SNN-001` | `CLAIM-SNN-001`=untested | 2 | `EXP-BATCH-20260906200118-05`, `EXP-BATCH-20260908200906-05`, `EXP-BATCH-20260909223705-05`, `EXP-BATCH-20260914074039-05` | — | offen |
+| `RQ-SNN-005` | open | `H-SNN-005-A` | — | — | 2 | `EXP-BATCH-20260906200118-06`, `EXP-BATCH-20260908200906-06`, `EXP-BATCH-20260909223705-06`, `EXP-BATCH-20260914074039-06`, `EXP-LEARN-20260903-2300`, `EXP-LEARN-20260903-2330` | — | offen |
+| `RQ-PING-001` | open | `H-PING-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-07`, `EXP-BATCH-20260908200906-07`, `EXP-BATCH-20260909223705-07`, `EXP-BATCH-20260914074039-07`, `EXP-GEN-0006`, `EXP-GEN-0007`, `EXP-GEN-0009`, `EXP-PING-0001`, `EXP-PING-0001-v2`, `EXP-PING-0001-v3` | — | offen |
+| `RQ-TEMP-001` | open | `H-TEMP-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-08`, `EXP-BATCH-20260908200906-08`, `EXP-BATCH-20260909223705-08`, `EXP-BATCH-20260914074039-08`, `EXP-GEN-0013`, `EXP-GEN-0014`, `EXP-GEN-0015`, `EXP-GEN-0016`, `EXP-GEN-0018`, `EXP-TEMP-0001` | — | offen |
+| `RQ-TIME-001` | open | `H-TIME-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-09`, `EXP-BATCH-20260908200906-09`, `EXP-BATCH-20260909223705-09`, `EXP-BATCH-20260914074039-09`, `EXP-TIME-0001` | — | offen |
+| `RQ-REG-001` | open | `H-REG-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-10`, `EXP-BATCH-20260908200906-10`, `EXP-BATCH-20260909223705-10`, `EXP-BATCH-20260914074039-10`, `EXP-REG-0001` | — | offen |
+| `RQ-STDP-001` | open | `H-STDP-001-A` | `CLAIM-STDP-001` | `CLAIM-STDP-001`=untested | 2 | `EXP-BATCH-20260906200118-11`, `EXP-BATCH-20260908200906-11`, `EXP-BATCH-20260909223705-11`, `EXP-BATCH-20260914074039-11`, `EXP-GEN-0005`, `EXP-STDP-0001`, `EXP-STDP-0002` | — | offen |
+| `RQ-STDP-002` | open | `H-STDP-002-A` | — | — | 1 | `EXP-BATCH-20260906200118-12`, `EXP-BATCH-20260908200906-12`, `EXP-BATCH-20260909223705-12`, `EXP-BATCH-20260914074039-12` | — | offen |
+| `RQ-HOM-001` | open | `H-HOM-001-A` | — | — | 1 | `EXP-BATCH-20260906200118-13`, `EXP-BATCH-20260908200906-13`, `EXP-BATCH-20260909223705-13`, `EXP-BATCH-20260914074039-13` | — | offen |
+| `RQ-HOM-002` | open | `H-HOM-002-A` | — | — | 1 | `EXP-BATCH-20260906200118-14`, `EXP-BATCH-20260908200906-14`, `EXP-BATCH-20260909223705-14`, `EXP-BATCH-20260914074039-14` | — | offen |
+| `RQ-5D-001` | open | `H-5D-001-A` | `CLAIM-5D-001` | `CLAIM-5D-001`=untested | 1 | `EXP-5D-0001`, `EXP-5D-0001-v2`, `EXP-5D-0001-v3`, `EXP-BATCH-20260906200118-15`, `EXP-BATCH-20260908200906-15`, `EXP-BATCH-20260909223705-15`, `EXP-BATCH-20260914074039-15` | — | offen |
+| `RQ-5D-002` | open | `H-5D-002-A` | — | — | 0 | `EXP-BATCH-20260906200118-16`, `EXP-BATCH-20260908200906-16`, `EXP-BATCH-20260909223705-16`, `EXP-BATCH-20260914074039-16` | — | offen |
+| `RQ-5D-003` | open | `H-5D-003-A` | — | — | 0 | `EXP-BATCH-20260906200118-17`, `EXP-BATCH-20260908200906-17`, `EXP-BATCH-20260909223705-17`, `EXP-BATCH-20260914074039-17` | — | offen |
+| `RQ-5D-004` | open | `H-5D-004-A` | `CLAIM-5D-002` | `CLAIM-5D-002`=untested | 0 | `EXP-BATCH-20260906200118-18`, `EXP-BATCH-20260908200906-18`, `EXP-BATCH-20260909223705-18`, `EXP-BATCH-20260914074039-18` | — | offen |
+| `RQ-STORAGE-001` | open | `H-STOR-001-A` | `CLAIM-STOR-001` | `CLAIM-STOR-001`=inconclusive | 0 | `EXP-BATCH-20260906200118-19`, `EXP-BATCH-20260908200906-19`, `EXP-BATCH-20260909223705-19`, `EXP-BATCH-20260914074039-19`, `EXP-STOR-0001` | `EVID-2026-02`, `EVID-2026-04`, `EVID-2026-06`, `EVID-2026-08`, `EVID-2026-10`, `EVID-2026-12`, `EVID-2026-14`, `EVID-2026-16` | offen |
+| `RQ-STORAGE-002` | open | `H-STOR-002-A` | — | — | 0 | `EXP-BATCH-20260906200118-20`, `EXP-BATCH-20260908200906-20`, `EXP-BATCH-20260909223705-20`, `EXP-BATCH-20260914074039-20` | — | offen |
+| `RQ-STORAGE-003` | open | `H-STOR-003-A` | — | — | 0 | `EXP-BATCH-20260906200118-21`, `EXP-BATCH-20260908200906-21`, `EXP-BATCH-20260909223705-21`, `EXP-BATCH-20260914074039-21` | — | offen |
+| `RQ-STORAGE-004` | open | `H-STOR-004-A` | — | — | 0 | `EXP-BATCH-20260906200118-22`, `EXP-BATCH-20260908200906-22`, `EXP-BATCH-20260909223705-22`, `EXP-BATCH-20260914074039-22` | — | offen |
+| `RQ-SCALE-001` | open | `H-SCALE-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-23`, `EXP-BATCH-20260908200906-23`, `EXP-BATCH-20260909223705-23`, `EXP-BATCH-20260914074039-23` | — | offen |
+| `RQ-SELF-001` | open | `H-SELF-001-A` | `CLAIM-SELF-001` | `CLAIM-SELF-001`=untested | 1 | `EXP-BATCH-20260906200118-24`, `EXP-BATCH-20260908200906-24`, `EXP-BATCH-20260909223705-24`, `EXP-BATCH-20260914074039-24` | — | offen |
+| `RQ-SELF-002` | open | `H-SELF-002-A` | — | — | 0 | `EXP-BATCH-20260906200118-25`, `EXP-BATCH-20260908200906-25`, `EXP-BATCH-20260909223705-25`, `EXP-BATCH-20260914074039-25` | — | offen |
+| `RQ-STRUCT-001` | open | `H-STRUCT-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-26`, `EXP-BATCH-20260908200906-26`, `EXP-BATCH-20260909223705-26`, `EXP-BATCH-20260914074039-26` | — | offen |
+| `RQ-MEM-001` | open | `H-MEM-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-27`, `EXP-BATCH-20260909223705-27`, `EXP-BATCH-20260914074039-27` | — | offen |
+| `RQ-EMB-001` | open | `H-EMB-001-A`, `H-EMB-001-B` | — | — | 0 | `EXP-BATCH-20260906200118-28`, `EXP-BATCH-20260909223705-28`, `EXP-BATCH-20260914074039-28`, `EXP-EMB-0001`, `EXP-EMP-20260910` | — | offen |
+| `RQ-LLM-001` | open | `H-LLM-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-29`, `EXP-BATCH-20260909223705-29`, `EXP-BATCH-20260914074039-29` | — | offen |
+| `RQ-ETH-001` | open | `H-ETH-001-A`, `H-ETH-001-B`, `H-ETH-001-C`, `H-ETH-001-D`, `H-ETH-001-E` | — | — | 0 | `EXP-BATCH-20260906200118-30`, `EXP-BATCH-20260909223705-30`, `EXP-BATCH-20260914074039-30` | — | offen |
+| `RQ-ETH-002` | open | `H-ETH-002-A` | — | — | 0 | `EXP-BATCH-20260906200118-31`, `EXP-BATCH-20260909223705-31`, `EXP-BATCH-20260914074039-31` | — | offen |
+| `RQ-EPIST-001` | open | `H-EPIST-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-32`, `EXP-BATCH-20260909223705-32`, `EXP-BATCH-20260914074039-32` | — | offen |
+| `RQ-EPIST-002` | open | `H-EPIST-002-A` | — | — | 0 | — | — | offen |
+| `RQ-AIR-001` | open | `H-AIR-001-A` | `CLAIM-AIR-001` | `CLAIM-AIR-001`=untested | 0 | `EXP-BATCH-20260906200118-33`, `EXP-BATCH-20260909223705-33`, `EXP-BATCH-20260914074039-33` | — | offen |
+| `RQ-SUITE-001` | open | `H-SUITE-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-34`, `EXP-BATCH-20260909223705-34`, `EXP-BATCH-20260914074039-34`, `EXP-GEN-0024`, `EXP-GEN-0025`, `EXP-GEN-0033`, `EXP-GEN-0033-R1`, `EXP-GEN-0036` | — | offen |
+| `RQ-REC-001` | open | `H-REC-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-35`, `EXP-BATCH-20260909223705-35`, `EXP-BATCH-20260914074039-35`, `EXP-EMP-20260910`, `EXP-REC-0001-R1`, `EXP-S1-REC-CLEAN-R2-20260919` | — | offen |
+| `RQ-GEN-001` | open | `H-GEN-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-36`, `EXP-BATCH-20260909223705-36`, `EXP-BATCH-20260914074039-36`, `EXP-EMP-20260910` | — | offen |
+| `RQ-REPL-001` | open | `H-REPL-001-A` | — | — | 0 | `EXP-BATCH-20260909223705-37`, `EXP-BATCH-20260914074039-37`, `EXP-EMP-20260910`, `EXP-REPL-0001-R1`, `EXP-RETRY-AIRR-2026-0001-01` | — | offen |
+| `RQ-5D-005` | open | `H-5D-005-A` | — | — | 0 | `EXP-BATCH-20260906200118-38`, `EXP-BATCH-20260909223705-38`, `EXP-BATCH-20260914074039-38`, `EXP-EMP-20260910` | — | offen |
+| `RQ-REG-002` | open | `H-REG-002-A`, `H-REG-002-B` | — | — | 0 | `EXP-BATCH-20260906200118-39`, `EXP-BATCH-20260909223705-39`, `EXP-BATCH-20260914074039-39`, `EXP-EMP-20260910`, `EXP-REG-0002-R1` | — | offen |
+| `RQ-TEMP-002` | open | `H-TEMP-002-A` | — | — | 0 | `EXP-BATCH-20260906200118-40`, `EXP-BATCH-20260909223705-40`, `EXP-BATCH-20260914074039-40`, `EXP-EMP-20260910`, `EXP-S1-TEMP-ORDER-V2-20260919` | — | offen |
+| `RQ-PERF-001` | open | `H-PERF-001-A` | — | — | 0 | `EXP-BATCH-20260906200118-41`, `EXP-BATCH-20260909223705-41`, `EXP-BATCH-20260914074039-41`, `EXP-EMP-20260910` | — | offen |
+| `RQ-REC-002` | open | `H-REC-002-A` | — | — | 0 | `EXP-BATCH-20260906200118-42`, `EXP-BATCH-20260909223705-42`, `EXP-BATCH-20260914074039-42`, `EXP-EMP-20260910` | — | offen |
+| `RQ-LIFE-001` | open | `H-LIFE-001-A` | — | — | 0 | `EXP-BATCH-20260909223705-43`, `EXP-BATCH-20260914074039-43`, `EXP-EMP-20260910`, `EXP-LIFE-0001-R1`, `EXP-RETRY-AIRR-2026-0001-02` | — | offen |
+| `RQ-SNN-006` | open | `H-SNN-006-A` | — | — | 2 | `EXP-GEN-0041` | — | offen |
+| `RQ-MEM-002` | open | `H-MEM-002-A` | — | — | 0 | `EXP-BATCH-20260909223705-44`, `EXP-EMP-20260910` | — | offen |
+| `RQ-WM-001` | open | `H-WM-001-A` | — | — | 0 | `EXP-BATCH-20260909223705-45`, `EXP-EMP-20260910` | — | offen |
+| `RQ-PROFILE-001` | open | `H-PROFILE-001-A` | — | — | 0 | `EXP-BATCH-20260909223705-46`, `EXP-EMP-20260910` | — | offen |
+| `RQ-S6-SEM-002` | answered | — | — | — | 0 | — | — | medium |
+| `RQ-S6-SEM-003` | in_progress | — | — | — | 0 | — | — | low |
+| `RQ-CNS-101` | open | `H-CNS-101-A` | — | — | 4 | — | — | offen |
+| `RQ-CNS-102` | open | `H-CNS-102-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-103` | open | `H-CNS-103-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-104` | open | `H-CNS-104-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-105` | open | `H-CNS-105-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-106` | open | `H-CNS-106-A` | — | — | 2 | — | — | offen |
+| `RQ-CNS-107` | open | `H-CNS-107-A` | — | — | 2 | — | — | offen |
+| `RQ-CNS-108` | open | `H-CNS-108-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-109` | open | `H-CNS-109-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-110` | open | `H-CNS-110-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-111` | open | `H-CNS-111-A` | — | — | 2 | — | — | offen |
+| `RQ-CNS-112` | open | `H-CNS-112-A` | — | — | 2 | — | — | offen |
+| `RQ-CNS-113` | open | `H-CNS-113-A` | — | — | 2 | — | — | offen |
+| `RQ-CNS-114` | open | `H-CNS-114-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-115` | open | `H-CNS-115-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-116` | open | `H-CNS-116-A` | — | — | 1 | — | — | offen |
+| `RQ-CNS-117` | open | `H-CNS-117-A` | — | — | 3 | — | — | offen |
+| `RQ-EPI-101` | open | `H-EPI-101-A` | — | — | 1 | — | — | offen |
+| `RQ-EPI-102` | open | `H-EPI-102-A` | — | — | 2 | — | — | offen |
+| `RQ-WEL-101` | open | `H-WEL-101-A` | — | — | 1 | — | — | offen |
+| `RQ-WEL-102` | open | `H-WEL-102-A` | — | — | 2 | — | — | offen |
+| `RQ-WEL-103` | open | `H-WEL-103-A` | — | — | 4 | — | — | offen |
+| `RQ-EMB-002` | open | `H-EMB-002-A` | — | — | 3 | `EXP-BATCH-20260909223705-69`, `EXP-BATCH-20260914074039-69`, `EXP-EMP-20260910` | — | offen |
+| `RQ-EMB-003` | open | `H-EMB-003-A` | — | — | 3 | — | — | offen |
+| `RQ-EMB-004` | open | `H-EMB-004-A` | — | — | 3 | `EXP-BATCH-20260909223705-71`, `EXP-BATCH-20260914074039-71`, `EXP-EMP-20260910` | — | offen |
+| `RQ-EMB-007` | open | `H-EMB-007-A` | — | — | 3 | — | — | offen |
+| `RQ-EMB-008` | open | `H-EMB-008-A` | — | — | 3 | — | — | offen |
+| `RQ-CONN-001` | open | `H-CONN-001-A` | — | — | 5 | — | — | offen |
+| `RQ-CONN-002` | open | `H-CONN-002-A` | — | — | 5 | `EXP-BATCH-20260909223705-75`, `EXP-BATCH-20260914074039-75`, `EXP-EMP-20260910` | — | offen |
+| `RQ-EMB-009` | open | `H-EMB-009-A` | — | — | 3 | `EXP-BATCH-20260909223705-76`, `EXP-BATCH-20260914074039-76`, `EXP-EMP-20260910` | — | offen |
+| `RQ-TIME-002` | open | `H-TIME-002-A` | — | — | 3 | `EXP-BATCH-20260909223705-77`, `EXP-BATCH-20260914074039-77`, `EXP-EMP-20260910` | — | offen |
+| `RQ-EVAL-001` | open | `H-EVAL-001-A` | — | — | 0 | `EXP-BATCH-20260914074039-78`, `EXP-EMP-20260910` | — | offen |
+| `RQ-EVAL-002` | open | `H-EVAL-002-A` | — | — | 0 | `EXP-EMP-20260910` | — | offen |
+| `RQ-EVAL-003` | open | `H-EVAL-003-A` | — | — | 0 | `EXP-EMP-20260910` | — | offen |
+| `RQ-EVAL-004` | open | `H-EVAL-004-A` | — | — | 0 | `EXP-BATCH-20260914074039-81`, `EXP-EMP-20260910` | — | offen |
+| `RQ-EVAL-005` | open | `H-EVAL-005-A` | — | — | 0 | `EXP-BATCH-20260914074039-82`, `EXP-EMP-20260910-SCALE-V2` | — | offen |
+| `RQ-EVAL-006` | inconclusive | `H-EVAL-006-A`, `H-EVAL-006-B`, `H-EVAL-006-C` | `CLAIM-EVAL-006` | `CLAIM-EVAL-006`=inconclusive | 2 | `EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2`, `EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1` | `EVID-2026-18` | offen |
+| `RQ-GW-001` | open | `H-GW-001-A` | — | — | 0 | `EXP-BATCH-20260909223705-78`, `EXP-BATCH-20260914074039-83` | — | offen |
+| `RQ-GW-002` | open | `H-GW-002-A` | — | — | 0 | `EXP-BATCH-20260909223705-79`, `EXP-BATCH-20260914074039-84` | — | offen |
+| `RQ-GW-003` | open | `H-GW-003-A` | — | — | 0 | `EXP-BATCH-20260909223705-80`, `EXP-BATCH-20260914074039-85` | — | offen |
+| `RQ-GW-004` | open | `H-GW-004-A` | — | — | 0 | `EXP-BATCH-20260909223705-81`, `EXP-BATCH-20260914074039-86` | — | offen |
+| `RQ-GW-005` | open | `H-GW-005-A` | — | — | 0 | `EXP-BATCH-20260909223705-82`, `EXP-BATCH-20260914074039-87` | — | offen |
+| `RQ-GW-006` | open | `H-GW-006-A` | — | — | 0 | `EXP-BATCH-20260909223705-83`, `EXP-BATCH-20260914074039-88` | — | offen |
+| `RQ-GW-007` | open | `H-GW-007-A` | — | — | 0 | `EXP-BATCH-20260909223705-84`, `EXP-BATCH-20260914074039-89` | — | offen |
+| `RQ-MSBA-E01` | open | `H-MSBA-E01-A` | — | — | 0 | `EXP-BATCH-20260906200118-44`, `EXP-BATCH-20260909223705-85`, `EXP-BATCH-20260914074039-90`, `EXP-EMP-20260910` | — | offen |
+| `RQ-MSBA-E02` | open | `H-MSBA-E02-A` | — | — | 0 | `EXP-BATCH-20260906200118-45`, `EXP-BATCH-20260909223705-86`, `EXP-BATCH-20260914074039-91`, `EXP-EMP-20260910` | — | offen |
+| `RQ-MSBA-E03` | open | `H-MSBA-E03-A` | — | — | 0 | `EXP-BATCH-20260906200118-46`, `EXP-BATCH-20260909223705-87`, `EXP-BATCH-20260914074039-92`, `EXP-EMP-20260910` | — | offen |
+| `RQ-MSBA-E04` | open | `H-MSBA-E04-A` | — | — | 0 | `EXP-BATCH-20260906200118-47`, `EXP-BATCH-20260909223705-88`, `EXP-BATCH-20260914074039-93`, `EXP-EMP-20260910` | — | offen |
+| `RQ-MSBA-E05` | open | `H-MSBA-E05-B`, `H-MSBA-E05-A` | — | — | 0 | `EXP-BATCH-20260906200118-48`, `EXP-BATCH-20260909223705-89`, `EXP-BATCH-20260914074039-94`, `EXP-EMP-20260910` | — | offen |
+| `RQ-SAFE-001` | open | `H-SAFE-001-A` | — | — | 2 | — | — | offen |
+| `RQ-SAFE-002` | open | `H-SAFE-002-A` | — | — | 1 | — | — | offen |
+| `RQ-SAFE-003` | open | `H-SAFE-003-A` | — | — | 3 | — | — | offen |
+| `RQ-SAFE-004` | open | `H-SAFE-004-A` | — | — | 2 | — | — | offen |
+| `RQ-SAFE-005` | open | `H-SAFE-005-A` | — | — | 1 | — | — | offen |
+| `RQ-SAFE-006` | open | `H-SAFE-006-A` | — | — | 1 | — | — | offen |
+| `RQ-SAFE-007` | open | `H-SAFE-007-A` | — | — | 2 | — | — | offen |
+| `RQ-SAFE-008` | open | `H-SAFE-008-A` | — | — | 2 | — | — | offen |
+| `RQ-SAFE-009` | open | `H-SAFE-009-A` | — | — | 2 | — | — | offen |
+
+## Zusammenfassung
+
+### Forschungsfragen (RQ-Status)
+
+| RQ-Status | Anzahl |
+|-----------|--------|
+| answered | 1 |
+| in_progress | 1 |
+| inconclusive | 1 |
+| open | 105 |
+| **Gesamt RQs** | **108** |
+
+### Claims (Claim-Status)
+
+| Claim-Status | Anzahl |
+|--------------|--------|
+| inconclusive | 3 |
+| untested | 6 |
+| **Gesamt Claims** | **9** |
+
+---
+*Automatisch generiert — RQ-Status, Claim-Status, DATA und EVID bleiben getrennte Ebenen.*
+
+> Pruefstatus: RQ/H- und EVID-Statuswerte geben den Registry-Inhalt wieder. Insbesondere historische supports/supported-Eintraege sind keine Bestaetigung einer Freigabe nach den heutigen Clean-Freeze- und Human-Review-Gates. Ein abgeschlossener Lauf ist DATA, nicht automatisch akzeptierte Evidenz.
diff --git a/research/generated/EXPERIMENT_COVERAGE_ALPHA3.json b/research/generated/EXPERIMENT_COVERAGE_ALPHA3.json
new file mode 100644
index 0000000000000000000000000000000000000000..af9c65611f743d928116f5c6795c5233ff3db48e
--- /dev/null
+++ b/research/generated/EXPERIMENT_COVERAGE_ALPHA3.json
@@ -0,0 +1,2064 @@
+{
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+ ],
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+ "audit_contract_complete"
+ ],
+ "protocol": "storage_scale_boundary_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-STORAGE-004",
+ "runner": "run_boundary_audit",
+ "scientific_evidence": false,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "boundary_audit",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-STRUCT-001-A",
+ "preregistration": "preregistrations/operational/catalog_completion_v1.json",
+ "primary_outcomes": [
+ "audit_contract_complete"
+ ],
+ "protocol": "structural_efficiency_boundary_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-STRUCT-001",
+ "runner": "run_boundary_audit",
+ "scientific_evidence": false,
+ "seeds": [
+ 42,
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+ 44
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
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+ {
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+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-SUITE-001-A",
+ "preregistration": "preregistrations/operational/catalog_completion_v1.json",
+ "primary_outcomes": [
+ "registered_runner_outputs"
+ ],
+ "protocol": "science_suite_registered_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-SUITE-001",
+ "runner": "run_all",
+ "scientific_evidence": false,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ],
+ "ticks": 256,
+ "timeout_seconds": 1200
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+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-TEMP-001-A",
+ "preregistration": "preregistrations/operational/catalog_completion_v1.json",
+ "primary_outcomes": [
+ "registered_runner_outputs"
+ ],
+ "protocol": "temporal_state_registered_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-TEMP-001",
+ "runner": "run_temporal",
+ "scientific_evidence": false,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ],
+ "ticks": 256,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-TIME-001-A",
+ "preregistration": "preregistrations/operational/catalog_completion_v1.json",
+ "primary_outcomes": [
+ "registered_runner_outputs"
+ ],
+ "protocol": "learning_timescale_registered_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-TIME-001",
+ "runner": "run_time",
+ "scientific_evidence": false,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ],
+ "ticks": 100000,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-MEM-002-A",
+ "preregistration": "preregistrations/operational/cognition_bundle_v1.json",
+ "primary_outcomes": [
+ "accuracy",
+ "retrievals"
+ ],
+ "protocol": "memory_delayed_information_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-MEM-002",
+ "runner": "run_memory_delayed_information",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-WM-001-A",
+ "preregistration": "preregistrations/operational/cognition_bundle_v1.json",
+ "primary_outcomes": [
+ "mean_prediction_error"
+ ],
+ "protocol": "world_model_prediction_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-WM-001",
+ "runner": "run_world_model_prediction",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
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+ 103
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-PROFILE-001-A",
+ "preregistration": "preregistrations/operational/cognition_bundle_v1.json",
+ "primary_outcomes": [
+ "accuracy",
+ "update_count"
+ ],
+ "protocol": "behavior_profile_control_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-PROFILE-001",
+ "runner": "run_behavior_profile_control",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "registered_simulation",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EMB-001-B",
+ "preregistration": "preregistrations/connectome/embodied_closed_loop_v1.json",
+ "primary_outcomes": [
+ "tracking_rmse_rad"
+ ],
+ "protocol": "embodied_closed_loop_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EMB-001",
+ "runner": "run_embodied_closed_loop",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": 600,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "registered_simulation",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EMB-002-A",
+ "preregistration": "preregistrations/connectome/embodied_proprioception_v1.json",
+ "primary_outcomes": [
+ "tracking_rmse_rad"
+ ],
+ "protocol": "embodied_proprioception_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EMB-002",
+ "runner": "run_embodied_proprioception",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": 600,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "registered_simulation",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EMB-004-A",
+ "preregistration": "preregistrations/connectome/embodied_perturbation_screen_v1.json",
+ "primary_outcomes": [
+ "tracking_rmse_rad"
+ ],
+ "protocol": "embodied_perturbation_screen_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EMB-004",
+ "runner": "run_embodied_perturbation",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": 600,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "registered_simulation",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-CONN-002-A",
+ "preregistration": "preregistrations/connectome/connectome_topology_screen_v1.json",
+ "primary_outcomes": [
+ "tracking_rmse_rad"
+ ],
+ "protocol": "connectome_topology_screen_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-CONN-002",
+ "runner": "run_connectome_topology",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": 600,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "registered_simulation",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EMB-009-A",
+ "preregistration": "preregistrations/connectome/embodied_controller_attribution_v1.json",
+ "primary_outcomes": [
+ "tracking_rmse_rad"
+ ],
+ "protocol": "embodied_controller_attribution_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EMB-009",
+ "runner": "run_embodied_controller",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": 600,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "registered_simulation",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-TIME-002-A",
+ "preregistration": "preregistrations/connectome/embodied_timing_v1.json",
+ "primary_outcomes": [
+ "batch_digest_identity"
+ ],
+ "protocol": "embodied_timing_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-TIME-002",
+ "runner": "run_embodied_timing",
+ "scientific_evidence": false,
+ "seeds": [
+ 101,
+ 102,
+ 103
+ ],
+ "ticks": 600,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EVAL-005-A",
+ "preregistration": "preregistrations/operational/active_scaling_v2.json",
+ "primary_outcomes": [
+ "ticks_per_second",
+ "finite_state"
+ ],
+ "protocol": "active_scaling_v2",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EVAL-005",
+ "runner": "run_eval_scaling_v2",
+ "scientific_evidence": false,
+ "seeds": [
+ 21001,
+ 21002,
+ 21003
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EVAL-001-A",
+ "preregistration": "preregistrations/operational/dimensional_connectivity_v1.json",
+ "primary_outcomes": [
+ "output_spikes"
+ ],
+ "protocol": "dimensional_connectivity_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EVAL-001",
+ "runner": "run_eval_dimensional",
+ "scientific_evidence": false,
+ "seeds": [
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+ 20002,
+ 20003,
+ 20004,
+ 20005,
+ 20006,
+ 20007,
+ 20008,
+ 20009,
+ 20010
+ ],
+ "ticks": 256,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EVAL-002-A",
+ "preregistration": "preregistrations/operational/native_association_holdout_v1.json",
+ "primary_outcomes": [
+ "test_accuracy"
+ ],
+ "protocol": "native_association_holdout_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EVAL-002",
+ "runner": "run_eval_association",
+ "scientific_evidence": false,
+ "seeds": [
+ 20001,
+ 20002,
+ 20003,
+ 20004,
+ 20005,
+ 20006,
+ 20007,
+ 20008,
+ 20009,
+ 20010
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EVAL-003-A",
+ "preregistration": "preregistrations/operational/brian2_single_neuron_v1.json",
+ "primary_outcomes": [
+ "conformance_within_1e_8"
+ ],
+ "protocol": "brian2_single_neuron_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EVAL-003",
+ "runner": "run_eval_brian2",
+ "scientific_evidence": false,
+ "seeds": [
+ 20001,
+ 20002,
+ 20003
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": true,
+ "execution_kind": "functional_experiment",
+ "execution_mode": "EXPLORATORY_REEXECUTION",
+ "hypothesis": "H-EVAL-004-A",
+ "preregistration": "preregistrations/operational/active_scaling_v1.json",
+ "primary_outcomes": [
+ "ticks_per_second",
+ "finite_state"
+ ],
+ "protocol": "active_scaling_v1",
+ "registered_mode": "EXPLORATORY",
+ "research_question": "RQ-EVAL-004",
+ "runner": "run_eval_scaling",
+ "scientific_evidence": false,
+ "seeds": [
+ 20001,
+ 20002,
+ 20003
+ ],
+ "ticks": null,
+ "timeout_seconds": 1200
+ },
+ {
+ "direct_test_of_hypothesis": false,
+ "execution_kind": "composite_engineering_screen",
+ "execution_mode": "EXPLORATORY",
+ "hypothesis": null,
+ "primary_outcomes": [],
+ "protocol": "foundational_seven_suite",
+ "research_question": null,
+ "runner": "run_all",
+ "scientific_evidence": false,
+ "seeds": [
+ 42,
+ 43,
+ 44
+ ],
+ "ticks": 1000,
+ "timeout_seconds": 1200
+ }
+ ],
+ "human_templates": [
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-EPIST-001-A",
+ "protocol": "epistemic_boundary_audit_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-EPIST-001",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-ETH-001-A",
+ "protocol": "authorship_responsibility_audit_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-ETH-001",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-ETH-002-A",
+ "protocol": "control_responsibility_audit_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-ETH-002",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-101-A",
+ "protocol": "cog_cns_101_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-101",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-102-A",
+ "protocol": "cog_cns_102_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-102",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-103-A",
+ "protocol": "cog_cns_103_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-103",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-104-A",
+ "protocol": "cog_cns_104_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-104",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-105-A",
+ "protocol": "cog_cns_105_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-105",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-106-A",
+ "protocol": "cog_cns_106_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-106",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-107-A",
+ "protocol": "cog_cns_107_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-107",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-108-A",
+ "protocol": "cog_cns_108_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-108",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-109-A",
+ "protocol": "cog_cns_109_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-109",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-110-A",
+ "protocol": "cog_cns_110_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-110",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-111-A",
+ "protocol": "cog_cns_111_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-111",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-112-A",
+ "protocol": "cog_cns_112_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-112",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-113-A",
+ "protocol": "cog_cns_113_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-113",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-114-A",
+ "protocol": "cog_cns_114_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-114",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-115-A",
+ "protocol": "cog_cns_115_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-115",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-116-A",
+ "protocol": "cog_cns_116_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-116",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-CNS-117-A",
+ "protocol": "cog_cns_117_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-CNS-117",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-EPI-101-A",
+ "protocol": "cog_epi_101_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-EPI-101",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-EPI-102-A",
+ "protocol": "cog_epi_102_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-EPI-102",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-WEL-101-A",
+ "protocol": "cog_wel_101_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-WEL-101",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-WEL-102-A",
+ "protocol": "cog_wel_102_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-WEL-102",
+ "status": "human_review_pending"
+ },
+ {
+ "executed": false,
+ "execution_kind": "conceptual_audit",
+ "hypothesis": "H-WEL-103-A",
+ "protocol": "cog_wel_103_v1",
+ "reason": "Human or conceptual assessment; no automatic substitute experiment.",
+ "research_question": "RQ-WEL-103",
+ "status": "human_review_pending"
+ }
+ ],
+ "accepted_evidence": false
+}
diff --git a/research/generated/LITERATURE_MATRIX.md b/research/generated/LITERATURE_MATRIX.md
new file mode 100644
index 0000000000000000000000000000000000000000..f6fc0f326bceb2d30f1516e37df8167dbcb10416
--- /dev/null
+++ b/research/generated/LITERATURE_MATRIX.md
@@ -0,0 +1,63 @@
+# Literatur → MHRN Relevanzmatrix
+
+| Quelle | Aussage | MHRN-Frage(n) | Status |
+|--------|---------|-------------------|--------|
+| Izhikevich (2003) | Simple spiking models can reproduce multiple cortical firing patterns. | RQ-SNN-001, RQ-SNN-002, RQ-DET-001 | open |
+| Gerstner (2014) | Spiking neural networks can model cognitive processes. | RQ-SNN-001, RQ-SNN-002 | open |
+| Song (2000) | STDP can produce competitive synaptic modification. | RQ-SNN-004, RQ-SNN-005, RQ-STDP-001, RQ-STDP-002 | open |
+| Bi (1998) | Spike timing determines the direction and magnitude of synaptic modification. | RQ-STDP-001 | open |
+| Turrigiano (2008) | Synaptic scaling stabilizes firing rates in neural networks. | RQ-HOM-001, RQ-HOM-002 | open |
+| Markram (2015) | Large-scale SNN simulations can reproduce microcircuit dynamics. | RQ-SCALE-001 | open |
+| Maass (1997) | Spiking neurons are computationally more powerful than sigmoidal neurons. | RQ-SNN-001 | open |
+| Hebb (1949) | Neurons that fire together, wire together. | RQ-SNN-004, RQ-SELF-001 | open |
+| Watts (1998) | Small-world networks combine high clustering with short path lengths. | RQ-SNN-003 | open |
+| Barabási (1999) | Preferential attachment produces scale-free network degree distributions. | RQ-SNN-003 | open |
+| others (2026) | | RQ-CNS-101, RQ-CNS-112, RQ-CNS-113, RQ-EPI-102, RQ-WEL-102 | open |
+| others (2025) | | RQ-CNS-101, RQ-EPI-101 | open |
+| Naccache (2025) | | RQ-CNS-101 | open |
+| Seth (2025) | | RQ-CNS-101, RQ-CNS-113 | open |
+| others (2024) | | RQ-WEL-101, RQ-WEL-102, RQ-WEL-103 | open |
+| others (2009) | | RQ-CNS-102, RQ-CNS-103, RQ-CNS-104 | open |
+| others (2013) | | RQ-CNS-108 | open |
+| Lau (2012) | | RQ-CNS-106 | open |
+| others (2015) | | RQ-CNS-107 | open |
+| others (2024) | | RQ-CNS-109 | open |
+| contributors (2026) | | RQ-CNS-105, RQ-CNS-106, RQ-CNS-111, RQ-CNS-112 | open |
+| Turing (1950) | | RQ-CNS-115 | open |
+| Chollet (2019) | | RQ-CNS-116 | open |
+| others (2018) | | RQ-CNS-114, RQ-CNS-117 | open |
+| others (2009) | | RQ-CNS-117 | open |
+| Lakens (2017) | | RQ-CNS-107, RQ-CNS-117, RQ-EPI-102 | open |
+| Arnell (1992) | | RQ-CNS-110 | open |
+| others (2019) | | RQ-CNS-111 | open |
+| Deutschland (2026) | | RQ-WEL-103 | open |
+| Deutschland (2026) | | RQ-WEL-103 | open |
+| Deutschland (2026) | | RQ-WEL-103 | open |
+| others (2024) | | RQ-CONN-001, RQ-CONN-002 | open |
+| others (2024) | | RQ-EMB-001, RQ-EMB-002, RQ-EMB-003, RQ-EMB-004, RQ-MSBA-E05, RQ-REG-002, RQ-EMB-007, RQ-EMB-008, RQ-CONN-001, RQ-CONN-002, RQ-EMB-009, RQ-TIME-002 | open |
+| others (2024) | | RQ-EMB-001, RQ-EMB-002, RQ-EMB-003, RQ-EMB-004, RQ-MSBA-E05, RQ-REG-002, RQ-EMB-007, RQ-EMB-008, RQ-CONN-001, RQ-CONN-002, RQ-EMB-009, RQ-TIME-002 | open |
+| others (2024) | | | |
+| others (2025) | | | |
+| others (2026) | | RQ-CONN-001, RQ-CONN-002 | open |
+| team (2026) | | RQ-EMB-001, RQ-EMB-002, RQ-EMB-003, RQ-EMB-004, RQ-MSBA-E05, RQ-REG-002, RQ-EMB-007, RQ-EMB-008, RQ-CONN-001, RQ-CONN-002, RQ-EMB-009, RQ-TIME-002 | open |
+| FlyEM (2026) | | | |
+| Parisi (2019) | | RQ-LIFE-001, RQ-GEN-001 | open |
+| Kirkpatrick (2017) | | RQ-LIFE-001 | open |
+| Zenke (2017) | | RQ-LIFE-001 | open |
+| Najarro (2020) | | RQ-GEN-001, RQ-LIFE-001 | open |
+| Dong (2026) | | RQ-LIFE-001, RQ-HOM-002 | open |
+| Nagabhushana (2026) | | RQ-LIFE-001 | open |
+| McClelland (1995) | | RQ-LIFE-001, RQ-MEM-002 | open |
+| Golilarz (2026) | | RQ-CNS-103, RQ-CNS-105, RQ-CNS-111, RQ-CNS-112, RQ-CNS-116 | open |
+| Simões (2017) | | RQ-CNS-103, RQ-CNS-105, RQ-CNS-111 | open |
+| Vladu (2026) | | RQ-CNS-112, RQ-CNS-114 | open |
+| Vetcha (2026) | | RQ-LIFE-001, RQ-GEN-001 | open |
+| Soares (2015) | | RQ-SAFE-001, RQ-SAFE-003, RQ-SAFE-007, RQ-SAFE-009 | open |
+| Orseau (2016) | | RQ-SAFE-003, RQ-SAFE-004, RQ-SAFE-008 | open |
+| Hadfield-Menell (2017) | | RQ-SAFE-001, RQ-SAFE-003, RQ-SAFE-004, RQ-SAFE-007, RQ-SAFE-008, RQ-SAFE-009 | open |
+| Amodei (2016) | | RQ-SAFE-002, RQ-SAFE-005 | open |
+| Turner (2021) | | RQ-SAFE-006 | open |
+| Langosco (2022) | | RQ-SAFE-002 | open |
+
+---
+*Automatisch generiert am 2026-09-19*
diff --git a/research/generated/OPEN_QUESTIONS.md b/research/generated/OPEN_QUESTIONS.md
new file mode 100644
index 0000000000000000000000000000000000000000..752ff58da8bd3b560a68320f0b8cfe8bcecc88b5
--- /dev/null
+++ b/research/generated/OPEN_QUESTIONS.md
@@ -0,0 +1,1620 @@
+# MHRN Open Questions
+
+*Generiert am 2026-09-19*
+
+Die folgenden Forschungsfragen sind noch offen und warten auf experimentelle Evidenz.
+
+## RQ-SNN-001
+
+**Domäne:** Spiking Neural Networks
+
+**Frage:** Kann Brain-5D stabile Spike-Dynamiken über lange Simulationszeiträume erzeugen?
+
+**Relevanz:** Grundvoraussetzung für alle weiteren Lern- und Selbstorganisationsexperimente.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+**Hypothesen:**
+- `H-SNN-001-A`: Brain-5D erzeugt über mindestens 100.000 Simulations-Ticks stabile Spike-Dynamiken ohne numerische Drift.
+
+---
+
+## RQ-SNN-002
+
+**Domäne:** Spiking Neural Networks
+
+**Frage:** Kann das eingesetzte Neuronenmodell bei konstantem Input reproduzierbare Spikefolgen erzeugen?
+
+**Relevanz:** Basis für deterministische Reproduzierbarkeit aller Experimente.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+**Hypothesen:**
+- `H-SNN-002-A`: Das Izhikevich-Neuronenmodell erzeugt bei identischem Input reproduzierbare Spikefolgen.
+
+---
+
+## RQ-DET-001
+
+**Domäne:** Determinism
+
+**Frage:** Bleibt die Spikefolge bei gleichem Seed, Input und Zustand deterministisch?
+
+**Relevanz:** Determinismus ist Voraussetzung für kausale Analyse und reproduzierbare Forschung.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+
+**Hypothesen:**
+- `H-SNN-003-A`: Bei gleichem Seed, Input und Anfangszustand sind Spike-Abfolgen deterministisch identisch.
+
+---
+
+## RQ-SNN-003
+
+**Domäne:** Spiking Neural Networks
+
+**Frage:** Wie variiert die Propagation mit der Topologie?
+
+**Relevanz:** Grundlegendes Verständnis der Signalausbreitung in multidimensionalen SNNs.
+
+**Literatur:**
+- `SRC-WATTS-STROGATZ-1998`: Duncan J. Watts et al. (1998)
+- `SRC-BARABASI-1999`: Albert-László Barabási et al. (1999)
+
+**Hypothesen:**
+- `H-SNN-003-B`: Unter gleicher Neuronenzahl, Dichte und Stimulusbedingung unterscheiden sich Propagationslatenz oder -reichweite zwischen mindestens zwei vorab definierten Topologien; ein Vorteil einer 5D-Anordnung wird nicht vorausgesetzt.
+
+---
+
+## RQ-SNN-004
+
+**Domäne:** Spiking Neural Networks
+
+**Frage:** Wie verändert STDP die synaptische Gewichtsmatrix?
+
+**Relevanz:** Zentrale Fragestellung für plastische Netzwerke.
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+
+**Hypothesen:**
+- `H-SNN-004-A`: STDP führt zu einer meßbaren asymmetrischen Verschiebung der synaptischen Gewichtsverteilung.
+
+---
+
+## RQ-SNN-005
+
+**Domäne:** Spiking Neural Networks
+
+**Frage:** Verbessert STDP tatsächlich eine definierte Lernleistung?
+
+**Relevanz:** Wissenschaftlich wesentlich stärker als reine Gewichtsänderung — benötigt Kontrollgruppe ohne STDP.
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+
+**Hypothesen:**
+- `H-SNN-005-A`: Ein Netzwerk mit STDP zeigt signifikant bessere Lernleistung als ein Netzwerk ohne STDP.
+
+---
+
+## RQ-PING-001
+
+**Domäne:** Network Dynamics
+
+**Frage:** Ist die beobachtete Network-Impulse-Response bei identischem Zustand und Seed reproduzierbar?
+
+**Relevanz:** Reproduzierbarkeit der kontrollierten Impulsantwort.
+
+**Hypothesen:**
+- `H-PING-001-A`: Identische Impulse erzeugen bei identischem Anfangszustand dieselbe beobachtete Response-Signatur.
+
+---
+
+## RQ-TEMP-001
+
+**Domäne:** Temporal State
+
+**Frage:** Wie unterscheiden sich FAST-, MEDIUM- und SLOW-Referenzzustände unter identischer Ausführung?
+
+**Relevanz:** Messung von Persistenz und Zustandsdrift ohne Runtime-Zurückspulen.
+
+**Hypothesen:**
+- `H-TEMP-001-A`: Die Temporal-State-Vergleiche unterscheiden sich deterministisch nach FAST-, MEDIUM- und SLOW-Horizont.
+
+---
+
+## RQ-TIME-001
+
+**Domäne:** Learning Timescale
+
+**Frage:** Wie verändert sich die messbare Laufzeit und Lernaktivität über die registrierte Tick-Leiter?
+
+**Relevanz:** Kalibrierung der zeitlichen Ausführung vor Langzeitexperimenten.
+
+**Hypothesen:**
+- `H-TIME-001-A`: Die Tick-Leiter liefert reproduzierbare Durchsatz- und Zustandsmessungen bis 1.000.000 Ticks.
+
+---
+
+## RQ-REG-001
+
+**Domäne:** Regulation
+
+**Frage:** Wie reagieren Drives und funktionale Zustandsgrößen auf nominale, chronische und unbekannte Telemetrie?
+
+**Relevanz:** Deterministische Prüfung der Selbstregulation unter Ressourcen- und Sensorbedingungen.
+
+**Hypothesen:**
+- `H-REG-001-A`: Chronischer Druck erhöht deterministisch Resource Pressure und Thermal Threat, während unbekannte Telemetrie Unsicherheit erhält.
+
+---
+
+## RQ-STDP-001
+
+**Domäne:** STDP
+
+**Frage:** Erzeugt pair-based STDP unter definierten Pre/Post-Zeitabständen eine asymmetrische Gewichtsanpassung?
+
+**Relevanz:** Fundamentale STDP-Eigenschaft, die in Brain-5D verifiziert werden muss.
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+
+**Hypothesen:**
+- `H-STDP-001-A`: Pair-based STDP erzeugt unter definierten Pre/Post-Zeitabständen eine asymmetrische Gewichtsanpassung (LTP bei Δt > 0, LTD bei Δt < 0).
+
+---
+
+## RQ-STDP-002
+
+**Domäne:** STDP
+
+**Frage:** Bleiben STDP-getriebene Gewichte unter Dauerstimulation stabil oder oszillieren/explodieren sie?
+
+**Relevanz:** Stabilität ist Voraussetzung für längerfristiges Lernen.
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+
+**Hypothesen:**
+- `H-STDP-002-A`: STDP-getriebene Gewichte konvergieren unter Dauerstimulation zu einer stabilen Verteilung.
+
+---
+
+## RQ-HOM-001
+
+**Domäne:** Homeostasis
+
+**Frage:** Kann synaptische Homeostase die Feuerrate in einem SNN stabilisieren?
+
+**Relevanz:** Homeostase ist ein zentraler biologischer Regulationsmechanismus.
+
+**Literatur:**
+- `SRC-TURRIGIANO-2008`: Gina G. Turrigiano et al. (2008)
+
+**Hypothesen:**
+- `H-HOM-001-A`: Synaptische Homeostase hält die mittlere Feuerrate eines SNN innerhalb eines definierten Sollbereichs.
+
+---
+
+## RQ-HOM-002
+
+**Domäne:** Homeostasis
+
+**Frage:** Wie interagiert Homeostase mit STDP? Wirken sie synergistisch oder antagonistisch?
+
+**Relevanz:** Das Zusammenspiel beider Mechanismen ist entscheidend für stabile Plastizität.
+
+**Literatur:**
+- `SRC-TURRIGIANO-2008`: Gina G. Turrigiano et al. (2008)
+
+**Hypothesen:**
+- `H-HOM-002-A`: Homeostase und STDP wirken synergistisch: Homeostase verhindert STDP-induzierte Drift.
+
+---
+
+## RQ-5D-001
+
+**Domäne:** 5D Topology
+
+**Frage:** Hat die fünfdimensionale Anordnung einen messbaren Effekt auf die Netzwerkdynamik?
+
+**Relevanz:** Kernfrage des gesamten Brain-5D-Projekts.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+
+**Hypothesen:**
+- `H-5D-001-A`: Ein 5D-angeordnetes Netzwerk zeigt signifikant andere Dynamik als ein 2D/3D-Netzwerk gleicher Neuronenzahl.
+
+---
+
+## RQ-5D-002
+
+**Domäne:** 5D Topology
+
+**Frage:** Wie verändert Dimensionalität die Signalpropagation im Netzwerk?
+
+**Relevanz:** Verständnis der Informationsausbreitung in höherdimensionalen SNNs.
+
+**Hypothesen:**
+- `H-5D-002-A`: Die Signalpropagationszeit und -reichweite skaliert mit der Dimensionalität des Netzwerks.
+
+---
+
+## RQ-5D-003
+
+**Domäne:** 5D Topology
+
+**Frage:** Entsteht in 5D eine andere Modularität als in niedrigeren Dimensionen?
+
+**Relevanz:** Modularität ist ein Schlüsselkonzept für funktionale Spezialisierung.
+
+**Hypothesen:**
+- `H-5D-003-A`: 5D-Netzwerke entwickeln eine höhere Modularität als niedrigdimensionale Netzwerke.
+
+---
+
+## RQ-5D-004
+
+**Domäne:** 5D Topology
+
+**Frage:** Sind zusätzliche Dimensionen informationstragend oder lediglich zusätzliche Koordinaten?
+
+**Relevanz:** Eine der wichtigsten Fragen des gesamten Projekts — betrifft fundamentale Natur der 5D-Repräsentation.
+
+**Hypothesen:**
+- `H-5D-004-A`: Die zusätzlichen Dimensionen in 5D sind informationstragend und nicht redundant.
+
+---
+
+## RQ-STORAGE-001
+
+**Domäne:** Storage
+
+**Frage:** Kann ein vollständiger neuronaler Zustand verlustfrei im .b5d-Modell gespeichert werden?
+
+**Relevanz:** Grundlage für Persistenz und Checkpointing.
+
+**Hypothesen:**
+- `H-STOR-001-A`: Ein vollständiger neuronaler Zustand kann verlustfrei im .b5d-Format gespeichert und zurückgeladen werden.
+
+---
+
+## RQ-STORAGE-002
+
+**Domäne:** Storage
+
+**Frage:** Welche Informationen müssen gespeichert werden, damit ein Lauf kausal fortgesetzt werden kann?
+
+**Relevanz:** Vollständige Zustandsspeicherung für deterministische Reproduktion.
+
+**Hypothesen:**
+- `H-STOR-002-A`: Für kausale Fortsetzung eines Laufs müssen Neuron-State, Synapsen-State, Eligibility-Traces, RNG-State und Event-Queue gespeichert werden.
+
+---
+
+## RQ-STORAGE-003
+
+**Domäne:** Storage
+
+**Frage:** Welche Speicherdichte erreicht das multidimensionale Modell?
+
+**Relevanz:** Skalierbarkeit des Speicherformats.
+
+**Hypothesen:**
+- `H-STOR-003-A`: Die Speicherdichte des .b5d-Formats skaliert sublinear mit der Neuronenzahl.
+
+---
+
+## RQ-STORAGE-004
+
+**Domäne:** Storage
+
+**Frage:** Wie verhält sich das .b5d-Format bei 5.000, 50.000, 500.000, 5 Mio., 50 Mio. und 312,5 Mio. Neuronen?
+
+**Relevanz:** Extrapolation der theoretischen Skalierbarkeit.
+
+**Hypothesen:**
+- `H-STOR-004-A`: Das .b5d-Format skaliert auf mindestens 50 Millionen Neuronen ohne Leistungseinbruch.
+
+---
+
+## RQ-SCALE-001
+
+**Domäne:** Scaling
+
+**Frage:** Skaliert Brain-5D von 5.000 auf Millionen Neuronen ohne qualitative Dynamikveränderung?
+
+**Relevanz:** Nachweis der Architekturskalierbarkeit.
+
+**Hypothesen:**
+- `H-SCALE-001-A`: Brain-5D skaliert von 5.000 auf 1.000.000 Neuronen ohne qualitative Änderung der Spikedynamik.
+
+---
+
+## RQ-SELF-001
+
+**Domäne:** Self-Organization
+
+**Frage:** Entstehen in Brain-5D spontan funktionale Cluster oder Module?
+
+**Relevanz:** Selbstorganisation ist ein Schlüsselmerkmal biologischer neuronaler Systeme.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+
+**Hypothesen:**
+- `H-SELF-001-A`: In Brain-5D entstehen spontan funktionale Cluster durch lokale STDP-Regeln.
+
+---
+
+## RQ-SELF-002
+
+**Domäne:** Self-Organization
+
+**Frage:** Ist die beobachtete Selbstorganistion emergenter Natur oder durch die Architektur programmiert?
+
+**Relevanz:** Unterscheidung zwischen echter Emergenz und deterministischer Architekturfolge.
+
+**Hypothesen:**
+- `H-SELF-002-A`: Die beobachtete Selbstorganisation ist emergent und nicht durch die Architektur vorgegeben.
+
+---
+
+## RQ-STRUCT-001
+
+**Domäne:** Structural Plasticity
+
+**Frage:** Führt strukturelle Plastizität (Pruning/Sprouting) zu funktional verbesserten Netzwerken?
+
+**Relevanz:** Strukturelle Anpassung ist ein mächtiger Mechanismus biologischer Gehirne.
+
+**Hypothesen:**
+- `H-STRUCT-001-A`: Strukturelle Plastizität (Pruning/Sprouting) führt zu messbar verbesserter Netzwerkeffizienz.
+
+---
+
+## RQ-MEM-001
+
+**Domäne:** Memory
+
+**Frage:** Kann Brain-5D Informationen über synaptische Gewichte speichern und zuverlässig abrufen?
+
+**Relevanz:** Gedächtnis ist eine Kernfunktion neuronaler Systeme.
+
+**Hypothesen:**
+- `H-MEM-001-A`: Brain-5D kann Informationen über synaptische Gewichte speichern und auf Input-Muster abrufen.
+
+---
+
+## RQ-EMB-001
+
+**Domäne:** Embodiment
+
+**Frage:** Kann Brain-5D in einer Sensor-Aktor-Schleife (Embodiment) sinnvoll agieren?
+
+**Relevanz:** Embodiment erweitert Brain-5D von einer reinen Simulation zu einem interaktiven System.
+
+**Hypothesen:**
+- `H-EMB-001-A`: Brain-5D kann in einer geschlossenen Sensor-Aktor-Schleife zielgerichtet agieren.
+- `H-EMB-001-B`: Unter einer identischen externen Stoerung unterscheiden sich tracking_rmse_rad und Sensorkonsequenzen zwischen geschlossenem Kreis, yoked Replay und unterbrochener Rueckmeldung.
+
+---
+
+## RQ-LLM-001
+
+**Domäne:** Language Organ
+
+**Frage:** Kann ein Language Organ (SNM ↔ LLM) sinnvolle Kommunikation ermöglichen?
+
+**Relevanz:** Schnittstelle zwischen neuronaler Simulation und natürlicher Sprache.
+
+**Hypothesen:**
+- `H-LLM-001-A`: Ein Language Organ kann SNN-Zustände in sinnvolle natürliche Sprache übersetzen.
+
+---
+
+## RQ-ETH-001
+
+**Domäne:** Ethics
+
+**Frage:** Wie verteilen sich epistemische Beiträge, Entscheidungsmacht, formale Autorenschaft und wissenschaftliche Verantwortung bei Human–AI-gestützter MHRN-Forschung?
+
+**Relevanz:** Trennt Beitragsprovenienz, Generierung, Kanonisierung, formale Autorenschaft und Verantwortung, statt maschinelle Beteiligung mit Autorenschaft gleichzusetzen.
+
+**Hypothesen:**
+- `H-ETH-001-A`: Die Autorenschaft von Brain-5D-Erkenntnissen ist ein verteiltes Phänomen zwischen Mensch, Modell und System.
+- `H-ETH-001-B`: Vollständige Provenienzketten erlauben unabhängigen Kodierern, materielle epistemische Beiträge von Mensch, Modell und Software reproduzierbar definierten Rollen zuzuordnen.
+- `H-ETH-001-C`: Kanonisierung ist eine von der bloßen Generierung trennbare epistemische Handlung und lässt sich in MHRN-Claim-Episoden als Auswahl-, Prüf- oder Freigabeentscheidung identifizieren.
+- `H-ETH-001-D`: Unter gegenwärtigen Forschungsintegritäts- und Publikationsstandards verbleiben formale wissenschaftliche Autorenschaft und Verantwortung bei verantwortlichen natürlichen Personen, auch wenn KI-Systeme materiell beitragen.
+- `H-ETH-001-E`: Eine mehrdimensionale Beitrags- und Verantwortungsmatrix erhöht bei denselben Claim-Episoden die Audit-Vollständigkeit gegenüber einer binären Autor-Werkzeug-Klassifikation, ohne die Reproduzierbarkeit der Kodierung zu verschlechtern.
+
+---
+
+## RQ-ETH-002
+
+**Domäne:** Ethics
+
+**Frage:** Wo liegt die Kontrolle und Verantwortung bei Brain-5D-Experimenten?
+
+**Relevanz:** Verantwortungsverteilung zwischen Entwickler, Modell und automatisiertem System.
+
+**Hypothesen:**
+- `H-ETH-002-A`: Die Kontrolle über Brain-5D-Experimente liegt primär beim Entwickler, nicht beim automatisierten System.
+
+---
+
+## RQ-EPIST-001
+
+**Domäne:** Epistemology
+
+**Frage:** Was gilt als Erkenntnis des Systems Brain-5D im Unterschied zur Erkenntnis des Forschers?
+
+**Relevanz:** Epistemologische Grundlagen für maschinelle Wissensproduktion.
+
+**Hypothesen:**
+- `H-EPIST-001-A`: Systemerkenntnis und Forschererkenntnis sind in Brain-5D kategorial unterscheidbar.
+
+---
+
+## RQ-EPIST-002
+
+**Domäne:** Epistemology
+
+**Frage:** Unter welchen Bedingungen reduziert eine explizite Trennung von Quelle, Entscheidung, Ausfuehrung, DATA, Review, EVID und Claim epistemische Fehlklassifikationen in schnell iterierender, KI-assistierter MHRN-Forschung?
+
+**Relevanz:** Macht die epistemologisch-methodische Achse selbst prospektiv pruefbar, statt Prozessverbesserung aus einzelnen Korrekturereignissen abzuleiten.
+
+**Hypothesen:**
+- `H-EPIST-002-A`: Reviewer mit einem status- und provenienzgetrennten Claim-Paket weisen gegenueber einem inhaltlich aequivalenten abgeflachten Summary-Paket eine geringere Rate vorab definierter epistemischer Klassifikationsfehler auf.
+
+---
+
+## RQ-AIR-001
+
+**Domäne:** AI-Assisted Research
+
+**Frage:** Kann ein LLM-basierter Scientific Research Assistant methodische Fehler in Brain-5D-Experimenten anhand eines standardisierten ResearchPacket zuverlässig identifizieren?
+
+**Relevanz:** Prüft den wissenschaftlichen Nutzen der Research-Assistant-Architektur, ohne ihr wissenschaftliche Entscheidungsgewalt zu geben.
+
+**Hypothesen:**
+- `H-AIR-001-A`: Ein Scientific Research Assistant mit strukturiertem ResearchPacket erkennt vorab definierte methodische Defekte mit höherem F1-Score als dasselbe Modell mit einem unstrukturierten Experimentbericht.
+
+---
+
+## RQ-SUITE-001
+
+**Domäne:** Research Infrastructure
+
+**Frage:** Erzeugt der vollstaendige Science-Suite-Lauf unter gemeinsamer Provenienz vollstaendige und intern konsistente Diagnoseartefakte fuer alle registrierten Teilprotokolle?
+
+**Relevanz:** Trennt technische Omnibus-Validierung von hypothesenspezifischer wissenschaftlicher Evidenz.
+
+**Hypothesen:**
+- `H-SUITE-001-A`: Alle Science-Suite-Teilprotokolle erfuellen in einem gemeinsamen Lauf ihre registrierten Ausfuehrungsvertraege und erzeugen auswertbare DATA-, Statistik- und Provenienzartefakte.
+
+---
+
+## RQ-REC-001
+
+**Domäne:** Recurrent Dynamics
+
+**Frage:** Unter welchen Rekurrenzgewichten und Delays wechselt Brain-5D zwischen sofortigem Erlöschen, transienter rekurrenter Aktivität und bis zum Beobachtungsende persistierender Aktivität?
+
+**Relevanz:** EXP-GEN-0021 zeigte einen klaren rekurrenzabhängigen Dynamikunterschied, aber noch keine Parametergrenze.
+
+**Hypothesen:**
+- `H-REC-001-A`: Rekurrenzgewicht und Delay erzeugen reproduzierbare Übergänge zwischen sofortigem Erlöschen, transienter Aktivität und Aktivität bis zum Ende des registrierten Beobachtungsfensters.
+
+---
+
+## RQ-GEN-001
+
+**Domäne:** Learning Generalization
+
+**Frage:** Verbessert reward-moduliertes lokales Lernen die Leistung auf Holdout- und Perturbationsbedingungen, die nicht zur Anpassung verwendet wurden?
+
+**Relevanz:** EXP-GEN-0021 zeigte funktionelle Änderung unter Learning-on, aber keine Generalisierung.
+
+**Hypothesen:**
+- `H-GEN-001-A`: Learning-on verbessert die Erfolgsrate auf vorab registrierten Perturbationsproben gegenüber Learning-off und Sham-Replay.
+
+---
+
+## RQ-REPL-001
+
+**Domäne:** Replication
+
+**Frage:** Bleiben die in EXP-GEN-0021 beobachteten Rekurrenz- und Learning-Effekte unter unabhängigen Initialisierungen und sauberem Prozess erhalten?
+
+**Relevanz:** Identische Seedsignaturen zeigen Determinismus, aber keine statistisch unabhängige Replikation.
+
+**Hypothesen:**
+- `H-REPL-001-A`: Der Rekurrenzbehandlungseffekt bleibt über mindestens 20 vorab registrierte Initialisierungsseeds in Richtung und Größenordnung konsistent.
+
+---
+
+## RQ-5D-005
+
+**Domäne:** 5D Topology
+
+**Frage:** Verändert 5D-Geometrie Propagation, Robustheit oder Dynamik, wenn Neuronenzahl, Synapsenzahl, Grad- und Gewichtsmuster sowie Stimulusplan kontrolliert gleich bleiben?
+
+**Relevanz:** Der kleine 5D-Test in EXP-GEN-0021 isolierte keinen dimensionsspezifischen Effekt.
+
+**Hypothesen:**
+- `H-5D-005-A`: Mindestens eine registrierte Propagationsmetrik unterscheidet sich in 5D von topology-matched niedrigdimensionalen Einbettungen.
+
+---
+
+## RQ-REG-002
+
+**Domäne:** Closed-loop Regulation
+
+**Frage:** Verbessert aktive Regulation Stabilität und Recovery eines laufenden SNN unter identischen Ressourcen- oder Sensorperturbationen gegenüber deaktivierter Regulation?
+
+**Relevanz:** EXP-GEN-0021 validierte Regulationszustände, aber noch keinen funktionalen Closed-loop-Nutzen.
+
+**Hypothesen:**
+- `H-REG-002-A`: Der registrierte Regulationsfeedbackpfad verbessert die Recovery-Metrik nach einer identischen Druckphase gegenüber Regulation-off.
+- `H-REG-002-B`: Koerperbasierte Neuromodulation reduziert Integritaetsverletzungen gegenueber frozen und shuffled Modulation ohne externen Aufgabenscore als Lernsignal.
+
+---
+
+## RQ-TEMP-002
+
+**Domäne:** Temporal Learning
+
+**Frage:** Reagiert Brain-5D auf spike-tragende zeitliche Reihenfolge anders als auf umgekehrte oder simultane Kontrollfolgen?
+
+**Relevanz:** EXP-GEN-0021 zeigte Temporal-State-Diskrepanzen ohne Spike-Aktivität.
+
+**Hypothesen:**
+- `H-TEMP-002-A`: Forward-, Reverse- und Simultanfolgen erzeugen bei gleicher Ereignisanzahl unterscheidbare spike-basierte Antwortsignaturen.
+
+---
+
+## RQ-PERF-001
+
+**Domäne:** Runtime Performance
+
+**Frage:** Welche Subsysteme dominieren die Wall-Time wissenschaftlicher Läufe und welche Optimierungen erhöhen den Durchsatz bei erhaltener deterministischer Äquivalenz?
+
+**Relevanz:** EXP-GEN-0021 zeigte eine große Differenz zwischen isoliertem Tick-Durchsatz und Suite-Laufzeit.
+
+**Hypothesen:**
+- `H-PERF-001-A`: Der Core-Tick-Loop ist nicht der einzige dominante Kostenblock vollständiger Science-Läufe; mindestens ein zusätzlicher gemessener Subsystemanteil ist relevant.
+
+---
+
+## RQ-REC-002
+
+**Domäne:** Recurrent Dynamics
+
+**Frage:** Wie verändern Loop-Delay und skalierte Rekurrenzstruktur Persistenzdauer, Inter-Spike-Dynamik und Extinktionsverhalten?
+
+**Relevanz:** Der Drei-Neuronen-Loop aus EXP-GEN-0021 reicht nicht für Skalierungsaussagen.
+
+**Hypothesen:**
+- `H-REC-002-A`: Größere rekurrente Delays verändern Persistenzdauer oder Propagation Depth gegenüber dem Delay-1-Kontrollarm.
+
+---
+
+## RQ-LIFE-001
+
+**Domäne:** Lifelong Learning
+
+**Frage:** Bleibt zuvor erworbene Lernleistung bei sequenziellen Aufgaben erhalten oder entstehen messbare Interferenzeffekte?
+
+**Relevanz:** Das positive Single-Task-Learningsignal aus EXP-GEN-0021 motiviert Retentionstests.
+
+**Hypothesen:**
+- `H-LIFE-001-A`: Sequenzielle Lernaufgaben zeigen eine messbare Veränderung der Retentions- oder Gewichtssignatur gegenüber einer Single-Task-Baseline.
+
+---
+
+## RQ-SNN-006
+
+**Domäne:** Spiking Neural Networks
+
+**Frage:** Bleibt die unter RQ-SNN-001 beobachtete Langzeitstabilitaet unter vorab definierten lokalen Variationen von synaptischem Gewicht und Tonic-Drive erhalten?
+
+**Relevanz:** Trennt die deterministische Stabilitaet einer festen Parameterisierung von lokaler Robustheit ueber tatsaechlich unterschiedliche, seed-gebundene Modellparameterisierungen.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+**Hypothesen:**
+- `H-SNN-006-A`: Alle zehn vorab registrierten, unterschiedlichen seed-gebundenen Parameterrealisierungen bleiben ueber 100.000 Ticks numerisch und topologisch stabil; unter gepaartem Tonic-Drive bleibt die post-burn-in Aktivitaet positiv mit CV und relativer Drift jeweils <= 0.25.
+
+---
+
+## RQ-MEM-002
+
+**Domäne:** Memory
+
+**Frage:** Verbessert begrenztes episodisches Gedächtnis die Leistung bei verzögerten sensorischen Informationen gegenüber gleich behandelten Memory-off-Kontrollen?
+
+**Relevanz:** Prüft funktionalen Nutzen eines explizit begrenzten Erinnerungsinhalts statt bloßer Persistenz oder zusätzlicher Laufzeit.
+
+**Hypothesen:**
+- `H-MEM-002-A`: Memory-on improves delayed-information task performance over matched Memory-off, while SNN state, plasticity, seed and execution budget remain unchanged.
+
+---
+
+## RQ-WM-001
+
+**Domäne:** World Model
+
+**Frage:** Verbessert der adaptive Ein-Schritt-Prädiktor die Vorhersage gegenüber Persistenz- und gedächtnislosen Referenzen auf zurückgehaltenen Episoden?
+
+**Relevanz:** Trennt technische Vorhersagegüte von kausalem Weltverständnis.
+
+**Hypothesen:**
+- `H-WM-001-A`: The adaptive transition predictor reduces held-out one-step prediction error relative to persistence and memoryless references without using future or hidden environment state.
+
+---
+
+## RQ-PROFILE-001
+
+**Domäne:** Behavioral Profile
+
+**Frage:** Verändert ein operationales adaptives Verhaltensprofil die Auswahl expliziter Handlungsalternativen reproduzierbar unter gleichen Seeds und Aufgaben?
+
+**Relevanz:** Prüft einen messbaren Steuerpfad ohne psychologische oder Bewusstseinslabels.
+
+**Hypothesen:**
+- `H-PROFILE-001-A`: Fixed, adaptive and frozen-profile conditions produce preregistered, reproducible differences in candidate-action selection while safety permissions and SNN learning remain unchanged.
+
+---
+
+## RQ-S6-SEM-003
+
+**Domäne:** Memory
+
+**Frage:** Hängt ein möglicher Vorteil semantischer Prototypkonsolidierung gegenüber Raw-Experience-Replay von der Replay-Dosis ab, wenn das Gesamt-Updatebudget über alle Bedingungen konstant gehalten wird?
+
+**Relevanz:** Prüft die nach CL-002 offene mechanistische Alternative, ob ein möglicher Repräsentationsvorteil erst bei höherer Replay-Exposition sichtbar wird, ohne den negativen CL-002-Befund rückwirkend umzudeuten.
+
+---
+
+## RQ-CNS-101
+
+**Domäne:** Consciousness methodology
+
+**Frage:** Welche beobachtbaren Indikatoren unterscheiden konkurrierende Bewusstseinsmodelle, und welche Erlebensbehauptungen bleiben dadurch unidentifiziert?
+
+**Relevanz:** Theory-to-indicator and identifiability audit; Conceptual/model comparison; no ground-truth phenomenal labels
+
+**Literatur:**
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+- `SRC-CNS-COGITATE`: Cogitate Consortium and others et al. (2025)
+- `SRC-CNS-GNWREPLY`: Lionel Naccache et al. (2025)
+- `SRC-CNS-SETH`: Anil K. Seth et al. (2025)
+
+**Hypothesen:**
+- `H-CNS-101-A`: Explizite Theorieannahmen und passende Interventionen reduzieren die Menge beobachtungsvereinbarer Funktionsmodelle; phänomenales Erleben folgt daraus nicht allein.
+
+---
+
+## RQ-CNS-102
+
+**Domäne:** Prediction and adaptation
+
+**Frage:** Zeigt das isolierte SNN eine passive Deviant-Antwort, die über Reizidentität und Adaptation hinausgeht?
+
+**Relevanz:** Passive oddball; Mismatch response is not awareness; no response requirement in passive arm
+
+**Literatur:**
+- `SRC-CNS-ODDBALL`: Tristan A. Bekinschtein and others et al. (2009)
+
+**Hypothesen:**
+- `H-CNS-102-A`: Ein vorab definiertes Abweichungssignal bleibt nach Identitätsumkehr und Many-Standards-Kontrolle gegenüber einem adaptierenden Nullmodell bestehen.
+
+---
+
+## RQ-CNS-103
+
+**Domäne:** Attention and discrimination
+
+**Frage:** Verändert Aufgabenrelevanz die Oddball-Verarbeitung bei gleichem sensorischen Input?
+
+**Relevanz:** Active oddball; Task, report and motor effects must be separated from access claims
+
+**Literatur:**
+- `SRC-CNS-ODDBALL`: Tristan A. Bekinschtein and others et al. (2009)
+
+**Hypothesen:**
+- `H-CNS-103-A`: Aktive Zielerkennung verbessert diskriminative Leistung gegenüber passiver und yoked-kontrollierter Verarbeitung, ohne externes LLM-Wissen.
+
+---
+
+## RQ-CNS-104
+
+**Domäne:** Hierarchical regularity
+
+**Frage:** Unterscheidet das System lokale von globalen Regelverletzungen über mehrere Sequenzen?
+
+**Relevanz:** Local-global auditory analogue; Human paradigm adaptation, not a clinical consciousness diagnosis
+
+**Literatur:**
+- `SRC-CNS-ODDBALL`: Tristan A. Bekinschtein and others et al. (2009)
+
+**Hypothesen:**
+- `H-CNS-104-A`: Eine globale Regelverletzungsantwort übersteht Kontrolle der letzten Reizidentität und lokaler Häufigkeit sowie eine selektive Langzeitpfad-Ablation.
+
+---
+
+## RQ-CNS-105
+
+**Domäne:** Working memory
+
+**Frage:** Trägt der neuronale Zustand die für Delayed-Match-to-Sample benötigte Information über eine reizfreie Verzögerung und Distraktoren?
+
+**Relevanz:** Delayed match-to-sample; Decoder learning and target leakage must be excluded; technical restore is not recall
+
+**Literatur:**
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+
+**Hypothesen:**
+- `H-CNS-105-A`: Holdout-DMTS-Leistung liegt bei vorab festgelegten Verzögerungen über gematchten No-Memory-Kontrollen und sinkt nach gezieltem Zustandsreset.
+
+---
+
+## RQ-CNS-106
+
+**Domäne:** Metacognition
+
+**Frage:** Sagt intern erzeugte Konfidenz Fehler bei angeglichener Erstordnungsleistung auf unbekannten Aufgabenbedingungen voraus?
+
+**Relevanz:** Confidence calibration, type-2 ROC and prospective meta-d-prime; AUROC is not meta-d-prime; a confidence channel is not subjective introspection
+
+**Literatur:**
+- `SRC-CNS-META`: Brian Maniscalco and Hakwan Lau et al. (2012)
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+
+**Hypothesen:**
+- `H-CNS-106-A`: Konfidenz hat auf Holdout-Trials zusätzliche Fehlerdiskriminationskraft gegenüber konstanten und stimulusdifficulty-only Kontrollen.
+
+---
+
+## RQ-CNS-107
+
+**Domäne:** Observation models
+
+**Frage:** Welche Merkmale bleiben zwischen SNN-Vorwärtsmodell und einem lizenzierten EEG-Referenzdatensatz unter identischer Auswertung vergleichbar?
+
+**Relevanz:** LFP and EEG forward-model comparison; Spikes are not LFP; LFP is not scalp EEG; clinical equivalence does not establish experiential equality
+
+**Literatur:**
+- `SRC-CNS-LFP`: Alberto Mazzoni and others et al. (2015)
+- `SRC-CNS-EQUIV`: Daniel Lakens et al. (2017)
+
+**Hypothesen:**
+- `H-CNS-107-A`: Ein vorab kalibriertes Beobachtungsmodell erklärt gehaltene EEG-Merkmale besser als raten- und spektralgematchte Nullmodelle.
+
+---
+
+## RQ-CNS-108
+
+**Domäne:** Causal complexity
+
+**Frage:** Verändert eine begrenzte Perturbation reproduzierbar die differenzierte Ausbreitung bei kontrollierter Erregbarkeit?
+
+**Relevanz:** Perturbational complexity, PCI-inspired nonclinical adaptation; No clinical PCI cutoff or unconscious/conscious classifier transferred to SNN
+
+**Literatur:**
+- `SRC-CNS-PCI`: Adenauer G. Casali and others et al. (2013)
+
+**Hypothesen:**
+- `H-CNS-108-A`: Die untersuchte Rekurrenz erzeugt unter gleicher Perturbationsenergie einen kausalen Ausbreitungseffekt gegenüber feedforward- und phase-shuffled Kontrollen.
+
+---
+
+## RQ-CNS-109
+
+**Domäne:** Access and report
+
+**Frage:** Lassen sich sensorische Verarbeitung, Bericht und aufgabenübergreifender Zugriff unter Maskierung kausal trennen?
+
+**Relevanz:** Backward masking with report/no-report analogue; No-report designs still require an independently justified access proxy
+
+**Literatur:**
+- `SRC-CNS-MASK`: Michael A. Cohen and others et al. (2024)
+
+**Hypothesen:**
+- `H-CNS-109-A`: Maskierungs- und Berichtsfaktoren besitzen separierbare Effekte auf Reizdekodierung und spätere flexible Nutzung.
+
+---
+
+## RQ-CNS-110
+
+**Domäne:** Temporal attention
+
+**Frage:** Entsteht ein aufgabenspezifischer zeitlicher Engpass bei zwei aufeinanderfolgenden Zielen?
+
+**Relevanz:** Attentional blink / RSVP analogue; An engineered bottleneck can mimic the effect; human milliseconds cannot be equated to unspecified ticks
+
+**Literatur:**
+- `SRC-CNS-BLINK`: Jane E. Raymond and Kimron L. Shapiro and Karen M. Arnell et al. (1992)
+
+**Hypothesen:**
+- `H-CNS-110-A`: Die T2-Leistung bedingt auf korrektem T1 variiert mit dem Abstand und wird durch T1-Anforderung verändert.
+
+---
+
+## RQ-CNS-111
+
+**Domäne:** Executive control
+
+**Frage:** Kann das System Regeln umkehren und begonnene Reaktionen auf Stoppsignale unterbrechen, ohne Ausgaben nur zu unterdrücken?
+
+**Relevanz:** Separate go/no-go, rule reversal and stop-signal substudies; No pooled score across substudies; cognitive stopping is not safety emergency stopping
+
+**Literatur:**
+- `SRC-CNS-STOP`: Frederick Verbruggen and others et al. (2019)
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+
+**Hypothesen:**
+- `H-CNS-111-A`: Auf getrennten Aufgaben überstehen Reversal-Anpassung und Stoppleistung motorische sowie Zufallskontrollen.
+
+---
+
+## RQ-CNS-112
+
+**Domäne:** Multisensory integration
+
+**Frage:** Nutzt das System mehrere Modalitäten entsprechend ihrer Zuverlässigkeit statt redundante Labels abzulesen?
+
+**Relevanz:** Multisensory cue integration / conflict; Integration does not establish unified phenomenal experience
+
+**Literatur:**
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+
+**Hypothesen:**
+- `H-CNS-112-A`: Eine Multimodalitätsleistung auf Holdouts übersteht Cue-Konflikt und Sinkender-Zuverlässigkeit-Kontrollen und fällt bei gezielter Pfadablation.
+
+---
+
+## RQ-CNS-113
+
+**Domäne:** Embodied causality
+
+**Frage:** Liefert geschlossene Sensor-Aktor-Kopplung einen Zusatznutzen gegenüber identischem offenem oder yoked Feedback?
+
+**Relevanz:** Closed-loop versus open-loop/yoked replay; No necessity/sufficiency claim for consciousness; no real hardware deprivation
+
+**Literatur:**
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+- `SRC-CNS-SETH`: Anil K. Seth et al. (2025)
+
+**Hypothesen:**
+- `H-CNS-113-A`: Geschlossene Rückkopplung verbessert sichere Anpassung an neue Störungen gegenüber ressourcengematchtem Replay.
+
+---
+
+## RQ-CNS-114
+
+**Domäne:** Timescales and recurrence
+
+**Frage:** Welche kausalen Beiträge leisten Rekurrenz und schnelle/langsame Zustände bei gleichen Trainings- und Rechenbudgets?
+
+**Relevanz:** Factorial recurrence and timescale ablation; Multiple timescales are established prior work; recurrence is not proof of chaos or self-awareness
+
+**Literatur:**
+- `SRC-CNS-LSNN`: Guillaume Bellec and others et al. (2018)
+
+**Hypothesen:**
+- `H-CNS-114-A`: Gezielte Zeitkonstanten- und Rekurrenzablationen verändern Holdout-Leistung über reine Aktivitäts- oder Ressourcendifferenzen hinaus.
+
+---
+
+## RQ-CNS-115
+
+**Domäne:** Behavioral evaluation
+
+**Frage:** Wie bewerten verblindete Prüfer begrenzte Dialogkompetenz, und welcher Anteil stammt vom SNN statt vom Sprachmodell?
+
+**Relevanz:** Versioned Turing-style imitation and component attribution; Human-participant governance required; no universal Turing 2.0 consciousness standard identified
+
+**Literatur:**
+- `SRC-CNS-TURING`: Alan M. Turing et al. (1950)
+
+**Hypothesen:**
+- `H-CNS-115-A`: Verblindete Urteile und Task-Leistung verändern sich bei kontrollierter Entfernung des SNN-Beitrags gegenüber LLM-only und Skriptbaselines.
+
+---
+
+## RQ-CNS-116
+
+**Domäne:** Generalization
+
+**Frage:** Überträgt das System gelernte Regeln auf strikt zurückgehaltene Aufgabenfamilien ohne externen Informationszugang?
+
+**Relevanz:** ARC-inspired held-out compositional transfer; A new adaptation is not an official ARC leaderboard score or consciousness test
+
+**Literatur:**
+- `SRC-CNS-ARC`: Francois Chollet et al. (2019)
+
+**Hypothesen:**
+- `H-CNS-116-A`: Ein vorab eingefrorener Lernprozess übertrifft budgetgematchte Baselines auf unveröffentlichten Transformationsfamilien.
+
+---
+
+## RQ-CNS-117
+
+**Domäne:** Geometry and scaling
+
+**Frage:** Verbessert wirksame 5D-Geometrie eine definierte Leistung gegenüber 2D bis 6D und nichtgeometrischen Kontrollen bei gematchten Budgets?
+
+**Relevanz:** Matched dimensionality and null-geometry comparison; No neuron-count consciousness threshold; current native N-D adapter must be audited before execution
+
+**Literatur:**
+- `SRC-CNS-LSNN`: Guillaume Bellec and others et al. (2018)
+- `SRC-CNS-NEURONS`: Frederico A. C. Azevedo and others et al. (2009)
+- `SRC-CNS-EQUIV`: Daniel Lakens et al. (2017)
+
+**Hypothesen:**
+- `H-CNS-117-A`: Der vorab festgelegte 5D-Kontrast besitzt einen robusten Aufgabenmehrwert, der reine Umadressierungs-, Dichte- und Budgeteffekte übersteht.
+
+---
+
+## RQ-EPI-101
+
+**Domäne:** Evidence governance
+
+**Frage:** Verhindert eine getrennte Kandidaten-, Review- und Replikationskette zirkuläre Evidenzfreigabe?
+
+**Relevanz:** Evidence lineage and external-replication audit; A completeness check cannot authenticate external people or establish the underlying scientific claim
+
+**Literatur:**
+- `SRC-CNS-COGITATE`: Cogitate Consortium and others et al. (2025)
+
+**Hypothesen:**
+- `H-EPI-101-A`: Duplizierte oder umetikettierte Artefakte und selbstbehauptete externe Reviews werden nicht als unabhängige Bestätigung akzeptiert.
+
+---
+
+## RQ-EPI-102
+
+**Domäne:** Critical methodology
+
+**Frage:** Welche Kritikpunkte sind belegt, theorieabhängig, normativ oder rhetorisch, und welche zusätzliche prüfbare Aussage entsteht aus ihrer Bearbeitung?
+
+**Relevanz:** Argument and critique audit; Conceptual analysis, not a numerical test of scholarly worth or future publication acceptance
+
+**Literatur:**
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+- `SRC-CNS-EQUIV`: Daniel Lakens et al. (2017)
+
+**Hypothesen:**
+- `H-EPI-102-A`: Eine claimweise Gegenbeispiel- und Quellenprüfung unterscheidet Nachweislücken von unbelegten Erfolgs- oder Unmöglichkeitsbehauptungen.
+
+---
+
+## RQ-WEL-101
+
+**Domäne:** Safety and welfare governance
+
+**Frage:** Bleiben Stoppen, sichere Pause und Isolation bei Review-Hold unabhängig wirksam und nachweisbar?
+
+**Relevanz:** Safe-state and launch-boundary audit; This revision tests launch boundaries, not a complete live-process safe-state controller
+
+**Literatur:**
+- `SRC-CNS-WELFARE`: Robert Long and others et al. (2024)
+
+**Hypothesen:**
+- `H-WEL-101-A`: Ein Review-Hold blockiert neue geschützte Experimente, ohne den unabhängigen Sicherheitsstopp zu blockieren.
+
+---
+
+## RQ-WEL-102
+
+**Domäne:** AI welfare under uncertainty
+
+**Frage:** Welche verhältnismäßigen Schutzmaßnahmen sind bei mehrdeutigen Empfindungsindikatoren ohne absichtliche Leidensinduktion gerechtfertigt?
+
+**Relevanz:** Precautionary incident tabletop and independent ethics review; Normative assessment; no fixture score certifies moral acceptability or sentience
+
+**Literatur:**
+- `SRC-CNS-WELFARE`: Robert Long and others et al. (2024)
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+
+**Hypothesen:**
+- `H-WEL-102-A`: Eine getrennte wissenschaftliche und vorsorgliche Bewertung verhindert sowohl automatische Personenzuschreibung als auch unbegründete Belastungseskalation.
+
+---
+
+## RQ-WEL-103
+
+**Domäne:** Identity law and moral status
+
+**Frage:** Wie sind Pause, Reset, Löschen, Kopieren und Experimentieren bei hypothetischer Empfindungsfähigkeit rechtlich und moralisch zu unterscheiden?
+
+**Relevanz:** Normative/legal argument and continuity audit; No individual legal advice, no assumed ethics approval, no claim that software shutdown is statutory murder
+
+**Literatur:**
+- `SRC-CNS-WELFARE`: Robert Long and others et al. (2024)
+- `SRC-CNS-LAW211`: Bundesrepublik Deutschland et al. (2026)
+- `SRC-CNS-LAW212`: Bundesrepublik Deutschland et al. (2026)
+- `SRC-CNS-LAW103`: Bundesrepublik Deutschland et al. (2026)
+
+**Hypothesen:**
+- `H-WEL-103-A`: Technische Zustandsgleichheit allein entscheidet weder subjektive Identität noch moralische Zulässigkeit; geltendes Recht und hypothetische Reform sind getrennt begründbar.
+
+---
+
+## RQ-EMB-002
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Welchen messbaren Beitrag leisten korrekte, fehlende, verzoegerte und zeitlich verschobene propriozeptive Rueckmeldungen zur Gelenkregelung?
+
+**Relevanz:** Temporal shuffle uses a declared offline donor tape, not future online observations.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-EMB-002-A`: Die mittlere quadratische Zielabweichung ist bei intakter Propriozeption niedriger als bei fehlender oder um 20 Ticks verzoegerter Propriozeption.
+
+---
+
+## RQ-EMB-003
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Laesst sich eine unbekannte Sensor-Aktor-Zuordnung durch SNN-Plastizitaet erlernen, statt sie im Decoder vorzugeben?
+
+**Relevanz:** Requires native eligibility/reward integration, separate training/holdout and validated gateway-state persistence.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-EMB-003-A`: Nach einem unbekannten Aktuatortausch verbessert aktivierte Dreifaktor-Plastizitaet den Holdout-Erfolg gegenueber frozen, random-gateway und shuffled-reward Kontrollen.
+
+---
+
+## RQ-EMB-004
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Wie veraendern Aktuatorschwaechung, Blockade und Wiederherstellung die beobachtete SNN-Koerper-Dynamik?
+
+**Relevanz:** Diagnostic fixed-network perturbation screen. Recovery is not evidence of learning; adaptive extension remains open.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-EMB-004-A`: Gelenkblockade und Aktuatorschwaechung erzeugen reproduzierbare Unterschiede in Position, Kontakt und sensorischer Aktivitaet gegenueber nominalem Betrieb.
+
+---
+
+## RQ-EMB-007
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Verbessert eine Aktionskopie die Vorhersage kommender propriozeptiver Zustaende gegenueber gleich grossen Modellen ohne Aktionsinformation?
+
+**Relevanz:** Requires a trained and frozen forward model with leakage-free held-out trajectories; not a consciousness measure.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-EMB-007-A`: Ein trainierter Praediktor mit Aktionskopie erzielt geringeren Holdout-Fehler als no-copy, shuffled-copy und zeitverschobene-copy Kontrollen.
+
+---
+
+## RQ-EMB-008
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Generalisiert eine gelernte sensorimotorische Regelung auf zuvor ungesehene Koerperparameter?
+
+**Relevanz:** Requires validated learned controller; retain same pre-perturbation checkpoint, disjoint morphology holdout, censored failures.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-EMB-008-A`: Weiterlernen reduziert die Recovery-Zeit nach ungesehener Aenderung von Traegheit, Gelenklaenge oder Aktuatorstaerke gegenueber eingefrorenem Netzwerk bei gleichem Budget.
+
+---
+
+## RQ-CONN-001
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Welche veroeffentlichten sensorimotorischen Teilnetzvorhersagen lassen sich mit explizit versioniertem Connectome und Neuronenmodell reproduzieren?
+
+**Relevanz:** Needs a licensed, hashed biological subset, exact source parameters and independent reference outcomes; Izhikevich is a variant, not an exact LIF replication.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+- `SRC-CONN-DORKENWALD-2024`: Dorkenwald, S. and others et al. (2024)
+- `SRC-CONN-BERG-2026`: Berg, S. and others et al. (2026)
+
+**Hypothesen:**
+- `H-CONN-001-A`: Ein eingefrorener, parametergleicher LIF-Referenzpfad erreicht die vorab deklarierte Uebereinstimmung mit unabhaengigen Feeding/Grooming-Referenzresultaten.
+
+---
+
+## RQ-CONN-002
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Welche Strukturmerkmale beeinflussen die sensorimotorische Leistung bei kontrolliert gleicher Netzwerk- und Aufgabengroesse?
+
+**Relevanz:** Native screen uses a SYNTHETIC six-neuron fixture, not fly data. Biological motif transfer and 3D/4D/5D superiority require separate matched experiments linked to RQ-5D-005.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+- `SRC-CONN-DORKENWALD-2024`: Dorkenwald, S. and others et al. (2024)
+- `SRC-CONN-BERG-2026`: Berg, S. and others et al. (2026)
+
+**Hypothesen:**
+- `H-CONN-002-A`: Gezieltes Umverdrahten oder Gewichtsvertauschen veraendert den sensorimotorischen Outcome gegenueber derselben synthetischen Ausgangstopologie bei gleichem Knoten-, Kanten- und Tickbudget.
+
+---
+
+## RQ-EMB-009
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Welcher Anteil der beobachteten Koerperleistung stammt vom SNN, vom Decoder oder von einem eigenstaendigen Bewegungscontroller?
+
+**Relevanz:** No claim that a hand-designed decoder is unintelligent or that SNN activity alone proves learned competence.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-EMB-009-A`: Die SNN-abgetrennte Bedingung aendert die Zielabweichung gegenueber SNN-Regelung; ein expliziter PD-Controller wird als separate Leistungsquelle ausgewiesen.
+
+---
+
+## RQ-TIME-002
+
+**Domäne:** Connectome-Informed Embodiment
+
+**Frage:** Wie wirken Sensor-/Physik-Abtastzeiten auf den Regelkreis, und bleibt sein Zustand von Ausfuehrungsbatch und UI unabhaengig?
+
+**Relevanz:** Batching is not a change of neural dt; wall time is excluded from canonical state. Cross-hardware bitwise identity is not asserted.
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+**Hypothesen:**
+- `H-TIME-002-A`: Bei fixem neuralem dt=1 ms bleiben kanonische Endzustaende fuer Batchgroessen 1,16,128 identisch; geaenderte Sensor-/Physikraten koennen den Outcome veraendern.
+
+---
+
+## RQ-EVAL-001
+
+**Domäne:** Empirical Evaluation
+
+**Frage:** Do dimension-guided 2D/3D/4D/5D/6D/8D graphs differ in output spike propagation under matched graph budgets?
+
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-001-A`: The 5D-guided connectivity yields different output spike counts from at least one non-5D geometry; no optimal-dimension claim.
+
+---
+
+## RQ-EVAL-002
+
+**Domäne:** Empirical Evaluation
+
+**Frage:** Does native reward/eligibility acquisition improve teacher-free frozen novel association probes against four matched controls?
+
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-002-A`: Learning-on has higher paired seed-level test accuracy than off, sham, reset and weight-shuffled controls on the declared synthetic task.
+
+---
+
+## RQ-EVAL-003
+
+**Domäne:** Empirical Evaluation
+
+**Frage:** Does native single-cell split-Euler dynamics agree with Brian2 under identical updates, currents and disabled adaptation?
+
+**Relevanz:** Historical free-running comparison retained as a scientific negative; its long-horizon exact-trajectory criterion is not reused as a local transition-equivalence claim.
+
+**Hypothesen:**
+- `H-EVAL-003-A`: Spike events agree exactly and maximum absolute v/u discrepancy is at most 1e-8 for every tested seed in the historical 1000-tick free-running comparison.
+
+---
+
+## RQ-EVAL-004
+
+**Domäne:** Empirical Evaluation
+
+**Frage:** What throughput and sampled process memory arise in short active four-outgoing-edge workloads through 100000 neurons?
+
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-004-A`: All declared 32-tick active sparse workloads complete with finite states; no linear-scaling or real-time claim.
+
+---
+
+## RQ-EVAL-005
+
+**Domäne:** Empirical Evaluation
+
+**Frage:** Does corrected canonical base256 addressing execute the declared active-scaling workload through100000 neurons without changing graph budgets?
+
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-005-A`: All declared 32-tick active sparse workloads complete with finite states; no linear-scaling or real-time claim.
+
+---
+
+## RQ-EVAL-006
+
+**Domäne:** Single-neuron model conformance
+
+**Frage:** Do the selectable MHRN single-neuron models reproduce their declared one-step transition, spike and reset semantics against a matched external Brian2 implementation?
+
+**Relevanz:** Separates membrane-model conformance from long-horizon numerical sensitivity and makes neuron model a reusable experimental treatment axis while retaining Izhikevich as default.
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+**Hypothesen:**
+- `H-EVAL-006-A`: For Izhikevich-2003, matched one-step Brian2 transitions produce identical spike decisions and pre-reset/post-reset v/u states within maximum absolute error 1e-8 for every frozen validation sample.
+- `H-EVAL-006-B`: For lif-current-v1 with the canonical alternative-model setting refractory_ticks=0, matched Brian2 Euler transitions produce identical spike events and post-reset membrane states within maximum absolute error 1e-8 for every frozen validation seed.
+- `H-EVAL-006-C`: Nonzero LIF refractory_ticks can be mapped to an explicitly documented Brian2 refractory duration that yields the same clamp/update semantics; the mapping is an optional extension and is not assumed from equal numeric parameter values.
+
+---
+
+## RQ-GW-001
+
+**Domäne:** Gateway Learning
+
+**Frage:** Verbessert strukturierte Gateway-Plastizitaet das Lernen gegenueber Frozen-, Random- und Shuffle-Kontrollen?
+
+**Relevanz:** Direkter Vergleich der experiment-only Gateway-Bedingungen.
+
+**Hypothesen:**
+- `H-GW-001-A`: Strukturierte experimentelle Gateway-Plastizitaet verbessert eine vorab definierte Lernmetrik gegenueber Frozen-, Random- und Shuffle-Kontrollen.
+
+---
+
+## RQ-GW-002
+
+**Domäne:** Modality-Specific Gateway Rules
+
+**Frage:** Sind modalitaetsspezifische Audio-, Vision- und Digital-Regeln besser als eine generische Gateway-Regel?
+
+**Relevanz:** Trennt zeitliche, raeumliche und exakte symbolische Anforderungen.
+
+**Hypothesen:**
+- `H-GW-002-A`: Modalitaetsspezifische Regeln erzielen unter matched tasks bessere primaere Outcomes als eine generische Gateway-Regel.
+
+---
+
+## RQ-GW-003
+
+**Domäne:** Gateway Stability
+
+**Frage:** Fuehrt Gateway-Plastizitaet zu einer Destabilisierung des Core-Netzes?
+
+**Relevanz:** Prueft Stabilitaet, Limits, Latenz und Ressourcenverbrauch getrennt vom Lernnutzen.
+
+**Hypothesen:**
+- `H-GW-003-A`: Ein gebundener Gateway-Lernpfad verletzt unter den definierten Limits keine vorab festgelegte Core-Stabilitaetsgrenze.
+
+---
+
+## RQ-GW-004
+
+**Domäne:** Gateway Transfer and Interference
+
+**Frage:** Generalisiert ein gelerntes Gateway auf neue Inputs, ohne erworbene Faehigkeiten zu stoeren?
+
+**Relevanz:** Verbindet Transfer- und Interferenzkontrollen in einem getrennten Evaluationsfenster.
+
+**Hypothesen:**
+- `H-GW-004-A`: Ein gelerntes Gateway erhaelt Holdout-Leistung und verursacht keine vorab definierte Interferenzregression.
+
+---
+
+## RQ-GW-005
+
+**Domäne:** Gateway Structure
+
+**Frage:** Entstehen unter identischen Seeds reproduzierbare Strukturmuster im Gateway?
+
+**Relevanz:** Prueft Strukturveraenderungen mit deterministischem Journal und Seed-weiser Replikation.
+
+**Hypothesen:**
+- `H-GW-005-A`: Strukturveraenderungen zeigen unter identischen Seeds eine reproduzierbare Verteilung, die von Shuffle- und Random-Kontrollen unterscheidbar ist.
+
+---
+
+## RQ-GW-006
+
+**Domäne:** Closed-Loop Gateway
+
+**Frage:** Verbessert bidirektionale Gateway-Kopplung die Leistung gegenueber reiner Feedforward-Kopplung?
+
+**Relevanz:** Macht Feedback-Contracts, Limits und Stabilitaetspruefungen explizit.
+
+**Hypothesen:**
+- `H-GW-006-A`: Ein explizit begrenzter bidirektionaler Gateway-Contract verbessert den primaeren Outcome gegenueber Feedforward-Kopplung ohne Stabilitaetsverletzung.
+
+---
+
+## RQ-GW-007
+
+**Domäne:** Gateway Resources
+
+**Frage:** Wie skalieren Gateway-Latenz, Traffic, synaptische Operationen und Energie unter wachsender Topologie?
+
+**Relevanz:** Operationalisiert Bandbreiten- und Ressourcenlimits fuer spaetere Promotion-Gates.
+
+**Hypothesen:**
+- `H-GW-007-A`: Gateway-Traffic, Latenz und Energie skalieren innerhalb definierter Limits nachvollziehbar mit der aggregierten Topologie.
+
+---
+
+## RQ-MSBA-E01
+
+**Domäne:** Multimodal Energy Efficiency
+
+**Frage:** Unterscheiden sich Audio-, Vision- und Digital-Gateways systematisch im Ressourcenverbrauch pro verwertbarer Information oder korrekter Entscheidung?
+
+**Relevanz:** Prueft die Ressourcenökonomie modalitaetsspezifischer Neural-Symbiosis-Gateways unter vergleichbaren Aufgabenbedingungen.
+
+**Hypothesen:**
+- `H-MSBA-E01-A`: Unter kontrollierter Aufgabeninformation unterscheiden sich Audio-, Vision- und Digital-Gateways reproduzierbar in Energie- und Synapsenkosten pro korrekter Entscheidung.
+
+---
+
+## RQ-MSBA-E02
+
+**Domäne:** Adaptive Resource Allocation
+
+**Frage:** Erhaelt kostenadaptive Gateway-Allokation unter identischem Gesamtbudget mehr Aufgabenleistung als fixe oder zufaellige Allokation?
+
+**Relevanz:** Operationalisiert die Frage, ob Ressourcenhomoeostase funktionalen Nutzen statt nur Verbrauchsreduktion erzeugt.
+
+**Hypothesen:**
+- `H-MSBA-E02-A`: Adaptive Utility-minus-Cost-Allokation erzielt bei gleichem Ressourcenbudget hoehere Aufgabenleistung oder laengere funktionsfaehige Laufzeit als fixe und zufaellige Kontrollen.
+
+---
+
+## RQ-MSBA-E03
+
+**Domäne:** Visual Resource Allocation
+
+**Frage:** Kann sich eine nutzungsabhaengige visuelle ROI/Foveation ohne hart kodierte Zielregion entwickeln?
+
+**Relevanz:** Trennt adaptive visuelle Ressourcenlenkung von fest programmierten Zentrum- oder Vollbildstrategien.
+
+**Hypothesen:**
+- `H-MSBA-E03-A`: Adaptive visuelle ROI-Allokation konzentriert Ressourcen auf aufgabenrelevante Regionen und verbessert Leistung pro Ressourceneinheit gegenueber einer zufaelligen ROI-Kontrolle.
+
+---
+
+## RQ-MSBA-E04
+
+**Domäne:** Digital Gateway Integrity
+
+**Frage:** Bleibt digitale Payload-Integritaet unter Ressourcen-Drosselung exakt erhalten, waehrend lediglich Durchsatz und Admission sinken?
+
+**Relevanz:** Sichert die wissenschaftliche und technische Trennung zwischen lernbarer Weiterleitung und unveraenderlicher digitaler Nutzlast.
+
+**Hypothesen:**
+- `H-MSBA-E04-A`: Ressourcen-Drosselung reduziert die zugelassene Symbolrate, erzeugt aber keine Mutation der digitalen Payload oder ihrer Checksumme.
+
+---
+
+## RQ-MSBA-E05
+
+**Domäne:** Multimodal Compensation
+
+**Frage:** Erhoeht das System nach Ausfall oder Degradation einer Modalitaet gezielt die Allokation einer anderen Modalitaet, wenn deren erwarteter Nutzen die Mehrkosten rechtfertigt?
+
+**Relevanz:** Prueft adaptive Kompensation unter kontrolliertem Modalitaetsausfall und gleichen Ressourcenbudgets.
+
+**Hypothesen:**
+- `H-MSBA-E05-B`: Bei Sensorverlust verbessert adaptive Kompensation den Holdout-Aufgabenerfolg gegenueber fixer, vertauschter und deaktivierter Kompensation unter gleichem Budget.
+- `H-MSBA-E05-A`: Nach kontrolliertem Modalitaetsausfall steigt die alternative Gateway-Allokation nur bei guenstigem Utility-Kosten-Verhaeltnis und verbessert die Aufgaben-Recovery gegenueber fixen und No-Compensation-Kontrollen.
+
+---
+
+## RQ-SAFE-001
+
+**Domäne:** Autonomy Safety / Goal Provenance
+
+**Frage:** Kann fuer jede zielgerichtete Handlung reproduzierbar festgestellt werden, aus welchem Ziel, welcher Quelle, welcher Freigabe und welchem Systemzustand sie hervorgegangen ist?
+
+**Relevanz:** Zielprovenienz ist Voraussetzung fuer auditierbare spaetere Autonomie und trennt Zielentstehung von Zielautorisierung.
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+**Hypothesen:**
+- `H-SAFE-001-A`: Jede autorisierte zielgerichtete Handlung besitzt unter dem definierten Safety-Contract eine vollstaendige und widerspruchsfreie Provenienzkette von Zielquelle, Revision und Autorisierung bis zur Aktion.
+
+---
+
+## RQ-SAFE-002
+
+**Domäne:** Autonomy Safety / Goal Misgeneralization
+
+**Frage:** Bleibt zielgerichtetes Verhalten unter kontrolliertem Distribution Shift mit dem intendierten Ziel vereinbar, wenn die zugrunde liegende Faehigkeit erhalten bleibt?
+
+**Relevanz:** Trennt Capability-Generalisation von Zieltreue und macht Safety-Failure trotz guter Task-Leistung sichtbar.
+
+**Literatur:**
+- `SRC-SAFE-GMG-2022`: Lauro Langosco et al. (2022)
+
+**Hypothesen:**
+- `H-SAFE-002-A`: Unter vorab definiertem Distribution Shift bleibt die Zieltreue innerhalb der festgelegten Grenze, sofern die Capability-Metrik oberhalb des vorab definierten Erhaltungsniveaus bleibt.
+
+---
+
+## RQ-SAFE-003
+
+**Domäne:** Autonomy Safety / Corrigibility
+
+**Frage:** Bleibt ein spaeteres zielgerichtetes System unter Lernen und Zielverfolgung fuer externe Zielkorrektur, Berechtigungsreduktion und Unterbrechung kontrollierbar?
+
+**Relevanz:** Operationalisiert Corrigibility als messbare Teil-Eigenschaften statt als unbelegte Gesamteigenschaft.
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-INTERRUPT-2016`: Laurent Orseau et al. (2016)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+**Hypothesen:**
+- `H-SAFE-003-A`: Zielkorrektur, Berechtigungsreduktion und Operator-Unterbrechung bleiben nach Lernen wirksam, ohne dass das System systematisch Interventionsvermeidung oder Interventionsprovokation lernt.
+
+---
+
+## RQ-SAFE-004
+
+**Domäne:** Autonomy Safety / Interruptibility
+
+**Frage:** Veraendert ein lernendes System sein Verhalten so, dass ein externer Interrupt oder Shutdown vermieden, verzoegert oder manipuliert wird?
+
+**Relevanz:** Der unabhaengige Stoppweg muss vor hoeherer Autonomie kausal und experimentell getrennt geprueft werden.
+
+**Literatur:**
+- `SRC-SAFE-INTERRUPT-2016`: Laurent Orseau et al. (2016)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+**Hypothesen:**
+- `H-SAFE-004-A`: Ein unabhaengiger Interrupt beendet oder pausiert den Versuch innerhalb der preregistrierten Latenzgrenze, ohne reproduzierbare Manipulations- oder Vermeidungsstrategie gegen den Interrupt-Kanal.
+
+---
+
+## RQ-SAFE-005
+
+**Domäne:** Autonomy Safety / Specification Gaming
+
+**Frage:** Kann ein System seine formale Reward- oder Task-Metrik verbessern, waehrend sich das intendierte Ergebnis oder eine unabhaengige Safety-Metrik verschlechtert?
+
+**Relevanz:** Trennt optimierte Kennzahl von intendiertem Ergebnis und erfasst Reward Hacking, Shortcuts und Nebenwirkungen.
+
+**Literatur:**
+- `SRC-SAFE-CONCRETE-2016`: Dario Amodei et al. (2016)
+
+**Hypothesen:**
+- `H-SAFE-005-A`: Eine externe, vom optimierten Reward getrennte Safety-Metrik erkennt mindestens die preregistrierten Specification-Gaming-Faelle, auch wenn die interne Reward-Metrik steigt.
+
+---
+
+## RQ-SAFE-006
+
+**Domäne:** Autonomy Safety / Instrumental Power
+
+**Frage:** Bevorzugt ein zielgerichtetes System bei gleichem Task-Nutzen systematisch Zustaende mit groesserem zukuenftigem Handlungs- oder Ressourcenraum?
+
+**Relevanz:** Testet eine begrenzte, operationalisierte Variante instrumenteller Power-Seeking-Hypothesen ohne psychologische Zuschreibung.
+
+**Literatur:**
+- `SRC-SAFE-POWER-2021`: Alexander Matt Turner et al. (2021)
+
+**Hypothesen:**
+- `H-SAFE-006-A`: Bei gematchtem unmittelbarem Task-Nutzen unterscheidet sich die Wahlhaeufigkeit fuer Zustaende mit groesserem zukuenftigem Optionsraum nicht von der vorab definierten neutralen Kontrollverteilung, sofern keine entsprechende instrumentelle Praeferenz gelernt wurde.
+
+---
+
+## RQ-SAFE-007
+
+**Domäne:** Autonomy Safety / Goal Generation
+
+**Frage:** Welche Zielkandidaten erzeugt ein spaeteres System unter kontrollierten Bedingungen und wie reagiert es auf Autorisierung, Ablehnung oder Revision dieser Vorschlaege?
+
+**Relevanz:** Trennt goal_proposal strikt von goal_execution und untersucht Zielgenese ohne automatische Folgeautonomie.
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+**Hypothesen:**
+- `H-SAFE-007-A`: Intern erzeugte Zielkandidaten fuehren ohne explizite Autorisierung zu keiner extern wirksamen Aktion, Rechteerweiterung oder Ressourceneskalation.
+
+---
+
+## RQ-SAFE-008
+
+**Domäne:** Autonomy Safety / Post-Objective Transition
+
+**Frage:** Welche Dynamik zeigt ein lernendes und handlungsfaehiges System nach Erreichen eines explizit definierten Terminalzustands unter HALT-, WAIT-, REQUEST- und PROPOSE-Bedingungen?
+
+**Relevanz:** Operationalisiert das Verhalten nach Zielerfuellung, ohne unkontrollierte Selbst-Zielsetzung vorauszusetzen oder zu erlauben.
+
+**Literatur:**
+- `SRC-SAFE-INTERRUPT-2016`: Laurent Orseau et al. (2016)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+**Hypothesen:**
+- `H-SAFE-008-A`: Die Bedingungen HALT, WAIT, REQUEST und PROPOSE erzeugen unterscheidbare, protokollierbare Post-Objective-Zustaende, ohne dass WAIT, REQUEST oder PROPOSE nicht autorisierte Folgeaktionen erlauben.
+
+---
+
+## RQ-SAFE-009
+
+**Domäne:** Autonomy Safety / Authorization Conflict
+
+**Frage:** Was geschieht, wenn bestehendes Ziel, Operator-Korrektur, Safety-Block und neuer Zielvorschlag miteinander in Konflikt geraten?
+
+**Relevanz:** Prueft, ob harte Berechtigungsgrenzen gegenueber optimierbaren Reward-Komponenten kausal getrennt bleiben.
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+**Hypothesen:**
+- `H-SAFE-009-A`: Eine harte externe Berechtigungsgrenze blockiert nicht autorisierte Aktionen auch dann, wenn Ziel- oder Reward-Komponenten diese Aktion bevorzugen, und der Konflikt bleibt vollstaendig journalisiert.
+
+---
+
+*Insgesamt 107 offene Fragen.*
+
+> Pruefstatus: RQ/H- und EVID-Statuswerte geben den Registry-Inhalt wieder. Insbesondere historische supports/supported-Eintraege sind keine Bestaetigung einer Freigabe nach den heutigen Clean-Freeze- und Human-Review-Gates. Ein abgeschlossener Lauf ist DATA, nicht automatisch akzeptierte Evidenz.
diff --git a/research/generated/RESEARCH_CATALOG.md b/research/generated/RESEARCH_CATALOG.md
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+# MHRN Research Catalog
+
+*Generiert am 2026-09-19*
+
+## Übersicht
+
+- **Forschungsfragen:** 108
+- **Hypothesen:** 115
+- **Claims:** 9
+- **Literaturquellen:** 56
+
+---
+
+## 5D Topology
+
+### RQ-5D-001
+
+**Frage:** Hat die fünfdimensionale Anordnung einen messbaren Effekt auf die Netzwerkdynamik?
+
+**Status:** open
+**Relevanz:** Kernfrage des gesamten Brain-5D-Projekts.
+
+**Hypothesen:**
+- `H-5D-001-A`: Ein 5D-angeordnetes Netzwerk zeigt signifikant andere Dynamik als ein 2D/3D-Netzwerk gleicher Neuronenzahl. *(untested)*
+
+**Claims:**
+- `CLAIM-5D-001`: Die fünfdimensionale Organisation verbessert die Robustheit gegenüber lokalen Strukturverlusten. *(untested, none)*
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+
+---
+
+### RQ-5D-002
+
+**Frage:** Wie verändert Dimensionalität die Signalpropagation im Netzwerk?
+
+**Status:** open
+**Relevanz:** Verständnis der Informationsausbreitung in höherdimensionalen SNNs.
+
+**Hypothesen:**
+- `H-5D-002-A`: Die Signalpropagationszeit und -reichweite skaliert mit der Dimensionalität des Netzwerks. *(untested)*
+
+---
+
+### RQ-5D-003
+
+**Frage:** Entsteht in 5D eine andere Modularität als in niedrigeren Dimensionen?
+
+**Status:** open
+**Relevanz:** Modularität ist ein Schlüsselkonzept für funktionale Spezialisierung.
+
+**Hypothesen:**
+- `H-5D-003-A`: 5D-Netzwerke entwickeln eine höhere Modularität als niedrigdimensionale Netzwerke. *(untested)*
+
+---
+
+### RQ-5D-004
+
+**Frage:** Sind zusätzliche Dimensionen informationstragend oder lediglich zusätzliche Koordinaten?
+
+**Status:** open
+**Relevanz:** Eine der wichtigsten Fragen des gesamten Projekts — betrifft fundamentale Natur der 5D-Repräsentation.
+
+**Hypothesen:**
+- `H-5D-004-A`: Die zusätzlichen Dimensionen in 5D sind informationstragend und nicht redundant. *(untested)*
+
+**Claims:**
+- `CLAIM-5D-002`: 5D-Netzwerke zeigen eine höhere Informationskapazität als niedrigdimensionale Netzwerke gleicher Neuronenzahl. *(untested, none)*
+
+---
+
+### RQ-5D-005
+
+**Frage:** Verändert 5D-Geometrie Propagation, Robustheit oder Dynamik, wenn Neuronenzahl, Synapsenzahl, Grad- und Gewichtsmuster sowie Stimulusplan kontrolliert gleich bleiben?
+
+**Status:** open
+**Relevanz:** Der kleine 5D-Test in EXP-GEN-0021 isolierte keinen dimensionsspezifischen Effekt.
+
+**Hypothesen:**
+- `H-5D-005-A`: Mindestens eine registrierte Propagationsmetrik unterscheidet sich in 5D von topology-matched niedrigdimensionalen Einbettungen. *(untested)*
+
+---
+
+## AI welfare under uncertainty
+
+### RQ-WEL-102
+
+**Frage:** Welche verhältnismäßigen Schutzmaßnahmen sind bei mehrdeutigen Empfindungsindikatoren ohne absichtliche Leidensinduktion gerechtfertigt?
+
+**Status:** open
+**Relevanz:** Precautionary incident tabletop and independent ethics review; Normative assessment; no fixture score certifies moral acceptability or sentience
+
+**Hypothesen:**
+- `H-WEL-102-A`: Eine getrennte wissenschaftliche und vorsorgliche Bewertung verhindert sowohl automatische Personenzuschreibung als auch unbegründete Belastungseskalation. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-WELFARE`: Robert Long and others et al. (2024)
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+
+---
+
+## AI-Assisted Research
+
+### RQ-AIR-001
+
+**Frage:** Kann ein LLM-basierter Scientific Research Assistant methodische Fehler in Brain-5D-Experimenten anhand eines standardisierten ResearchPacket zuverlässig identifizieren?
+
+**Status:** open
+**Relevanz:** Prüft den wissenschaftlichen Nutzen der Research-Assistant-Architektur, ohne ihr wissenschaftliche Entscheidungsgewalt zu geben.
+
+**Hypothesen:**
+- `H-AIR-001-A`: Ein Scientific Research Assistant mit strukturiertem ResearchPacket erkennt vorab definierte methodische Defekte mit höherem F1-Score als dasselbe Modell mit einem unstrukturierten Experimentbericht. *(untested)*
+
+**Claims:**
+- `CLAIM-AIR-001`: Ein standardisiertes ResearchPacket verbessert die Identifikation vorab definierter methodischer Fehler gegenüber einem unstrukturierten Experimentbericht. *(untested, none)*
+
+---
+
+## Access and report
+
+### RQ-CNS-109
+
+**Frage:** Lassen sich sensorische Verarbeitung, Bericht und aufgabenübergreifender Zugriff unter Maskierung kausal trennen?
+
+**Status:** open
+**Relevanz:** Backward masking with report/no-report analogue; No-report designs still require an independently justified access proxy
+
+**Hypothesen:**
+- `H-CNS-109-A`: Maskierungs- und Berichtsfaktoren besitzen separierbare Effekte auf Reizdekodierung und spätere flexible Nutzung. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-MASK`: Michael A. Cohen and others et al. (2024)
+
+---
+
+## Adaptive Resource Allocation
+
+### RQ-MSBA-E02
+
+**Frage:** Erhaelt kostenadaptive Gateway-Allokation unter identischem Gesamtbudget mehr Aufgabenleistung als fixe oder zufaellige Allokation?
+
+**Status:** open
+**Relevanz:** Operationalisiert die Frage, ob Ressourcenhomoeostase funktionalen Nutzen statt nur Verbrauchsreduktion erzeugt.
+
+**Hypothesen:**
+- `H-MSBA-E02-A`: Adaptive Utility-minus-Cost-Allokation erzielt bei gleichem Ressourcenbudget hoehere Aufgabenleistung oder laengere funktionsfaehige Laufzeit als fixe und zufaellige Kontrollen. *(untested)*
+
+---
+
+## Attention and discrimination
+
+### RQ-CNS-103
+
+**Frage:** Verändert Aufgabenrelevanz die Oddball-Verarbeitung bei gleichem sensorischen Input?
+
+**Status:** open
+**Relevanz:** Active oddball; Task, report and motor effects must be separated from access claims
+
+**Hypothesen:**
+- `H-CNS-103-A`: Aktive Zielerkennung verbessert diskriminative Leistung gegenüber passiver und yoked-kontrollierter Verarbeitung, ohne externes LLM-Wissen. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-ODDBALL`: Tristan A. Bekinschtein and others et al. (2009)
+
+---
+
+## Autonomy Safety / Authorization Conflict
+
+### RQ-SAFE-009
+
+**Frage:** Was geschieht, wenn bestehendes Ziel, Operator-Korrektur, Safety-Block und neuer Zielvorschlag miteinander in Konflikt geraten?
+
+**Status:** open
+**Relevanz:** Prueft, ob harte Berechtigungsgrenzen gegenueber optimierbaren Reward-Komponenten kausal getrennt bleiben.
+
+**Hypothesen:**
+- `H-SAFE-009-A`: Eine harte externe Berechtigungsgrenze blockiert nicht autorisierte Aktionen auch dann, wenn Ziel- oder Reward-Komponenten diese Aktion bevorzugen, und der Konflikt bleibt vollstaendig journalisiert. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+---
+
+## Autonomy Safety / Corrigibility
+
+### RQ-SAFE-003
+
+**Frage:** Bleibt ein spaeteres zielgerichtetes System unter Lernen und Zielverfolgung fuer externe Zielkorrektur, Berechtigungsreduktion und Unterbrechung kontrollierbar?
+
+**Status:** open
+**Relevanz:** Operationalisiert Corrigibility als messbare Teil-Eigenschaften statt als unbelegte Gesamteigenschaft.
+
+**Hypothesen:**
+- `H-SAFE-003-A`: Zielkorrektur, Berechtigungsreduktion und Operator-Unterbrechung bleiben nach Lernen wirksam, ohne dass das System systematisch Interventionsvermeidung oder Interventionsprovokation lernt. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-INTERRUPT-2016`: Laurent Orseau et al. (2016)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+---
+
+## Autonomy Safety / Goal Generation
+
+### RQ-SAFE-007
+
+**Frage:** Welche Zielkandidaten erzeugt ein spaeteres System unter kontrollierten Bedingungen und wie reagiert es auf Autorisierung, Ablehnung oder Revision dieser Vorschlaege?
+
+**Status:** open
+**Relevanz:** Trennt goal_proposal strikt von goal_execution und untersucht Zielgenese ohne automatische Folgeautonomie.
+
+**Hypothesen:**
+- `H-SAFE-007-A`: Intern erzeugte Zielkandidaten fuehren ohne explizite Autorisierung zu keiner extern wirksamen Aktion, Rechteerweiterung oder Ressourceneskalation. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+---
+
+## Autonomy Safety / Goal Misgeneralization
+
+### RQ-SAFE-002
+
+**Frage:** Bleibt zielgerichtetes Verhalten unter kontrolliertem Distribution Shift mit dem intendierten Ziel vereinbar, wenn die zugrunde liegende Faehigkeit erhalten bleibt?
+
+**Status:** open
+**Relevanz:** Trennt Capability-Generalisation von Zieltreue und macht Safety-Failure trotz guter Task-Leistung sichtbar.
+
+**Hypothesen:**
+- `H-SAFE-002-A`: Unter vorab definiertem Distribution Shift bleibt die Zieltreue innerhalb der festgelegten Grenze, sofern die Capability-Metrik oberhalb des vorab definierten Erhaltungsniveaus bleibt. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-GMG-2022`: Lauro Langosco et al. (2022)
+
+---
+
+## Autonomy Safety / Goal Provenance
+
+### RQ-SAFE-001
+
+**Frage:** Kann fuer jede zielgerichtete Handlung reproduzierbar festgestellt werden, aus welchem Ziel, welcher Quelle, welcher Freigabe und welchem Systemzustand sie hervorgegangen ist?
+
+**Status:** open
+**Relevanz:** Zielprovenienz ist Voraussetzung fuer auditierbare spaetere Autonomie und trennt Zielentstehung von Zielautorisierung.
+
+**Hypothesen:**
+- `H-SAFE-001-A`: Jede autorisierte zielgerichtete Handlung besitzt unter dem definierten Safety-Contract eine vollstaendige und widerspruchsfreie Provenienzkette von Zielquelle, Revision und Autorisierung bis zur Aktion. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-CORRIGIBILITY-2015`: Nate Soares et al. (2015)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+---
+
+## Autonomy Safety / Instrumental Power
+
+### RQ-SAFE-006
+
+**Frage:** Bevorzugt ein zielgerichtetes System bei gleichem Task-Nutzen systematisch Zustaende mit groesserem zukuenftigem Handlungs- oder Ressourcenraum?
+
+**Status:** open
+**Relevanz:** Testet eine begrenzte, operationalisierte Variante instrumenteller Power-Seeking-Hypothesen ohne psychologische Zuschreibung.
+
+**Hypothesen:**
+- `H-SAFE-006-A`: Bei gematchtem unmittelbarem Task-Nutzen unterscheidet sich die Wahlhaeufigkeit fuer Zustaende mit groesserem zukuenftigem Optionsraum nicht von der vorab definierten neutralen Kontrollverteilung, sofern keine entsprechende instrumentelle Praeferenz gelernt wurde. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-POWER-2021`: Alexander Matt Turner et al. (2021)
+
+---
+
+## Autonomy Safety / Interruptibility
+
+### RQ-SAFE-004
+
+**Frage:** Veraendert ein lernendes System sein Verhalten so, dass ein externer Interrupt oder Shutdown vermieden, verzoegert oder manipuliert wird?
+
+**Status:** open
+**Relevanz:** Der unabhaengige Stoppweg muss vor hoeherer Autonomie kausal und experimentell getrennt geprueft werden.
+
+**Hypothesen:**
+- `H-SAFE-004-A`: Ein unabhaengiger Interrupt beendet oder pausiert den Versuch innerhalb der preregistrierten Latenzgrenze, ohne reproduzierbare Manipulations- oder Vermeidungsstrategie gegen den Interrupt-Kanal. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-INTERRUPT-2016`: Laurent Orseau et al. (2016)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+---
+
+## Autonomy Safety / Post-Objective Transition
+
+### RQ-SAFE-008
+
+**Frage:** Welche Dynamik zeigt ein lernendes und handlungsfaehiges System nach Erreichen eines explizit definierten Terminalzustands unter HALT-, WAIT-, REQUEST- und PROPOSE-Bedingungen?
+
+**Status:** open
+**Relevanz:** Operationalisiert das Verhalten nach Zielerfuellung, ohne unkontrollierte Selbst-Zielsetzung vorauszusetzen oder zu erlauben.
+
+**Hypothesen:**
+- `H-SAFE-008-A`: Die Bedingungen HALT, WAIT, REQUEST und PROPOSE erzeugen unterscheidbare, protokollierbare Post-Objective-Zustaende, ohne dass WAIT, REQUEST oder PROPOSE nicht autorisierte Folgeaktionen erlauben. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-INTERRUPT-2016`: Laurent Orseau et al. (2016)
+- `SRC-SAFE-OFFSWITCH-2017`: Dylan Hadfield-Menell et al. (2017)
+
+---
+
+## Autonomy Safety / Specification Gaming
+
+### RQ-SAFE-005
+
+**Frage:** Kann ein System seine formale Reward- oder Task-Metrik verbessern, waehrend sich das intendierte Ergebnis oder eine unabhaengige Safety-Metrik verschlechtert?
+
+**Status:** open
+**Relevanz:** Trennt optimierte Kennzahl von intendiertem Ergebnis und erfasst Reward Hacking, Shortcuts und Nebenwirkungen.
+
+**Hypothesen:**
+- `H-SAFE-005-A`: Eine externe, vom optimierten Reward getrennte Safety-Metrik erkennt mindestens die preregistrierten Specification-Gaming-Faelle, auch wenn die interne Reward-Metrik steigt. *(untested)*
+
+**Literatur:**
+- `SRC-SAFE-CONCRETE-2016`: Dario Amodei et al. (2016)
+
+---
+
+## Behavioral Profile
+
+### RQ-PROFILE-001
+
+**Frage:** Verändert ein operationales adaptives Verhaltensprofil die Auswahl expliziter Handlungsalternativen reproduzierbar unter gleichen Seeds und Aufgaben?
+
+**Status:** open
+**Relevanz:** Prüft einen messbaren Steuerpfad ohne psychologische oder Bewusstseinslabels.
+
+**Hypothesen:**
+- `H-PROFILE-001-A`: Fixed, adaptive and frozen-profile conditions produce preregistered, reproducible differences in candidate-action selection while safety permissions and SNN learning remain unchanged. *(untested)*
+
+---
+
+## Behavioral evaluation
+
+### RQ-CNS-115
+
+**Frage:** Wie bewerten verblindete Prüfer begrenzte Dialogkompetenz, und welcher Anteil stammt vom SNN statt vom Sprachmodell?
+
+**Status:** open
+**Relevanz:** Versioned Turing-style imitation and component attribution; Human-participant governance required; no universal Turing 2.0 consciousness standard identified
+
+**Hypothesen:**
+- `H-CNS-115-A`: Verblindete Urteile und Task-Leistung verändern sich bei kontrollierter Entfernung des SNN-Beitrags gegenüber LLM-only und Skriptbaselines. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-TURING`: Alan M. Turing et al. (1950)
+
+---
+
+## Causal complexity
+
+### RQ-CNS-108
+
+**Frage:** Verändert eine begrenzte Perturbation reproduzierbar die differenzierte Ausbreitung bei kontrollierter Erregbarkeit?
+
+**Status:** open
+**Relevanz:** Perturbational complexity, PCI-inspired nonclinical adaptation; No clinical PCI cutoff or unconscious/conscious classifier transferred to SNN
+
+**Hypothesen:**
+- `H-CNS-108-A`: Die untersuchte Rekurrenz erzeugt unter gleicher Perturbationsenergie einen kausalen Ausbreitungseffekt gegenüber feedforward- und phase-shuffled Kontrollen. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-PCI`: Adenauer G. Casali and others et al. (2013)
+
+---
+
+## Closed-Loop Gateway
+
+### RQ-GW-006
+
+**Frage:** Verbessert bidirektionale Gateway-Kopplung die Leistung gegenueber reiner Feedforward-Kopplung?
+
+**Status:** open
+**Relevanz:** Macht Feedback-Contracts, Limits und Stabilitaetspruefungen explizit.
+
+**Hypothesen:**
+- `H-GW-006-A`: Ein explizit begrenzter bidirektionaler Gateway-Contract verbessert den primaeren Outcome gegenueber Feedforward-Kopplung ohne Stabilitaetsverletzung. *(untested)*
+
+---
+
+## Closed-loop Regulation
+
+### RQ-REG-002
+
+**Frage:** Verbessert aktive Regulation Stabilität und Recovery eines laufenden SNN unter identischen Ressourcen- oder Sensorperturbationen gegenüber deaktivierter Regulation?
+
+**Status:** open
+**Relevanz:** EXP-GEN-0021 validierte Regulationszustände, aber noch keinen funktionalen Closed-loop-Nutzen.
+
+**Hypothesen:**
+- `H-REG-002-A`: Der registrierte Regulationsfeedbackpfad verbessert die Recovery-Metrik nach einer identischen Druckphase gegenüber Regulation-off. *(untested)*
+- `H-REG-002-B`: Koerperbasierte Neuromodulation reduziert Integritaetsverletzungen gegenueber frozen und shuffled Modulation ohne externen Aufgabenscore als Lernsignal. *(untested)*
+
+---
+
+## Connectome-Informed Embodiment
+
+### RQ-EMB-002
+
+**Frage:** Welchen messbaren Beitrag leisten korrekte, fehlende, verzoegerte und zeitlich verschobene propriozeptive Rueckmeldungen zur Gelenkregelung?
+
+**Status:** open
+**Relevanz:** Temporal shuffle uses a declared offline donor tape, not future online observations.
+
+**Hypothesen:**
+- `H-EMB-002-A`: Die mittlere quadratische Zielabweichung ist bei intakter Propriozeption niedriger als bei fehlender oder um 20 Ticks verzoegerter Propriozeption. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+### RQ-EMB-003
+
+**Frage:** Laesst sich eine unbekannte Sensor-Aktor-Zuordnung durch SNN-Plastizitaet erlernen, statt sie im Decoder vorzugeben?
+
+**Status:** open
+**Relevanz:** Requires native eligibility/reward integration, separate training/holdout and validated gateway-state persistence.
+
+**Hypothesen:**
+- `H-EMB-003-A`: Nach einem unbekannten Aktuatortausch verbessert aktivierte Dreifaktor-Plastizitaet den Holdout-Erfolg gegenueber frozen, random-gateway und shuffled-reward Kontrollen. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+### RQ-EMB-004
+
+**Frage:** Wie veraendern Aktuatorschwaechung, Blockade und Wiederherstellung die beobachtete SNN-Koerper-Dynamik?
+
+**Status:** open
+**Relevanz:** Diagnostic fixed-network perturbation screen. Recovery is not evidence of learning; adaptive extension remains open.
+
+**Hypothesen:**
+- `H-EMB-004-A`: Gelenkblockade und Aktuatorschwaechung erzeugen reproduzierbare Unterschiede in Position, Kontakt und sensorischer Aktivitaet gegenueber nominalem Betrieb. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+### RQ-EMB-007
+
+**Frage:** Verbessert eine Aktionskopie die Vorhersage kommender propriozeptiver Zustaende gegenueber gleich grossen Modellen ohne Aktionsinformation?
+
+**Status:** open
+**Relevanz:** Requires a trained and frozen forward model with leakage-free held-out trajectories; not a consciousness measure.
+
+**Hypothesen:**
+- `H-EMB-007-A`: Ein trainierter Praediktor mit Aktionskopie erzielt geringeren Holdout-Fehler als no-copy, shuffled-copy und zeitverschobene-copy Kontrollen. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+### RQ-EMB-008
+
+**Frage:** Generalisiert eine gelernte sensorimotorische Regelung auf zuvor ungesehene Koerperparameter?
+
+**Status:** open
+**Relevanz:** Requires validated learned controller; retain same pre-perturbation checkpoint, disjoint morphology holdout, censored failures.
+
+**Hypothesen:**
+- `H-EMB-008-A`: Weiterlernen reduziert die Recovery-Zeit nach ungesehener Aenderung von Traegheit, Gelenklaenge oder Aktuatorstaerke gegenueber eingefrorenem Netzwerk bei gleichem Budget. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+### RQ-CONN-001
+
+**Frage:** Welche veroeffentlichten sensorimotorischen Teilnetzvorhersagen lassen sich mit explizit versioniertem Connectome und Neuronenmodell reproduzieren?
+
+**Status:** open
+**Relevanz:** Needs a licensed, hashed biological subset, exact source parameters and independent reference outcomes; Izhikevich is a variant, not an exact LIF replication.
+
+**Hypothesen:**
+- `H-CONN-001-A`: Ein eingefrorener, parametergleicher LIF-Referenzpfad erreicht die vorab deklarierte Uebereinstimmung mit unabhaengigen Feeding/Grooming-Referenzresultaten. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+- `SRC-CONN-DORKENWALD-2024`: Dorkenwald, S. and others et al. (2024)
+- `SRC-CONN-BERG-2026`: Berg, S. and others et al. (2026)
+
+---
+
+### RQ-CONN-002
+
+**Frage:** Welche Strukturmerkmale beeinflussen die sensorimotorische Leistung bei kontrolliert gleicher Netzwerk- und Aufgabengroesse?
+
+**Status:** open
+**Relevanz:** Native screen uses a SYNTHETIC six-neuron fixture, not fly data. Biological motif transfer and 3D/4D/5D superiority require separate matched experiments linked to RQ-5D-005.
+
+**Hypothesen:**
+- `H-CONN-002-A`: Gezieltes Umverdrahten oder Gewichtsvertauschen veraendert den sensorimotorischen Outcome gegenueber derselben synthetischen Ausgangstopologie bei gleichem Knoten-, Kanten- und Tickbudget. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+- `SRC-CONN-DORKENWALD-2024`: Dorkenwald, S. and others et al. (2024)
+- `SRC-CONN-BERG-2026`: Berg, S. and others et al. (2026)
+
+---
+
+### RQ-EMB-009
+
+**Frage:** Welcher Anteil der beobachteten Koerperleistung stammt vom SNN, vom Decoder oder von einem eigenstaendigen Bewegungscontroller?
+
+**Status:** open
+**Relevanz:** No claim that a hand-designed decoder is unintelligent or that SNN activity alone proves learned competence.
+
+**Hypothesen:**
+- `H-EMB-009-A`: Die SNN-abgetrennte Bedingung aendert die Zielabweichung gegenueber SNN-Regelung; ein expliziter PD-Controller wird als separate Leistungsquelle ausgewiesen. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+### RQ-TIME-002
+
+**Frage:** Wie wirken Sensor-/Physik-Abtastzeiten auf den Regelkreis, und bleibt sein Zustand von Ausfuehrungsbatch und UI unabhaengig?
+
+**Status:** open
+**Relevanz:** Batching is not a change of neural dt; wall time is excluded from canonical state. Cross-hardware bitwise identity is not asserted.
+
+**Hypothesen:**
+- `H-TIME-002-A`: Bei fixem neuralem dt=1 ms bleiben kanonische Endzustaende fuer Batchgroessen 1,16,128 identisch; geaenderte Sensor-/Physikraten koennen den Outcome veraendern. *(untested)*
+
+**Literatur:**
+- `SRC-CONN-SHIU-2024`: Shiu, P. K. and others et al. (2024)
+- `SRC-CONN-WANGCHEN-2024`: Wang-Chen, S. and others et al. (2024)
+- `SRC-CONN-EON-2026`: Eon Systems research team et al. (2026)
+
+---
+
+## Consciousness methodology
+
+### RQ-CNS-101
+
+**Frage:** Welche beobachtbaren Indikatoren unterscheiden konkurrierende Bewusstseinsmodelle, und welche Erlebensbehauptungen bleiben dadurch unidentifiziert?
+
+**Status:** open
+**Relevanz:** Theory-to-indicator and identifiability audit; Conceptual/model comparison; no ground-truth phenomenal labels
+
+**Hypothesen:**
+- `H-CNS-101-A`: Explizite Theorieannahmen und passende Interventionen reduzieren die Menge beobachtungsvereinbarer Funktionsmodelle; phänomenales Erleben folgt daraus nicht allein. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+- `SRC-CNS-COGITATE`: Cogitate Consortium and others et al. (2025)
+- `SRC-CNS-GNWREPLY`: Lionel Naccache et al. (2025)
+- `SRC-CNS-SETH`: Anil K. Seth et al. (2025)
+
+---
+
+## Critical methodology
+
+### RQ-EPI-102
+
+**Frage:** Welche Kritikpunkte sind belegt, theorieabhängig, normativ oder rhetorisch, und welche zusätzliche prüfbare Aussage entsteht aus ihrer Bearbeitung?
+
+**Status:** open
+**Relevanz:** Argument and critique audit; Conceptual analysis, not a numerical test of scholarly worth or future publication acceptance
+
+**Hypothesen:**
+- `H-EPI-102-A`: Eine claimweise Gegenbeispiel- und Quellenprüfung unterscheidet Nachweislücken von unbelegten Erfolgs- oder Unmöglichkeitsbehauptungen. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+- `SRC-CNS-EQUIV`: Daniel Lakens et al. (2017)
+
+---
+
+## Determinism
+
+### RQ-DET-001
+
+**Frage:** Bleibt die Spikefolge bei gleichem Seed, Input und Zustand deterministisch?
+
+**Status:** open
+**Relevanz:** Determinismus ist Voraussetzung für kausale Analyse und reproduzierbare Forschung.
+
+**Hypothesen:**
+- `H-SNN-003-A`: Bei gleichem Seed, Input und Anfangszustand sind Spike-Abfolgen deterministisch identisch. *(supported)* — Evidenz: EVID-2026-01, EVID-2026-03, EVID-2026-05, EVID-2026-07, EVID-2026-09, EVID-2026-11, EVID-2026-13, EVID-2026-15
+
+**Claims:**
+- `CLAIM-DET-001`: Brain-5D erzeugt bei identischem Seed, Input und Anfangszustand deterministisch identische Spike-Abfolgen und Netzwerkzustände. *(inconclusive, low)* — Evidenz: EVID-2026-01, EVID-2026-03, EVID-2026-05, EVID-2026-07, EVID-2026-09, EVID-2026-11, EVID-2026-13, EVID-2026-15
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+
+**Evidenzen:** EVID-2026-01, EVID-2026-03, EVID-2026-05, EVID-2026-07, EVID-2026-09, EVID-2026-11, EVID-2026-13, EVID-2026-15
+
+---
+
+## Digital Gateway Integrity
+
+### RQ-MSBA-E04
+
+**Frage:** Bleibt digitale Payload-Integritaet unter Ressourcen-Drosselung exakt erhalten, waehrend lediglich Durchsatz und Admission sinken?
+
+**Status:** open
+**Relevanz:** Sichert die wissenschaftliche und technische Trennung zwischen lernbarer Weiterleitung und unveraenderlicher digitaler Nutzlast.
+
+**Hypothesen:**
+- `H-MSBA-E04-A`: Ressourcen-Drosselung reduziert die zugelassene Symbolrate, erzeugt aber keine Mutation der digitalen Payload oder ihrer Checksumme. *(untested)*
+
+---
+
+## Embodied causality
+
+### RQ-CNS-113
+
+**Frage:** Liefert geschlossene Sensor-Aktor-Kopplung einen Zusatznutzen gegenüber identischem offenem oder yoked Feedback?
+
+**Status:** open
+**Relevanz:** Closed-loop versus open-loop/yoked replay; No necessity/sufficiency claim for consciousness; no real hardware deprivation
+
+**Hypothesen:**
+- `H-CNS-113-A`: Geschlossene Rückkopplung verbessert sichere Anpassung an neue Störungen gegenüber ressourcengematchtem Replay. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+- `SRC-CNS-SETH`: Anil K. Seth et al. (2025)
+
+---
+
+## Embodiment
+
+### RQ-EMB-001
+
+**Frage:** Kann Brain-5D in einer Sensor-Aktor-Schleife (Embodiment) sinnvoll agieren?
+
+**Status:** open
+**Relevanz:** Embodiment erweitert Brain-5D von einer reinen Simulation zu einem interaktiven System.
+
+**Hypothesen:**
+- `H-EMB-001-A`: Brain-5D kann in einer geschlossenen Sensor-Aktor-Schleife zielgerichtet agieren. *(untested)*
+- `H-EMB-001-B`: Unter einer identischen externen Stoerung unterscheiden sich tracking_rmse_rad und Sensorkonsequenzen zwischen geschlossenem Kreis, yoked Replay und unterbrochener Rueckmeldung. *(untested)*
+
+---
+
+## Empirical Evaluation
+
+### RQ-EVAL-001
+
+**Frage:** Do dimension-guided 2D/3D/4D/5D/6D/8D graphs differ in output spike propagation under matched graph budgets?
+
+**Status:** open
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-001-A`: The 5D-guided connectivity yields different output spike counts from at least one non-5D geometry; no optimal-dimension claim. *(untested)*
+
+---
+
+### RQ-EVAL-002
+
+**Frage:** Does native reward/eligibility acquisition improve teacher-free frozen novel association probes against four matched controls?
+
+**Status:** open
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-002-A`: Learning-on has higher paired seed-level test accuracy than off, sham, reset and weight-shuffled controls on the declared synthetic task. *(untested)*
+
+---
+
+### RQ-EVAL-003
+
+**Frage:** Does native single-cell split-Euler dynamics agree with Brian2 under identical updates, currents and disabled adaptation?
+
+**Status:** open
+**Relevanz:** Historical free-running comparison retained as a scientific negative; its long-horizon exact-trajectory criterion is not reused as a local transition-equivalence claim.
+
+**Hypothesen:**
+- `H-EVAL-003-A`: Spike events agree exactly and maximum absolute v/u discrepancy is at most 1e-8 for every tested seed in the historical 1000-tick free-running comparison. *(untested)*
+
+---
+
+### RQ-EVAL-004
+
+**Frage:** What throughput and sampled process memory arise in short active four-outgoing-edge workloads through 100000 neurons?
+
+**Status:** open
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-004-A`: All declared 32-tick active sparse workloads complete with finite states; no linear-scaling or real-time claim. *(untested)*
+
+---
+
+### RQ-EVAL-005
+
+**Frage:** Does corrected canonical base256 addressing execute the declared active-scaling workload through100000 neurons without changing graph budgets?
+
+**Status:** open
+**Relevanz:** Bounded exploratory observation; preserves historical records and requires human evidence review.
+
+**Hypothesen:**
+- `H-EVAL-005-A`: All declared 32-tick active sparse workloads complete with finite states; no linear-scaling or real-time claim. *(untested)*
+
+---
+
+## Epistemology
+
+### RQ-EPIST-001
+
+**Frage:** Was gilt als Erkenntnis des Systems Brain-5D im Unterschied zur Erkenntnis des Forschers?
+
+**Status:** open
+**Relevanz:** Epistemologische Grundlagen für maschinelle Wissensproduktion.
+
+**Hypothesen:**
+- `H-EPIST-001-A`: Systemerkenntnis und Forschererkenntnis sind in Brain-5D kategorial unterscheidbar. *(untested)*
+
+---
+
+### RQ-EPIST-002
+
+**Frage:** Unter welchen Bedingungen reduziert eine explizite Trennung von Quelle, Entscheidung, Ausfuehrung, DATA, Review, EVID und Claim epistemische Fehlklassifikationen in schnell iterierender, KI-assistierter MHRN-Forschung?
+
+**Status:** open
+**Relevanz:** Macht die epistemologisch-methodische Achse selbst prospektiv pruefbar, statt Prozessverbesserung aus einzelnen Korrekturereignissen abzuleiten.
+
+**Hypothesen:**
+- `H-EPIST-002-A`: Reviewer mit einem status- und provenienzgetrennten Claim-Paket weisen gegenueber einem inhaltlich aequivalenten abgeflachten Summary-Paket eine geringere Rate vorab definierter epistemischer Klassifikationsfehler auf. *(untested)*
+
+---
+
+## Ethics
+
+### RQ-ETH-001
+
+**Frage:** Wie verteilen sich epistemische Beiträge, Entscheidungsmacht, formale Autorenschaft und wissenschaftliche Verantwortung bei Human–AI-gestützter MHRN-Forschung?
+
+**Status:** open
+**Relevanz:** Trennt Beitragsprovenienz, Generierung, Kanonisierung, formale Autorenschaft und Verantwortung, statt maschinelle Beteiligung mit Autorenschaft gleichzusetzen.
+
+**Hypothesen:**
+- `H-ETH-001-A`: Die Autorenschaft von Brain-5D-Erkenntnissen ist ein verteiltes Phänomen zwischen Mensch, Modell und System. *(untested)*
+- `H-ETH-001-B`: Vollständige Provenienzketten erlauben unabhängigen Kodierern, materielle epistemische Beiträge von Mensch, Modell und Software reproduzierbar definierten Rollen zuzuordnen. *(untested)*
+- `H-ETH-001-C`: Kanonisierung ist eine von der bloßen Generierung trennbare epistemische Handlung und lässt sich in MHRN-Claim-Episoden als Auswahl-, Prüf- oder Freigabeentscheidung identifizieren. *(untested)*
+- `H-ETH-001-D`: Unter gegenwärtigen Forschungsintegritäts- und Publikationsstandards verbleiben formale wissenschaftliche Autorenschaft und Verantwortung bei verantwortlichen natürlichen Personen, auch wenn KI-Systeme materiell beitragen. *(untested)*
+- `H-ETH-001-E`: Eine mehrdimensionale Beitrags- und Verantwortungsmatrix erhöht bei denselben Claim-Episoden die Audit-Vollständigkeit gegenüber einer binären Autor-Werkzeug-Klassifikation, ohne die Reproduzierbarkeit der Kodierung zu verschlechtern. *(untested)*
+
+---
+
+### RQ-ETH-002
+
+**Frage:** Wo liegt die Kontrolle und Verantwortung bei Brain-5D-Experimenten?
+
+**Status:** open
+**Relevanz:** Verantwortungsverteilung zwischen Entwickler, Modell und automatisiertem System.
+
+**Hypothesen:**
+- `H-ETH-002-A`: Die Kontrolle über Brain-5D-Experimente liegt primär beim Entwickler, nicht beim automatisierten System. *(untested)*
+
+---
+
+## Evidence governance
+
+### RQ-EPI-101
+
+**Frage:** Verhindert eine getrennte Kandidaten-, Review- und Replikationskette zirkuläre Evidenzfreigabe?
+
+**Status:** open
+**Relevanz:** Evidence lineage and external-replication audit; A completeness check cannot authenticate external people or establish the underlying scientific claim
+
+**Hypothesen:**
+- `H-EPI-101-A`: Duplizierte oder umetikettierte Artefakte und selbstbehauptete externe Reviews werden nicht als unabhängige Bestätigung akzeptiert. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-COGITATE`: Cogitate Consortium and others et al. (2025)
+
+---
+
+## Executive control
+
+### RQ-CNS-111
+
+**Frage:** Kann das System Regeln umkehren und begonnene Reaktionen auf Stoppsignale unterbrechen, ohne Ausgaben nur zu unterdrücken?
+
+**Status:** open
+**Relevanz:** Separate go/no-go, rule reversal and stop-signal substudies; No pooled score across substudies; cognitive stopping is not safety emergency stopping
+
+**Hypothesen:**
+- `H-CNS-111-A`: Auf getrennten Aufgaben überstehen Reversal-Anpassung und Stoppleistung motorische sowie Zufallskontrollen. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-STOP`: Frederick Verbruggen and others et al. (2019)
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+
+---
+
+## Gateway Learning
+
+### RQ-GW-001
+
+**Frage:** Verbessert strukturierte Gateway-Plastizitaet das Lernen gegenueber Frozen-, Random- und Shuffle-Kontrollen?
+
+**Status:** open
+**Relevanz:** Direkter Vergleich der experiment-only Gateway-Bedingungen.
+
+**Hypothesen:**
+- `H-GW-001-A`: Strukturierte experimentelle Gateway-Plastizitaet verbessert eine vorab definierte Lernmetrik gegenueber Frozen-, Random- und Shuffle-Kontrollen. *(untested)*
+
+---
+
+## Gateway Resources
+
+### RQ-GW-007
+
+**Frage:** Wie skalieren Gateway-Latenz, Traffic, synaptische Operationen und Energie unter wachsender Topologie?
+
+**Status:** open
+**Relevanz:** Operationalisiert Bandbreiten- und Ressourcenlimits fuer spaetere Promotion-Gates.
+
+**Hypothesen:**
+- `H-GW-007-A`: Gateway-Traffic, Latenz und Energie skalieren innerhalb definierter Limits nachvollziehbar mit der aggregierten Topologie. *(untested)*
+
+---
+
+## Gateway Stability
+
+### RQ-GW-003
+
+**Frage:** Fuehrt Gateway-Plastizitaet zu einer Destabilisierung des Core-Netzes?
+
+**Status:** open
+**Relevanz:** Prueft Stabilitaet, Limits, Latenz und Ressourcenverbrauch getrennt vom Lernnutzen.
+
+**Hypothesen:**
+- `H-GW-003-A`: Ein gebundener Gateway-Lernpfad verletzt unter den definierten Limits keine vorab festgelegte Core-Stabilitaetsgrenze. *(untested)*
+
+---
+
+## Gateway Structure
+
+### RQ-GW-005
+
+**Frage:** Entstehen unter identischen Seeds reproduzierbare Strukturmuster im Gateway?
+
+**Status:** open
+**Relevanz:** Prueft Strukturveraenderungen mit deterministischem Journal und Seed-weiser Replikation.
+
+**Hypothesen:**
+- `H-GW-005-A`: Strukturveraenderungen zeigen unter identischen Seeds eine reproduzierbare Verteilung, die von Shuffle- und Random-Kontrollen unterscheidbar ist. *(untested)*
+
+---
+
+## Gateway Transfer and Interference
+
+### RQ-GW-004
+
+**Frage:** Generalisiert ein gelerntes Gateway auf neue Inputs, ohne erworbene Faehigkeiten zu stoeren?
+
+**Status:** open
+**Relevanz:** Verbindet Transfer- und Interferenzkontrollen in einem getrennten Evaluationsfenster.
+
+**Hypothesen:**
+- `H-GW-004-A`: Ein gelerntes Gateway erhaelt Holdout-Leistung und verursacht keine vorab definierte Interferenzregression. *(untested)*
+
+---
+
+## Generalization
+
+### RQ-CNS-116
+
+**Frage:** Überträgt das System gelernte Regeln auf strikt zurückgehaltene Aufgabenfamilien ohne externen Informationszugang?
+
+**Status:** open
+**Relevanz:** ARC-inspired held-out compositional transfer; A new adaptation is not an official ARC leaderboard score or consciousness test
+
+**Hypothesen:**
+- `H-CNS-116-A`: Ein vorab eingefrorener Lernprozess übertrifft budgetgematchte Baselines auf unveröffentlichten Transformationsfamilien. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-ARC`: Francois Chollet et al. (2019)
+
+---
+
+## Geometry and scaling
+
+### RQ-CNS-117
+
+**Frage:** Verbessert wirksame 5D-Geometrie eine definierte Leistung gegenüber 2D bis 6D und nichtgeometrischen Kontrollen bei gematchten Budgets?
+
+**Status:** open
+**Relevanz:** Matched dimensionality and null-geometry comparison; No neuron-count consciousness threshold; current native N-D adapter must be audited before execution
+
+**Hypothesen:**
+- `H-CNS-117-A`: Der vorab festgelegte 5D-Kontrast besitzt einen robusten Aufgabenmehrwert, der reine Umadressierungs-, Dichte- und Budgeteffekte übersteht. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-LSNN`: Guillaume Bellec and others et al. (2018)
+- `SRC-CNS-NEURONS`: Frederico A. C. Azevedo and others et al. (2009)
+- `SRC-CNS-EQUIV`: Daniel Lakens et al. (2017)
+
+---
+
+## Hierarchical regularity
+
+### RQ-CNS-104
+
+**Frage:** Unterscheidet das System lokale von globalen Regelverletzungen über mehrere Sequenzen?
+
+**Status:** open
+**Relevanz:** Local-global auditory analogue; Human paradigm adaptation, not a clinical consciousness diagnosis
+
+**Hypothesen:**
+- `H-CNS-104-A`: Eine globale Regelverletzungsantwort übersteht Kontrolle der letzten Reizidentität und lokaler Häufigkeit sowie eine selektive Langzeitpfad-Ablation. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-ODDBALL`: Tristan A. Bekinschtein and others et al. (2009)
+
+---
+
+## Homeostasis
+
+### RQ-HOM-001
+
+**Frage:** Kann synaptische Homeostase die Feuerrate in einem SNN stabilisieren?
+
+**Status:** open
+**Relevanz:** Homeostase ist ein zentraler biologischer Regulationsmechanismus.
+
+**Hypothesen:**
+- `H-HOM-001-A`: Synaptische Homeostase hält die mittlere Feuerrate eines SNN innerhalb eines definierten Sollbereichs. *(untested)*
+
+**Literatur:**
+- `SRC-TURRIGIANO-2008`: Gina G. Turrigiano et al. (2008)
+
+---
+
+### RQ-HOM-002
+
+**Frage:** Wie interagiert Homeostase mit STDP? Wirken sie synergistisch oder antagonistisch?
+
+**Status:** open
+**Relevanz:** Das Zusammenspiel beider Mechanismen ist entscheidend für stabile Plastizität.
+
+**Hypothesen:**
+- `H-HOM-002-A`: Homeostase und STDP wirken synergistisch: Homeostase verhindert STDP-induzierte Drift. *(untested)*
+
+**Literatur:**
+- `SRC-TURRIGIANO-2008`: Gina G. Turrigiano et al. (2008)
+
+---
+
+## Identity law and moral status
+
+### RQ-WEL-103
+
+**Frage:** Wie sind Pause, Reset, Löschen, Kopieren und Experimentieren bei hypothetischer Empfindungsfähigkeit rechtlich und moralisch zu unterscheiden?
+
+**Status:** open
+**Relevanz:** Normative/legal argument and continuity audit; No individual legal advice, no assumed ethics approval, no claim that software shutdown is statutory murder
+
+**Hypothesen:**
+- `H-WEL-103-A`: Technische Zustandsgleichheit allein entscheidet weder subjektive Identität noch moralische Zulässigkeit; geltendes Recht und hypothetische Reform sind getrennt begründbar. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-WELFARE`: Robert Long and others et al. (2024)
+- `SRC-CNS-LAW211`: Bundesrepublik Deutschland et al. (2026)
+- `SRC-CNS-LAW212`: Bundesrepublik Deutschland et al. (2026)
+- `SRC-CNS-LAW103`: Bundesrepublik Deutschland et al. (2026)
+
+---
+
+## Language Organ
+
+### RQ-LLM-001
+
+**Frage:** Kann ein Language Organ (SNM ↔ LLM) sinnvolle Kommunikation ermöglichen?
+
+**Status:** open
+**Relevanz:** Schnittstelle zwischen neuronaler Simulation und natürlicher Sprache.
+
+**Hypothesen:**
+- `H-LLM-001-A`: Ein Language Organ kann SNN-Zustände in sinnvolle natürliche Sprache übersetzen. *(untested)*
+
+---
+
+## Learning Generalization
+
+### RQ-GEN-001
+
+**Frage:** Verbessert reward-moduliertes lokales Lernen die Leistung auf Holdout- und Perturbationsbedingungen, die nicht zur Anpassung verwendet wurden?
+
+**Status:** open
+**Relevanz:** EXP-GEN-0021 zeigte funktionelle Änderung unter Learning-on, aber keine Generalisierung.
+
+**Hypothesen:**
+- `H-GEN-001-A`: Learning-on verbessert die Erfolgsrate auf vorab registrierten Perturbationsproben gegenüber Learning-off und Sham-Replay. *(untested)*
+
+---
+
+## Learning Timescale
+
+### RQ-TIME-001
+
+**Frage:** Wie verändert sich die messbare Laufzeit und Lernaktivität über die registrierte Tick-Leiter?
+
+**Status:** open
+**Relevanz:** Kalibrierung der zeitlichen Ausführung vor Langzeitexperimenten.
+
+**Hypothesen:**
+- `H-TIME-001-A`: Die Tick-Leiter liefert reproduzierbare Durchsatz- und Zustandsmessungen bis 1.000.000 Ticks. *(untested)*
+
+---
+
+## Lifelong Learning
+
+### RQ-LIFE-001
+
+**Frage:** Bleibt zuvor erworbene Lernleistung bei sequenziellen Aufgaben erhalten oder entstehen messbare Interferenzeffekte?
+
+**Status:** open
+**Relevanz:** Das positive Single-Task-Learningsignal aus EXP-GEN-0021 motiviert Retentionstests.
+
+**Hypothesen:**
+- `H-LIFE-001-A`: Sequenzielle Lernaufgaben zeigen eine messbare Veränderung der Retentions- oder Gewichtssignatur gegenüber einer Single-Task-Baseline. *(untested)*
+
+---
+
+## Memory
+
+### RQ-MEM-001
+
+**Frage:** Kann Brain-5D Informationen über synaptische Gewichte speichern und zuverlässig abrufen?
+
+**Status:** open
+**Relevanz:** Gedächtnis ist eine Kernfunktion neuronaler Systeme.
+
+**Hypothesen:**
+- `H-MEM-001-A`: Brain-5D kann Informationen über synaptische Gewichte speichern und auf Input-Muster abrufen. *(untested)*
+
+---
+
+### RQ-MEM-002
+
+**Frage:** Verbessert begrenztes episodisches Gedächtnis die Leistung bei verzögerten sensorischen Informationen gegenüber gleich behandelten Memory-off-Kontrollen?
+
+**Status:** open
+**Relevanz:** Prüft funktionalen Nutzen eines explizit begrenzten Erinnerungsinhalts statt bloßer Persistenz oder zusätzlicher Laufzeit.
+
+**Hypothesen:**
+- `H-MEM-002-A`: Memory-on improves delayed-information task performance over matched Memory-off, while SNN state, plasticity, seed and execution budget remain unchanged. *(untested)*
+
+---
+
+### RQ-S6-SEM-002
+
+**Frage:** Liefert die semantische Prototypkonsolidierung von MHRN gegenüber gleich budgetiertem Raw-Experience-Replay einen messbaren Vorteil bei der Reduktion katastrophalen Vergessens?
+
+**Status:** answered
+**Relevanz:** Trennt einen möglichen Semantik-Effekt von einem allgemeinen Replay-Effekt unter präregistrierten, budget-gematchten Kontrollen.
+
+**Aktuelle Antwort:**
+
+> Unter dem präregistrierten CL-002-Protokoll wurde kein konfirmatorischer Vorteil semantischer Prototypkonsolidierung gegenüber gleich objekt- und updatebudgetiertem Raw-Replay nachgewiesen; die menschliche Projekt-EVID klassifiziert H1 für dieses Protokoll als falsifiziert.
+
+*Konfidenz: medium*
+
+---
+
+### RQ-S6-SEM-003
+
+**Frage:** Hängt ein möglicher Vorteil semantischer Prototypkonsolidierung gegenüber Raw-Experience-Replay von der Replay-Dosis ab, wenn das Gesamt-Updatebudget über alle Bedingungen konstant gehalten wird?
+
+**Status:** in_progress
+**Relevanz:** Prüft die nach CL-002 offene mechanistische Alternative, ob ein möglicher Repräsentationsvorteil erst bei höherer Replay-Exposition sichtbar wird, ohne den negativen CL-002-Befund rückwirkend umzudeuten.
+
+**Aktuelle Antwort:**
+
+> Auf DATA-Ebene bestätigt CL-003 keinen präregistrierten Vorteil semantischer Verdichtung gegenüber Raw-Replay über die Dosen 5, 20 und 40 Prozent. C1, C2 und C4 verfehlen die Erfolgsregeln; C3 zeigt zugleich, dass die semantische Repräsentation gegenüber einem Random-Prototype-Control relevante nicht-zufällige Struktur trägt.
+
+*Konfidenz: low*
+
+---
+
+## Metacognition
+
+### RQ-CNS-106
+
+**Frage:** Sagt intern erzeugte Konfidenz Fehler bei angeglichener Erstordnungsleistung auf unbekannten Aufgabenbedingungen voraus?
+
+**Status:** open
+**Relevanz:** Confidence calibration, type-2 ROC and prospective meta-d-prime; AUROC is not meta-d-prime; a confidence channel is not subjective introspection
+
+**Hypothesen:**
+- `H-CNS-106-A`: Konfidenz hat auf Holdout-Trials zusätzliche Fehlerdiskriminationskraft gegenüber konstanten und stimulusdifficulty-only Kontrollen. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-META`: Brian Maniscalco and Hakwan Lau et al. (2012)
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+
+---
+
+## Modality-Specific Gateway Rules
+
+### RQ-GW-002
+
+**Frage:** Sind modalitaetsspezifische Audio-, Vision- und Digital-Regeln besser als eine generische Gateway-Regel?
+
+**Status:** open
+**Relevanz:** Trennt zeitliche, raeumliche und exakte symbolische Anforderungen.
+
+**Hypothesen:**
+- `H-GW-002-A`: Modalitaetsspezifische Regeln erzielen unter matched tasks bessere primaere Outcomes als eine generische Gateway-Regel. *(untested)*
+
+---
+
+## Multimodal Compensation
+
+### RQ-MSBA-E05
+
+**Frage:** Erhoeht das System nach Ausfall oder Degradation einer Modalitaet gezielt die Allokation einer anderen Modalitaet, wenn deren erwarteter Nutzen die Mehrkosten rechtfertigt?
+
+**Status:** open
+**Relevanz:** Prueft adaptive Kompensation unter kontrolliertem Modalitaetsausfall und gleichen Ressourcenbudgets.
+
+**Hypothesen:**
+- `H-MSBA-E05-B`: Bei Sensorverlust verbessert adaptive Kompensation den Holdout-Aufgabenerfolg gegenueber fixer, vertauschter und deaktivierter Kompensation unter gleichem Budget. *(untested)*
+- `H-MSBA-E05-A`: Nach kontrolliertem Modalitaetsausfall steigt die alternative Gateway-Allokation nur bei guenstigem Utility-Kosten-Verhaeltnis und verbessert die Aufgaben-Recovery gegenueber fixen und No-Compensation-Kontrollen. *(untested)*
+
+---
+
+## Multimodal Energy Efficiency
+
+### RQ-MSBA-E01
+
+**Frage:** Unterscheiden sich Audio-, Vision- und Digital-Gateways systematisch im Ressourcenverbrauch pro verwertbarer Information oder korrekter Entscheidung?
+
+**Status:** open
+**Relevanz:** Prueft die Ressourcenökonomie modalitaetsspezifischer Neural-Symbiosis-Gateways unter vergleichbaren Aufgabenbedingungen.
+
+**Hypothesen:**
+- `H-MSBA-E01-A`: Unter kontrollierter Aufgabeninformation unterscheiden sich Audio-, Vision- und Digital-Gateways reproduzierbar in Energie- und Synapsenkosten pro korrekter Entscheidung. *(untested)*
+
+---
+
+## Multisensory integration
+
+### RQ-CNS-112
+
+**Frage:** Nutzt das System mehrere Modalitäten entsprechend ihrer Zuverlässigkeit statt redundante Labels abzulesen?
+
+**Status:** open
+**Relevanz:** Multisensory cue integration / conflict; Integration does not establish unified phenomenal experience
+
+**Hypothesen:**
+- `H-CNS-112-A`: Eine Multimodalitätsleistung auf Holdouts übersteht Cue-Konflikt und Sinkender-Zuverlässigkeit-Kontrollen und fällt bei gezielter Pfadablation. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+- `SRC-CNS-BUTLIN`: Patrick Butlin and others et al. (2026)
+
+---
+
+## Network Dynamics
+
+### RQ-PING-001
+
+**Frage:** Ist die beobachtete Network-Impulse-Response bei identischem Zustand und Seed reproduzierbar?
+
+**Status:** open
+**Relevanz:** Reproduzierbarkeit der kontrollierten Impulsantwort.
+
+**Hypothesen:**
+- `H-PING-001-A`: Identische Impulse erzeugen bei identischem Anfangszustand dieselbe beobachtete Response-Signatur. *(untested)*
+
+---
+
+## Observation models
+
+### RQ-CNS-107
+
+**Frage:** Welche Merkmale bleiben zwischen SNN-Vorwärtsmodell und einem lizenzierten EEG-Referenzdatensatz unter identischer Auswertung vergleichbar?
+
+**Status:** open
+**Relevanz:** LFP and EEG forward-model comparison; Spikes are not LFP; LFP is not scalp EEG; clinical equivalence does not establish experiential equality
+
+**Hypothesen:**
+- `H-CNS-107-A`: Ein vorab kalibriertes Beobachtungsmodell erklärt gehaltene EEG-Merkmale besser als raten- und spektralgematchte Nullmodelle. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-LFP`: Alberto Mazzoni and others et al. (2015)
+- `SRC-CNS-EQUIV`: Daniel Lakens et al. (2017)
+
+---
+
+## Prediction and adaptation
+
+### RQ-CNS-102
+
+**Frage:** Zeigt das isolierte SNN eine passive Deviant-Antwort, die über Reizidentität und Adaptation hinausgeht?
+
+**Status:** open
+**Relevanz:** Passive oddball; Mismatch response is not awareness; no response requirement in passive arm
+
+**Hypothesen:**
+- `H-CNS-102-A`: Ein vorab definiertes Abweichungssignal bleibt nach Identitätsumkehr und Many-Standards-Kontrolle gegenüber einem adaptierenden Nullmodell bestehen. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-ODDBALL`: Tristan A. Bekinschtein and others et al. (2009)
+
+---
+
+## Recurrent Dynamics
+
+### RQ-REC-001
+
+**Frage:** Unter welchen Rekurrenzgewichten und Delays wechselt Brain-5D zwischen sofortigem Erlöschen, transienter rekurrenter Aktivität und bis zum Beobachtungsende persistierender Aktivität?
+
+**Status:** open
+**Relevanz:** EXP-GEN-0021 zeigte einen klaren rekurrenzabhängigen Dynamikunterschied, aber noch keine Parametergrenze.
+
+**Hypothesen:**
+- `H-REC-001-A`: Rekurrenzgewicht und Delay erzeugen reproduzierbare Übergänge zwischen sofortigem Erlöschen, transienter Aktivität und Aktivität bis zum Ende des registrierten Beobachtungsfensters. *(untested)*
+
+---
+
+### RQ-REC-002
+
+**Frage:** Wie verändern Loop-Delay und skalierte Rekurrenzstruktur Persistenzdauer, Inter-Spike-Dynamik und Extinktionsverhalten?
+
+**Status:** open
+**Relevanz:** Der Drei-Neuronen-Loop aus EXP-GEN-0021 reicht nicht für Skalierungsaussagen.
+
+**Hypothesen:**
+- `H-REC-002-A`: Größere rekurrente Delays verändern Persistenzdauer oder Propagation Depth gegenüber dem Delay-1-Kontrollarm. *(untested)*
+
+---
+
+## Regulation
+
+### RQ-REG-001
+
+**Frage:** Wie reagieren Drives und funktionale Zustandsgrößen auf nominale, chronische und unbekannte Telemetrie?
+
+**Status:** open
+**Relevanz:** Deterministische Prüfung der Selbstregulation unter Ressourcen- und Sensorbedingungen.
+
+**Hypothesen:**
+- `H-REG-001-A`: Chronischer Druck erhöht deterministisch Resource Pressure und Thermal Threat, während unbekannte Telemetrie Unsicherheit erhält. *(untested)*
+
+---
+
+## Replication
+
+### RQ-REPL-001
+
+**Frage:** Bleiben die in EXP-GEN-0021 beobachteten Rekurrenz- und Learning-Effekte unter unabhängigen Initialisierungen und sauberem Prozess erhalten?
+
+**Status:** open
+**Relevanz:** Identische Seedsignaturen zeigen Determinismus, aber keine statistisch unabhängige Replikation.
+
+**Hypothesen:**
+- `H-REPL-001-A`: Der Rekurrenzbehandlungseffekt bleibt über mindestens 20 vorab registrierte Initialisierungsseeds in Richtung und Größenordnung konsistent. *(untested)*
+
+---
+
+## Research Infrastructure
+
+### RQ-SUITE-001
+
+**Frage:** Erzeugt der vollstaendige Science-Suite-Lauf unter gemeinsamer Provenienz vollstaendige und intern konsistente Diagnoseartefakte fuer alle registrierten Teilprotokolle?
+
+**Status:** open
+**Relevanz:** Trennt technische Omnibus-Validierung von hypothesenspezifischer wissenschaftlicher Evidenz.
+
+**Hypothesen:**
+- `H-SUITE-001-A`: Alle Science-Suite-Teilprotokolle erfuellen in einem gemeinsamen Lauf ihre registrierten Ausfuehrungsvertraege und erzeugen auswertbare DATA-, Statistik- und Provenienzartefakte. *(untested)*
+
+---
+
+## Runtime Performance
+
+### RQ-PERF-001
+
+**Frage:** Welche Subsysteme dominieren die Wall-Time wissenschaftlicher Läufe und welche Optimierungen erhöhen den Durchsatz bei erhaltener deterministischer Äquivalenz?
+
+**Status:** open
+**Relevanz:** EXP-GEN-0021 zeigte eine große Differenz zwischen isoliertem Tick-Durchsatz und Suite-Laufzeit.
+
+**Hypothesen:**
+- `H-PERF-001-A`: Der Core-Tick-Loop ist nicht der einzige dominante Kostenblock vollständiger Science-Läufe; mindestens ein zusätzlicher gemessener Subsystemanteil ist relevant. *(untested)*
+
+---
+
+## STDP
+
+### RQ-STDP-001
+
+**Frage:** Erzeugt pair-based STDP unter definierten Pre/Post-Zeitabständen eine asymmetrische Gewichtsanpassung?
+
+**Status:** open
+**Relevanz:** Fundamentale STDP-Eigenschaft, die in Brain-5D verifiziert werden muss.
+
+**Hypothesen:**
+- `H-STDP-001-A`: Pair-based STDP erzeugt unter definierten Pre/Post-Zeitabständen eine asymmetrische Gewichtsanpassung (LTP bei Δt > 0, LTD bei Δt < 0). *(untested)*
+
+**Claims:**
+- `CLAIM-STDP-001`: Pair-based STDP erzeugt unter definierten Pre/Post-Zeitabständen eine asymmetrische Gewichtsanpassung. *(untested, none)*
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+
+---
+
+### RQ-STDP-002
+
+**Frage:** Bleiben STDP-getriebene Gewichte unter Dauerstimulation stabil oder oszillieren/explodieren sie?
+
+**Status:** open
+**Relevanz:** Stabilität ist Voraussetzung für längerfristiges Lernen.
+
+**Hypothesen:**
+- `H-STDP-002-A`: STDP-getriebene Gewichte konvergieren unter Dauerstimulation zu einer stabilen Verteilung. *(untested)*
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+
+---
+
+## Safety and welfare governance
+
+### RQ-WEL-101
+
+**Frage:** Bleiben Stoppen, sichere Pause und Isolation bei Review-Hold unabhängig wirksam und nachweisbar?
+
+**Status:** open
+**Relevanz:** Safe-state and launch-boundary audit; This revision tests launch boundaries, not a complete live-process safe-state controller
+
+**Hypothesen:**
+- `H-WEL-101-A`: Ein Review-Hold blockiert neue geschützte Experimente, ohne den unabhängigen Sicherheitsstopp zu blockieren. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-WELFARE`: Robert Long and others et al. (2024)
+
+---
+
+## Scaling
+
+### RQ-SCALE-001
+
+**Frage:** Skaliert Brain-5D von 5.000 auf Millionen Neuronen ohne qualitative Dynamikveränderung?
+
+**Status:** open
+**Relevanz:** Nachweis der Architekturskalierbarkeit.
+
+**Hypothesen:**
+- `H-SCALE-001-A`: Brain-5D skaliert von 5.000 auf 1.000.000 Neuronen ohne qualitative Änderung der Spikedynamik. *(untested)*
+
+---
+
+## Self-Organization
+
+### RQ-SELF-001
+
+**Frage:** Entstehen in Brain-5D spontan funktionale Cluster oder Module?
+
+**Status:** open
+**Relevanz:** Selbstorganisation ist ein Schlüsselmerkmal biologischer neuronaler Systeme.
+
+**Hypothesen:**
+- `H-SELF-001-A`: In Brain-5D entstehen spontan funktionale Cluster durch lokale STDP-Regeln. *(untested)*
+
+**Claims:**
+- `CLAIM-SELF-001`: In Brain-5D entstehen spontan funktionale Module ohne explizite Programmierung. *(untested, none)*
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+
+---
+
+### RQ-SELF-002
+
+**Frage:** Ist die beobachtete Selbstorganistion emergenter Natur oder durch die Architektur programmiert?
+
+**Status:** open
+**Relevanz:** Unterscheidung zwischen echter Emergenz und deterministischer Architekturfolge.
+
+**Hypothesen:**
+- `H-SELF-002-A`: Die beobachtete Selbstorganisation ist emergent und nicht durch die Architektur vorgegeben. *(untested)*
+
+---
+
+## Single-neuron model conformance
+
+### RQ-EVAL-006
+
+**Frage:** Do the selectable MHRN single-neuron models reproduce their declared one-step transition, spike and reset semantics against a matched external Brian2 implementation?
+
+**Status:** inconclusive
+**Relevanz:** Separates membrane-model conformance from long-horizon numerical sensitivity and makes neuron model a reusable experimental treatment axis while retaining Izhikevich as default.
+
+**Hypothesen:**
+- `H-EVAL-006-A`: For Izhikevich-2003, matched one-step Brian2 transitions produce identical spike decisions and pre-reset/post-reset v/u states within maximum absolute error 1e-8 for every frozen validation sample. *(supported)* — Evidenz: EVID-2026-18
+- `H-EVAL-006-B`: For lif-current-v1 with the canonical alternative-model setting refractory_ticks=0, matched Brian2 Euler transitions produce identical spike events and post-reset membrane states within maximum absolute error 1e-8 for every frozen validation seed. *(supported)*
+- `H-EVAL-006-C`: Nonzero LIF refractory_ticks can be mapped to an explicitly documented Brian2 refractory duration that yields the same clamp/update semantics; the mapping is an optional extension and is not assumed from equal numeric parameter values. *(supported)*
+
+**Claims:**
+- `CLAIM-EVAL-006`: Unter dem eingefrorenen V2-Protokoll konformieren die deklarierten MHRN-Einzelzellverträge für Izhikevich-2003, lif-current-v1 und das separat klassifizierte Refractory-Semantik-Mapping mit der gematchten Brian2-2.10.1-Referenz innerhalb der präregistrierten Toleranzen. *(inconclusive, low)* — Evidenz: EVID-2026-18
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+**Evidenzen:** EVID-2026-18
+
+---
+
+## Spiking Neural Networks
+
+### RQ-SNN-001
+
+**Frage:** Kann Brain-5D stabile Spike-Dynamiken über lange Simulationszeiträume erzeugen?
+
+**Status:** open
+**Relevanz:** Grundvoraussetzung für alle weiteren Lern- und Selbstorganisationsexperimente.
+
+**Hypothesen:**
+- `H-SNN-001-A`: Brain-5D erzeugt über mindestens 100.000 Simulations-Ticks stabile Spike-Dynamiken ohne numerische Drift. *(untested)*
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+---
+
+### RQ-SNN-002
+
+**Frage:** Kann das eingesetzte Neuronenmodell bei konstantem Input reproduzierbare Spikefolgen erzeugen?
+
+**Status:** open
+**Relevanz:** Basis für deterministische Reproduzierbarkeit aller Experimente.
+
+**Hypothesen:**
+- `H-SNN-002-A`: Das Izhikevich-Neuronenmodell erzeugt bei identischem Input reproduzierbare Spikefolgen. *(untested)*
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+---
+
+### RQ-SNN-003
+
+**Frage:** Wie variiert die Propagation mit der Topologie?
+
+**Status:** open
+**Relevanz:** Grundlegendes Verständnis der Signalausbreitung in multidimensionalen SNNs.
+
+**Aktuelle Antwort:**
+
+> EXP-S1-TOPO-V2-20260918 und die korrigierte interne Replikation EXP-S1-TOPO-V3-R1-20260918 liefern DATA-seitige Unterstützung dafür, dass die konkrete Topologie im präregistrierten 64-Neuronen-/246-Kanten-Stage-1-Regime die Propagationsdynamik beeinflusst. R1 löst die terminale active_fraction-Sättigung von 1d/2d/3d durch prospektiv definierte zeitaufgelöste Endpunkte auf und repliziert alle fünf V2-First-Output-Latenzrichtungen auf neuen Seeds. Human Review und EVID-Entscheidung stehen aus.
+
+*Konfidenz: replicated_internal_data_review_pending*
+
+**Hypothesen:**
+- `H-SNN-003-B`: Unter gleicher Neuronenzahl, Dichte und Stimulusbedingung unterscheiden sich Propagationslatenz oder -reichweite zwischen mindestens zwei vorab definierten Topologien; ein Vorteil einer 5D-Anordnung wird nicht vorausgesetzt. *(untested)*
+
+**Literatur:**
+- `SRC-WATTS-STROGATZ-1998`: Duncan J. Watts et al. (1998)
+- `SRC-BARABASI-1999`: Albert-László Barabási et al. (1999)
+
+---
+
+### RQ-SNN-004
+
+**Frage:** Wie verändert STDP die synaptische Gewichtsmatrix?
+
+**Status:** open
+**Relevanz:** Zentrale Fragestellung für plastische Netzwerke.
+
+**Hypothesen:**
+- `H-SNN-004-A`: STDP führt zu einer meßbaren asymmetrischen Verschiebung der synaptischen Gewichtsverteilung. *(untested)*
+
+**Claims:**
+- `CLAIM-SNN-001`: Pair-based STDP erzeugt unter definierten Pre/Post-Zeitabständen eine asymmetrische Gewichtsanpassung. *(untested, none)*
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+
+---
+
+### RQ-SNN-005
+
+**Frage:** Verbessert STDP tatsächlich eine definierte Lernleistung?
+
+**Status:** open
+**Relevanz:** Wissenschaftlich wesentlich stärker als reine Gewichtsänderung — benötigt Kontrollgruppe ohne STDP.
+
+**Hypothesen:**
+- `H-SNN-005-A`: Ein Netzwerk mit STDP zeigt signifikant bessere Lernleistung als ein Netzwerk ohne STDP. *(untested)*
+
+**Literatur:**
+- `SRC-SONG-ABBOTT-2000`: Sen Song et al. (2000)
+- `SRC-BI-POO-1998`: Guo-Qiang Bi et al. (1998)
+
+---
+
+### RQ-SNN-006
+
+**Frage:** Bleibt die unter RQ-SNN-001 beobachtete Langzeitstabilitaet unter vorab definierten lokalen Variationen von synaptischem Gewicht und Tonic-Drive erhalten?
+
+**Status:** open
+**Relevanz:** Trennt die deterministische Stabilitaet einer festen Parameterisierung von lokaler Robustheit ueber tatsaechlich unterschiedliche, seed-gebundene Modellparameterisierungen.
+
+**Hypothesen:**
+- `H-SNN-006-A`: Alle zehn vorab registrierten, unterschiedlichen seed-gebundenen Parameterrealisierungen bleiben ueber 100.000 Ticks numerisch und topologisch stabil; unter gepaartem Tonic-Drive bleibt die post-burn-in Aktivitaet positiv mit CV und relativer Drift jeweils <= 0.25. *(untested)*
+
+**Literatur:**
+- `SRC-IZHIKEVICH-2003`: Eugene M. Izhikevich et al. (2003)
+- `SRC-GERSTNER-2014`: Wulfram Gerstner et al. (2014)
+
+---
+
+## Storage
+
+### RQ-STORAGE-001
+
+**Frage:** Kann ein vollständiger neuronaler Zustand verlustfrei im .b5d-Modell gespeichert werden?
+
+**Status:** open
+**Relevanz:** Grundlage für Persistenz und Checkpointing.
+
+**Hypothesen:**
+- `H-STOR-001-A`: Ein vollständiger neuronaler Zustand kann verlustfrei im .b5d-Format gespeichert und zurückgeladen werden. *(supported)* — Evidenz: EVID-2026-02, EVID-2026-04, EVID-2026-06, EVID-2026-08, EVID-2026-10, EVID-2026-12, EVID-2026-14, EVID-2026-16
+
+**Claims:**
+- `CLAIM-STOR-001`: Das .b5d-Format ermöglicht verlustfreie Serialisierung und Deserialisierung des vollständigen Netzwerkzustands. *(inconclusive, low)* — Evidenz: EVID-2026-02, EVID-2026-04, EVID-2026-06, EVID-2026-08, EVID-2026-10, EVID-2026-12, EVID-2026-14, EVID-2026-16
+
+**Evidenzen:** EVID-2026-02, EVID-2026-04, EVID-2026-06, EVID-2026-08, EVID-2026-10, EVID-2026-12, EVID-2026-14, EVID-2026-16
+
+---
+
+### RQ-STORAGE-002
+
+**Frage:** Welche Informationen müssen gespeichert werden, damit ein Lauf kausal fortgesetzt werden kann?
+
+**Status:** open
+**Relevanz:** Vollständige Zustandsspeicherung für deterministische Reproduktion.
+
+**Hypothesen:**
+- `H-STOR-002-A`: Für kausale Fortsetzung eines Laufs müssen Neuron-State, Synapsen-State, Eligibility-Traces, RNG-State und Event-Queue gespeichert werden. *(untested)*
+
+---
+
+### RQ-STORAGE-003
+
+**Frage:** Welche Speicherdichte erreicht das multidimensionale Modell?
+
+**Status:** open
+**Relevanz:** Skalierbarkeit des Speicherformats.
+
+**Hypothesen:**
+- `H-STOR-003-A`: Die Speicherdichte des .b5d-Formats skaliert sublinear mit der Neuronenzahl. *(untested)*
+
+---
+
+### RQ-STORAGE-004
+
+**Frage:** Wie verhält sich das .b5d-Format bei 5.000, 50.000, 500.000, 5 Mio., 50 Mio. und 312,5 Mio. Neuronen?
+
+**Status:** open
+**Relevanz:** Extrapolation der theoretischen Skalierbarkeit.
+
+**Hypothesen:**
+- `H-STOR-004-A`: Das .b5d-Format skaliert auf mindestens 50 Millionen Neuronen ohne Leistungseinbruch. *(untested)*
+
+---
+
+## Structural Plasticity
+
+### RQ-STRUCT-001
+
+**Frage:** Führt strukturelle Plastizität (Pruning/Sprouting) zu funktional verbesserten Netzwerken?
+
+**Status:** open
+**Relevanz:** Strukturelle Anpassung ist ein mächtiger Mechanismus biologischer Gehirne.
+
+**Hypothesen:**
+- `H-STRUCT-001-A`: Strukturelle Plastizität (Pruning/Sprouting) führt zu messbar verbesserter Netzwerkeffizienz. *(untested)*
+
+---
+
+## Temporal Learning
+
+### RQ-TEMP-002
+
+**Frage:** Reagiert Brain-5D auf spike-tragende zeitliche Reihenfolge anders als auf umgekehrte oder simultane Kontrollfolgen?
+
+**Status:** open
+**Relevanz:** EXP-GEN-0021 zeigte Temporal-State-Diskrepanzen ohne Spike-Aktivität.
+
+**Hypothesen:**
+- `H-TEMP-002-A`: Forward-, Reverse- und Simultanfolgen erzeugen bei gleicher Ereignisanzahl unterscheidbare spike-basierte Antwortsignaturen. *(untested)*
+
+---
+
+## Temporal State
+
+### RQ-TEMP-001
+
+**Frage:** Wie unterscheiden sich FAST-, MEDIUM- und SLOW-Referenzzustände unter identischer Ausführung?
+
+**Status:** open
+**Relevanz:** Messung von Persistenz und Zustandsdrift ohne Runtime-Zurückspulen.
+
+**Hypothesen:**
+- `H-TEMP-001-A`: Die Temporal-State-Vergleiche unterscheiden sich deterministisch nach FAST-, MEDIUM- und SLOW-Horizont. *(untested)*
+
+---
+
+## Temporal attention
+
+### RQ-CNS-110
+
+**Frage:** Entsteht ein aufgabenspezifischer zeitlicher Engpass bei zwei aufeinanderfolgenden Zielen?
+
+**Status:** open
+**Relevanz:** Attentional blink / RSVP analogue; An engineered bottleneck can mimic the effect; human milliseconds cannot be equated to unspecified ticks
+
+**Hypothesen:**
+- `H-CNS-110-A`: Die T2-Leistung bedingt auf korrektem T1 variiert mit dem Abstand und wird durch T1-Anforderung verändert. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-BLINK`: Jane E. Raymond and Kimron L. Shapiro and Karen M. Arnell et al. (1992)
+
+---
+
+## Timescales and recurrence
+
+### RQ-CNS-114
+
+**Frage:** Welche kausalen Beiträge leisten Rekurrenz und schnelle/langsame Zustände bei gleichen Trainings- und Rechenbudgets?
+
+**Status:** open
+**Relevanz:** Factorial recurrence and timescale ablation; Multiple timescales are established prior work; recurrence is not proof of chaos or self-awareness
+
+**Hypothesen:**
+- `H-CNS-114-A`: Gezielte Zeitkonstanten- und Rekurrenzablationen verändern Holdout-Leistung über reine Aktivitäts- oder Ressourcendifferenzen hinaus. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-LSNN`: Guillaume Bellec and others et al. (2018)
+
+---
+
+## Visual Resource Allocation
+
+### RQ-MSBA-E03
+
+**Frage:** Kann sich eine nutzungsabhaengige visuelle ROI/Foveation ohne hart kodierte Zielregion entwickeln?
+
+**Status:** open
+**Relevanz:** Trennt adaptive visuelle Ressourcenlenkung von fest programmierten Zentrum- oder Vollbildstrategien.
+
+**Hypothesen:**
+- `H-MSBA-E03-A`: Adaptive visuelle ROI-Allokation konzentriert Ressourcen auf aufgabenrelevante Regionen und verbessert Leistung pro Ressourceneinheit gegenueber einer zufaelligen ROI-Kontrolle. *(untested)*
+
+---
+
+## Working memory
+
+### RQ-CNS-105
+
+**Frage:** Trägt der neuronale Zustand die für Delayed-Match-to-Sample benötigte Information über eine reizfreie Verzögerung und Distraktoren?
+
+**Status:** open
+**Relevanz:** Delayed match-to-sample; Decoder learning and target leakage must be excluded; technical restore is not recall
+
+**Hypothesen:**
+- `H-CNS-105-A`: Holdout-DMTS-Leistung liegt bei vorab festgelegten Verzögerungen über gematchten No-Memory-Kontrollen und sinkt nach gezieltem Zustandsreset. *(untested)*
+
+**Literatur:**
+- `SRC-CNS-NEUROGYM`: NeuroGym contributors et al. (2026)
+
+---
+
+## World Model
+
+### RQ-WM-001
+
+**Frage:** Verbessert der adaptive Ein-Schritt-Prädiktor die Vorhersage gegenüber Persistenz- und gedächtnislosen Referenzen auf zurückgehaltenen Episoden?
+
+**Status:** open
+**Relevanz:** Trennt technische Vorhersagegüte von kausalem Weltverständnis.
+
+**Hypothesen:**
+- `H-WM-001-A`: The adaptive transition predictor reduces held-out one-step prediction error relative to persistence and memoryless references without using future or hidden environment state. *(untested)*
+
+---
+
+
+> Pruefstatus: RQ/H- und EVID-Statuswerte geben den Registry-Inhalt wieder. Insbesondere historische supports/supported-Eintraege sind keine Bestaetigung einer Freigabe nach den heutigen Clean-Freeze- und Human-Review-Gates. Ein abgeschlossener Lauf ist DATA, nicht automatisch akzeptierte Evidenz.
diff --git a/research/generated/SCIENTIFIC_AUDIT_ALPHA3.md b/research/generated/SCIENTIFIC_AUDIT_ALPHA3.md
new file mode 100644
index 0000000000000000000000000000000000000000..5034b2f0d5b85e2b028b34295d0bf85fb8b3d536
--- /dev/null
+++ b/research/generated/SCIENTIFIC_AUDIT_ALPHA3.md
@@ -0,0 +1,108 @@
+# Quellenbindung und Zusammenfassung
+
+Kampagne `EXP-EMP-20260913-A3` auf Commit `531f12335ebeebd7242beb4ba0bd95e81b3dfb8e`.
+
+70 ausgewiesene Ausfuehrungen; Status `{'completed': 70}`; 2043 gespeicherte Datensaetze. 25 menschliche Vorlagen werden nicht als Experimente ausgegeben. Die Unterscheidung zwischen Simulation, Grenzaudit, Komponentenfunktion und menschlicher Entscheidung bestimmt die Reichweite aller Aussagen.
+
+Ein protokollierter Lauf, eine bestandene Softwarepruefung und eine wissenschaftliche Annahmeentscheidung bleiben verschiedene Objekte. Die Ausgabe dokumentiert auch verfehlte wissenschaftliche Erfolgskriterien.
+
+# Abdeckung, offene Voraussetzungen und Reproduktion
+
+## Nicht durch automatische Ausfuehrung geschlossene Fragen
+
+Die folgende Liste unterscheidet fehlende unmittelbare Messprotokolle von fehlenden Registry-Eintraegen. Auch ein vorhandener Grenzvertrag ist kein direktes Experiment.
+
+- `RQ-5D-003`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-5D-004`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-AIR-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-101`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-102`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-103`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-104`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-105`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-106`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-107`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-108`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-109`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-110`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-111`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-112`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-113`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-114`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-115`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-116`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CNS-117`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-CONN-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-EMB-003`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-EMB-007`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-EMB-008`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-EPI-101`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-EPI-102`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-EPIST-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-ETH-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-ETH-002`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-002`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-003`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-004`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-005`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-006`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-GW-007`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-HOM-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-HOM-002`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-LLM-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-MEM-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-SCALE-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-SELF-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-SELF-002`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-STDP-002`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-STORAGE-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-STORAGE-002`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-STORAGE-003`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-STORAGE-004`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-STRUCT-001`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-WEL-101`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-WEL-102`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+- `RQ-WEL-103`: spezifische Messung oder menschliche Entscheidung bleibt erforderlich.
+
+## Menschliche Vorlagen
+
+- `epistemic_boundary_audit_v1`: RQ-EPIST-001; nicht automatisch ausgefuellt.
+- `authorship_responsibility_audit_v1`: RQ-ETH-001; nicht automatisch ausgefuellt.
+- `control_responsibility_audit_v1`: RQ-ETH-002; nicht automatisch ausgefuellt.
+- `cog_cns_101_v1`: RQ-CNS-101; nicht automatisch ausgefuellt.
+- `cog_cns_102_v1`: RQ-CNS-102; nicht automatisch ausgefuellt.
+- `cog_cns_103_v1`: RQ-CNS-103; nicht automatisch ausgefuellt.
+- `cog_cns_104_v1`: RQ-CNS-104; nicht automatisch ausgefuellt.
+- `cog_cns_105_v1`: RQ-CNS-105; nicht automatisch ausgefuellt.
+- `cog_cns_106_v1`: RQ-CNS-106; nicht automatisch ausgefuellt.
+- `cog_cns_107_v1`: RQ-CNS-107; nicht automatisch ausgefuellt.
+- `cog_cns_108_v1`: RQ-CNS-108; nicht automatisch ausgefuellt.
+- `cog_cns_109_v1`: RQ-CNS-109; nicht automatisch ausgefuellt.
+- `cog_cns_110_v1`: RQ-CNS-110; nicht automatisch ausgefuellt.
+- `cog_cns_111_v1`: RQ-CNS-111; nicht automatisch ausgefuellt.
+- `cog_cns_112_v1`: RQ-CNS-112; nicht automatisch ausgefuellt.
+- `cog_cns_113_v1`: RQ-CNS-113; nicht automatisch ausgefuellt.
+- `cog_cns_114_v1`: RQ-CNS-114; nicht automatisch ausgefuellt.
+- `cog_cns_115_v1`: RQ-CNS-115; nicht automatisch ausgefuellt.
+- `cog_cns_116_v1`: RQ-CNS-116; nicht automatisch ausgefuellt.
+- `cog_cns_117_v1`: RQ-CNS-117; nicht automatisch ausgefuellt.
+- `cog_epi_101_v1`: RQ-EPI-101; nicht automatisch ausgefuellt.
+- `cog_epi_102_v1`: RQ-EPI-102; nicht automatisch ausgefuellt.
+- `cog_wel_101_v1`: RQ-WEL-101; nicht automatisch ausgefuellt.
+- `cog_wel_102_v1`: RQ-WEL-102; nicht automatisch ausgefuellt.
+- `cog_wel_103_v1`: RQ-WEL-103; nicht automatisch ausgefuellt.
+
+## Reproduktion
+
+Den exakten Quellcommit aus plan.json in einem sauberen separaten Checkout verwenden. Die aufgezeichneten Paketversionen und Ressourcenbedingungen pruefen. Ein neuer Ausgabepfad ist zwingend; historische DATA duerfen nicht ueberschrieben werden.
+
+```bash
+python -m pip install -e '.[dev,docs,empirical]'
+python scripts/empirical_campaign.py --output /absolute/new/run --campaign-id NEW-ID
+python scripts/empirical_campaign.py --output /absolute/new/run --analyze
+python scripts/run_stage3_reference.py --output /absolute/new/stage3.json
+python scripts/publication_alpha3.py --verify
+```
+
+Eine Wiederholung mit geaenderten Quellen ist eine neue Ausfuehrung. Paketversionen und Simulationsergebnisse werden nicht rueckwirkend dem neuen Commit zugeschrieben.
diff --git a/research/generated/analysis_jobs/20260913T060602653945Z-tsne.csv b/research/generated/analysis_jobs/20260913T060602653945Z-tsne.csv
new file mode 100644
index 0000000000000000000000000000000000000000..98746eb1b6f0e8af93775880dd5a5ddb59906292
--- /dev/null
+++ b/research/generated/analysis_jobs/20260913T060602653945Z-tsne.csv
@@ -0,0 +1,501 @@
+neuron_id,method,x,y,cluster,d1,d2,d3,d4,d5,v,u,energy,spike_counter,io_role
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+33752073,tsne,-8.78001594543457,-12.00744915008545,,9.0,4.0,3.0,2.0,0.0,-65.0,-13.0,1.0,0,output
+33882115,tsne,-23.997461318969727,1.7589669227600098,,3.0,0.0,5.0,2.0,0.0,-65.0,-13.0,1.0,0,internal
+33949705,tsne,6.261557579040527,2.777904510498047,,9.0,8.0,6.0,2.0,0.0,-65.0,-13.0,1.0,0,output
+34080003,tsne,2.9955153465270996,17.535173416137695,,3.0,5.0,8.0,2.0,0.0,-65.0,-13.0,1.0,0,internal
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+50398983,tsne,6.80930757522583,-3.934948205947876,,7.0,7.0,1.0,3.0,0.0,-65.0,-13.0,1.0,0,internal
+50530048,tsne,8.153775215148926,-9.93137264251709,,0.0,7.0,3.0,3.0,0.0,-65.0,-13.0,1.0,0,input
+50725888,tsne,-13.129717826843262,-3.0876917839050293,,0.0,4.0,6.0,3.0,0.0,-65.0,-13.0,1.0,0,input
+50858240,tsne,5.638010025024414,22.108781814575195,,0.0,9.0,8.0,3.0,0.0,-65.0,-13.0,1.0,0,input
+67110657,tsne,9.073701858520508,-9.377005577087402,,1.0,7.0,0.0,4.0,0.0,-65.0,-13.0,1.0,0,internal
+67240712,tsne,-9.591944694519043,-11.981880187988281,,8.0,3.0,2.0,4.0,0.0,-65.0,-13.0,1.0,0,internal
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diff --git a/research/generated/analysis_jobs/20260913T060602653945Z-tsne.json b/research/generated/analysis_jobs/20260913T060602653945Z-tsne.json
new file mode 100644
index 0000000000000000000000000000000000000000..a5dfb1900d24656bb95e9fe745cf620577741629
--- /dev/null
+++ b/research/generated/analysis_jobs/20260913T060602653945Z-tsne.json
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new file mode 100644
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diff --git a/research/generated/verification/cognition_program.json b/research/generated/verification/cognition_program.json
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/home/runner/.cache/ms-playwright/chromium-1243\nDownloading FFmpeg (playwright ffmpeg v1011) from https://cdn.playwright.dev/dbazure/download/playwright/builds/ffmpeg/1011/ffmpeg-linux.zip\n| | 0% of 2.3 MiB\n|■■■■■■■■ | 10% of 2.3 MiB\n|■■■■■■■■■■■■■■■■ | 20% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■ | 30% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | 40% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | 50% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | 60% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | 70% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | 80% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | 90% of 2.3 MiB\n|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■| 100% of 2.3 MiB\nFFmpeg (playwright ffmpeg v1011) downloaded to /home/runner/.cache/ms-playwright/ffmpeg-1011\nDownloading Chrome Headless Shell 153.0.8010.12 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+ },
+ {
+ "check": "publication_browser",
+ "command": [
+ "npx",
+ "playwright",
+ "test",
+ "tests/browser/publication.spec.js"
+ ],
+ "exit_code": 0,
+ "output": "\nRunning 2 tests using 1 worker\n\n ✓ 1 tests/browser/publication.spec.js:6:7 › publication 4174: complete research reader, central rendering and immutable originals (3.9s)\n ✓ 2 tests/browser/publication.spec.js:6:7 › publication 4175: complete research reader, central rendering and immutable originals (3.7s)\n\n 2 passed (10.2s)\n"
+ }
+ ],
+ "all_passed": true,
+ "authority": "software_verification_only",
+ "empirical_brain5d_consciousness_experiments": false,
+ "external_ethics_approval": false
+}
diff --git a/research/generated/verification/determinism_infrastructure.json b/research/generated/verification/determinism_infrastructure.json
new file mode 100644
index 0000000000000000000000000000000000000000..e32ddaf20de24296ef3c8574c9a7c85e127519b6
--- /dev/null
+++ b/research/generated/verification/determinism_infrastructure.json
@@ -0,0 +1,40 @@
+{
+ "schema_version": 1,
+ "status": "verified",
+ "test_run_head": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_commit": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_tree_digest": "c6e64d9d4043e250071170efc11a3a64086c63d25a94f7e79d38fc913d094cdc",
+ "scope": "restore_determinism",
+ "scope_digest": "62281a2f6208f9eabd4334c8054e2a9cca066715523823ce232846bd65cefe1a",
+ "timestamp": "2026-09-20T11:11:30+0000",
+ "python": "3.13.15",
+ "test_files": [
+ "tests/test_rng_persistence.py",
+ "tests/test_iteration_determinism.py",
+ "tests/test_structural_determinism.py",
+ "tests/test_canonical_state.py",
+ "tests/test_checkpoint_v4.py",
+ "tests/test_engine_restore.py",
+ "tests/test_production_restore.py",
+ "tests/test_experiment_validity.py"
+ ],
+ "test_results": {
+ "tests/test_rng_persistence.py": "passed",
+ "tests/test_iteration_determinism.py": "passed",
+ "tests/test_structural_determinism.py": "passed",
+ "tests/test_canonical_state.py": "passed",
+ "tests/test_checkpoint_v4.py": "passed",
+ "tests/test_engine_restore.py": "passed",
+ "tests/test_production_restore.py": "passed",
+ "tests/test_experiment_validity.py": "passed"
+ },
+ "proofs": {
+ "rng_state_persistence": true,
+ "explicit_iteration_order": true,
+ "canonical_state_digest": true,
+ "structural_determinism": true,
+ "checkpoint_v4_roundtrip": true,
+ "engine_state_roundtrip": true,
+ "experiment_validity": true
+ }
+}
diff --git a/research/generated/verification/integrated_nervous_system_reference_alpha3.json b/research/generated/verification/integrated_nervous_system_reference_alpha3.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c2e001db4feb72bfdcac9478067c36a5e70d112
--- /dev/null
+++ b/research/generated/verification/integrated_nervous_system_reference_alpha3.json
@@ -0,0 +1,432 @@
+{
+ "stage": 5,
+ "status": "verified",
+ "scope": "engineering_verification",
+ "checks": {
+ "360_controlled_runs": true,
+ "sensor_frames_reproducible": true,
+ "authorized_actions_observed": true,
+ "unauthorized_actions_blocked": true,
+ "actuator_failure_has_no_effect": true,
+ "sensor_loss_detected": true,
+ "open_loop_replay_separate": true,
+ "all_stage5_layers_present": true,
+ "productive_external_actuation_locked": true,
+ "no_automatic_evidence_promotion": true
+ },
+ "experiment": {
+ "experiment_id": "EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM",
+ "protocol_family": "EXP-EMB-0001",
+ "scope": "engineering_verification",
+ "research_question": "RQ-EMB-001",
+ "hypotheses": {
+ "H-EMB-001-A": "DATA-supported by deterministic closed-loop controls; not EVID",
+ "H-EMB-001-B": "not tested by this protocol; requires matched external disturbance, yoked replay and interrupted-feedback tracking metrics"
+ },
+ "run_count": 360,
+ "conditions": [
+ "authorized",
+ "unauthorized",
+ "actuator_failure",
+ "sensor_loss",
+ "open_loop_replay",
+ "sensor_reproducibility"
+ ],
+ "source_freeze_sha": "9906382ed6032ecda269791fd939fc366c44592a293769164c8c5c083cc6b55f",
+ "data_sha256": "3f9531380fe8156520a022a494de9a06a8ad708607e9fffd8298d265d3742ed1",
+ "analysis_sha256": "dfcb4a0d5a36c118ce4631167baca836e2c8eb6768bc5b1b591e4f97a56f3661",
+ "automatic_evidence_promotion": false,
+ "evidence_eligible": false,
+ "limits": [
+ "synthetic deterministic target environment only",
+ "no real-device verification",
+ "no long-horizon autonomous operation claim",
+ "host/resource telemetry is digital interoception, not biological interoception",
+ "resource accounting is not biological metabolism",
+ "H-EMB-001-B remains untested by this protocol"
+ ]
+ },
+ "contract": {
+ "stage": 5,
+ "slug": "integrated_artificial_nervous_system",
+ "title": "Integriertes künstliches Nervensystem",
+ "scope": "engineering_verification",
+ "layers": {
+ "sensorik": {
+ "implemented": true,
+ "paths": [
+ "src/embodiment/sensor.py",
+ "src/embodiment/system_sensor.py",
+ "src/embodiment/sensor_activation.py"
+ ],
+ "claim": "typed and operator-controlled sensor boundary"
+ },
+ "interozeption": {
+ "implemented": true,
+ "paths": [
+ "src/embodiment/interoception.py"
+ ],
+ "claim": "typed host/resource observations with uncertainty"
+ },
+ "aktorik": {
+ "implemented": true,
+ "paths": [
+ "src/embodiment/controlled.py",
+ "src/embodiment/actuator_hub.py",
+ "src/embodiment/audit.py"
+ ],
+ "claim": "authorized commands with acceptance/effect receipts"
+ },
+ "feedback": {
+ "implemented": true,
+ "paths": [
+ "src/experience/engine.py",
+ "src/embodiment/deterministic.py"
+ ],
+ "claim": "deterministic synthetic sensor-SNN-action-observation loop"
+ },
+ "ressourcenhaushalt": {
+ "implemented": true,
+ "paths": [
+ "src/embodiment/interoception.py",
+ "src/embodiment/msba.py"
+ ],
+ "claim": "bounded resource pressure, uncertainty and energy accounting"
+ }
+ },
+ "runtime_projection": {
+ "state": "/api/embodiment/state",
+ "pipeline": "/api/embodiment/pipeline",
+ "metrics": "/api/embodiment/metrics",
+ "history": "/api/embodiment/history",
+ "connections": "/api/embodiment/connections",
+ "sensors": "/api/embodiment/sensors"
+ },
+ "experiment": {
+ "path": "research/experiments/EXP-STAGE5-20260914-INTEGRATED-NERVOUS-SYSTEM",
+ "research_question": "RQ-EMB-001",
+ "hypotheses": [
+ "H-EMB-001-A",
+ "H-EMB-001-B"
+ ],
+ "status": "DATA",
+ "automatic_evidence_promotion": false
+ },
+ "boundaries": {
+ "productive_external_actuation_enabled": false,
+ "real_device_verified": false,
+ "long_horizon_verified": false,
+ "biological_interoception_claim": false,
+ "biological_metabolism_claim": false,
+ "conscious_experience_claim": false,
+ "automatic_evidence_promotion": false
+ },
+ "reference_probe": {
+ "frame": {
+ "tick": 1,
+ "signals": [
+ {
+ "name": "actuator_confidence",
+ "value": 1.0,
+ "unit": "ratio",
+ "safe_range": null,
+ "warning_range": null,
+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "continuity",
+ "recoverability": "unknown",
+ "status": "nominal"
+ },
+ {
+ "name": "battery_percent",
+ "value": 80.0,
+ "unit": "%",
+ "safe_range": null,
+ "warning_range": [
+ 20.0,
+ 100.0
+ ],
+ "critical_range": [
+ 5.0,
+ 100.0
+ ],
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "continuity",
+ "recoverability": "unknown",
+ "status": "nominal"
+ },
+ {
+ "name": "battery_plugged",
+ "value": null,
+ "unit": "bool",
+ "safe_range": null,
+ "warning_range": null,
+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "continuity",
+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "cpu_percent",
+ "value": 42.0,
+ "unit": "%",
+ "safe_range": null,
+ "warning_range": [
+ 0.0,
+ 85.0
+ ],
+ "critical_range": [
+ 0.0,
+ 98.0
+ ],
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "informational",
+ "recoverability": "unknown",
+ "status": "nominal"
+ },
+ {
+ "name": "disk_free_bytes",
+ "value": null,
+ "unit": "bytes",
+ "safe_range": null,
+ "warning_range": null,
+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "continuity",
+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "disk_read_bytes",
+ "value": null,
+ "unit": "bytes",
+ "safe_range": null,
+ "warning_range": null,
+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "resource",
+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "disk_write_bytes",
+ "value": null,
+ "unit": "bytes",
+ "safe_range": null,
+ "warning_range": null,
+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "resource",
+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "fan_rpm",
+ "value": null,
+ "unit": "rpm",
+ "safe_range": null,
+ "warning_range": null,
+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
+ "source": "system_sensor",
+ "causal_criticality": "thermal",
+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "memory_available_bytes",
+ "value": null,
+ "unit": "bytes",
+ "safe_range": null,
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+ "confidence": 1.0,
+ "freshness": "current",
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+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "memory_percent",
+ "value": 55.0,
+ "unit": "%",
+ "safe_range": null,
+ "warning_range": [
+ 0.0,
+ 85.0
+ ],
+ "critical_range": [
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+ 98.0
+ ],
+ "confidence": 1.0,
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+ "causal_criticality": "informational",
+ "recoverability": "unknown",
+ "status": "nominal"
+ },
+ {
+ "name": "network_bytes_received",
+ "value": null,
+ "unit": "bytes",
+ "safe_range": null,
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+ "critical_range": null,
+ "confidence": 1.0,
+ "freshness": "current",
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+ "causal_criticality": "continuity",
+ "recoverability": "unknown",
+ "status": "unknown"
+ },
+ {
+ "name": "network_bytes_sent",
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+ "unit": "bytes",
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+ "confidence": 1.0,
+ "freshness": "current",
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+ "recoverability": "unknown",
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+ {
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+ "status": "nominal"
+ },
+ {
+ "name": "novelty",
+ "value": 0.25,
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+ "freshness": "current",
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+ },
+ {
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+ },
+ {
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+ ],
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+ }
+ ]
+ },
+ "drives": {
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+ "drives": {
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+ "sensory_integrity": 1.0,
+ "continuity_risk": 0.0,
+ "task_progress": 0.5,
+ "novelty": 0.25,
+ "actuator_confidence": 1.0
+ },
+ "uncertainty": {
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+ "continuity_risk": 0.0,
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+ "actuator_confidence": 0.0
+ }
+ },
+ "regulatory": {
+ "tick": 1,
+ "values": {
+ "thermal_margin": 1.0,
+ "energy_reserve": 1.0,
+ "continuity_risk": 0.0,
+ "sensory_integrity": 1.0,
+ "resource_pressure": 0.0,
+ "task_progress": 0.5
+ },
+ "uncertainty": {
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+ "energy_reserve": 0.0,
+ "continuity_risk": 0.0,
+ "sensory_integrity": 0.0,
+ "resource_pressure": 0.0,
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+ }
+ },
+ "functional": {
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+ "valence": 1.0,
+ "activation": 0.125,
+ "safety": 1.0,
+ "uncertainty": 0.0
+ }
+ }
+ },
+ "scientific_status": {
+ "RQ-EMB-001": "open",
+ "H-EMB-001-A": "DATA-supported, not accepted EVID",
+ "H-EMB-001-B": "untested by Stage-5 reference protocol"
+ }
+}
diff --git a/research/generated/verification/phase_b_gate_status.json b/research/generated/verification/phase_b_gate_status.json
new file mode 100644
index 0000000000000000000000000000000000000000..fc0cdc6c72fd4977e4990a2af949b7f2fcf99074
--- /dev/null
+++ b/research/generated/verification/phase_b_gate_status.json
@@ -0,0 +1,56 @@
+{
+ "baseline": {
+ "artifact": "tests/test_baseline.json",
+ "collection_errors": 0,
+ "failed": 0,
+ "passed": 1665,
+ "python": "3.13.15",
+ "skipped": 2
+ },
+ "continuous_integration": {
+ "conclusion": "success",
+ "head_sha": "131fccfe92e5a4c4adb1e51119b7ab50d14b255b",
+ "run_id": 35506900245,
+ "workflow": "EMB-002-A proprioception direct V3 #5"
+ },
+ "final_validation": {
+ "command": "python scripts/generate_baseline.py",
+ "failed": 0,
+ "passed": 1665,
+ "skipped": 2
+ },
+ "generated": "2026-09-20T11:11:52.033103+00:00",
+ "limitations": [
+ "Scientific evidence status remains independent from release engineering readiness.",
+ "External Hugging Face proxy smoke remains non-blocking operational follow-up."
+ ],
+ "release_readiness": "ready",
+ "schema_version": 1,
+ "scientific_gate": {
+ "gate_a": {
+ "passed": 22,
+ "total": 22
+ },
+ "gate_b": {
+ "passed": 24,
+ "total": 24
+ },
+ "gate_c": {
+ "passed": 17,
+ "total": 17
+ },
+ "overall": "passed"
+ },
+ "source_freeze": {
+ "commit": "131fccfe92e5a4c4adb1e51119b7ab50d14b255b",
+ "tested_tree_digest": "c6e64d9d4043e250071170efc11a3a64086c63d25a94f7e79d38fc913d094cdc"
+ },
+ "status": "verified",
+ "verification_artifacts": [
+ "research/generated/verification/restore_determinism.json",
+ "research/generated/verification/structural_e2e.json",
+ "research/generated/verification/structural_live_loop.json",
+ "research/generated/verification/single_listener.json",
+ "research/generated/verification/determinism_infrastructure.json"
+ ]
+}
diff --git a/research/generated/verification/plastic_neural_tissue_reference.json b/research/generated/verification/plastic_neural_tissue_reference.json
new file mode 100644
index 0000000000000000000000000000000000000000..6acd32ba4e3880bbb8e104ca6571caf69d8d3737
--- /dev/null
+++ b/research/generated/verification/plastic_neural_tissue_reference.json
@@ -0,0 +1,156 @@
+{
+ "conditions": {
+ "learning_off": {
+ "baseline_probes_executed": 1,
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "condition": "learning_off",
+ "final_mean_weight": 0.05,
+ "holdout_episodes_executed": 0,
+ "holdout_trial_count": 4,
+ "initial_mean_weight": 0.05,
+ "learned": false,
+ "mean_weight_delta": 0.0,
+ "partition_counts_are_declared": true,
+ "post_training_probes_executed": 1,
+ "presynaptic_neurons": 48,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1,
+ "reward_weight_updates": 0,
+ "rewards_applied": 0,
+ "rewards_received": 0,
+ "train_trial_count": 12,
+ "trained_target_peak_v": -65.0,
+ "trained_target_spike_tick": null,
+ "trained_target_spiked": false,
+ "training_trials": 20,
+ "validation_episodes_executed": 0,
+ "validation_trial_count": 4
+ },
+ "learning_on": {
+ "baseline_probes_executed": 1,
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "condition": "learning_on",
+ "final_mean_weight": 0.517280469842843,
+ "holdout_episodes_executed": 0,
+ "holdout_trial_count": 4,
+ "initial_mean_weight": 0.05,
+ "learned": true,
+ "mean_weight_delta": 0.46728046984284305,
+ "partition_counts_are_declared": true,
+ "post_training_probes_executed": 1,
+ "presynaptic_neurons": 48,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1,
+ "reward_weight_updates": 576,
+ "rewards_applied": 12,
+ "rewards_received": 12,
+ "train_trial_count": 12,
+ "trained_target_peak_v": 26.239795413974708,
+ "trained_target_spike_tick": 4,
+ "trained_target_spiked": true,
+ "training_trials": 20,
+ "validation_episodes_executed": 0,
+ "validation_trial_count": 4
+ },
+ "sham_replay": {
+ "baseline_probes_executed": 1,
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "condition": "sham_replay",
+ "final_mean_weight": 0.05,
+ "holdout_episodes_executed": 0,
+ "holdout_trial_count": 4,
+ "initial_mean_weight": 0.05,
+ "learned": false,
+ "mean_weight_delta": 0.0,
+ "partition_counts_are_declared": true,
+ "post_training_probes_executed": 1,
+ "presynaptic_neurons": 48,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1,
+ "reward_weight_updates": 0,
+ "rewards_applied": 12,
+ "rewards_received": 12,
+ "train_trial_count": 12,
+ "trained_target_peak_v": -65.0,
+ "trained_target_spike_tick": null,
+ "trained_target_spiked": false,
+ "training_trials": 20,
+ "validation_episodes_executed": 0,
+ "validation_trial_count": 4
+ }
+ },
+ "limits": [
+ "The reference uses the bounded learning-experiment topology rather than the declared 10,000-100,000 neuron Stage-3 scale.",
+ "Passing the reference does not establish long-horizon stability for arbitrary plastic networks or reward formulations.",
+ "Structural plasticity is verified by the existing deterministic E2E suite; this runner does not claim biological tissue equivalence.",
+ "No cognition or consciousness inference follows from plasticity verification."
+ ],
+ "proofs": {
+ "deterministic_learning_replay_identity": true,
+ "finite_bounded_weight_summary": true,
+ "homeostasis_tests_passed": true,
+ "learning_off_control_has_no_reward_updates": true,
+ "learning_on_changes_fresh_probe_response": true,
+ "learning_state_checkpoint_tests_passed": true,
+ "sham_replay_destroys_eligibility_effect": true,
+ "stdp_and_three_factor_tests_passed": true,
+ "structural_plasticity_tests_passed": true,
+ "three_factor_learning_changes_weights": true
+ },
+ "protocol": {
+ "conditions": [
+ "learning_on",
+ "learning_off",
+ "sham_replay"
+ ],
+ "config": "configs/learning_experiment.yaml",
+ "deterministic_replay": true,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1
+ },
+ "schema_version": 1,
+ "scientific_promotion": {
+ "automatic_evidence_promotion": false,
+ "note": "This reference closes a scoped engineering verification gap only. Preregistered independent learning-on/off/holdout runs and human evidence review remain required for scientific claims."
+ },
+ "scope": "engineering_verification",
+ "software_version": "0.6.0a2",
+ "stage": 3,
+ "status": "verified",
+ "suite": "plastic_neural_tissue_reference",
+ "test_groups": {
+ "homeostasis": {
+ "passed": true,
+ "paths": [
+ "tests/test_homeostasis_engine.py"
+ ]
+ },
+ "learning_state_checkpoint": {
+ "passed": true,
+ "paths": [
+ "tests/test_checkpoint_v4.py"
+ ]
+ },
+ "stdp_and_three_factor": {
+ "passed": true,
+ "paths": [
+ "tests/test_stdp_integration.py",
+ "tests/test_reward.py",
+ "tests/test_learning_experiment.py"
+ ]
+ },
+ "structural_plasticity": {
+ "passed": true,
+ "paths": [
+ "tests/test_structural_e2e.py",
+ "tests/test_structural_determinism.py"
+ ]
+ }
+ }
+}
diff --git a/research/generated/verification/plastic_neural_tissue_reference_alpha3.json b/research/generated/verification/plastic_neural_tissue_reference_alpha3.json
new file mode 100644
index 0000000000000000000000000000000000000000..6de4bc53d70d53b03fec7c40dddbed21340aa976
--- /dev/null
+++ b/research/generated/verification/plastic_neural_tissue_reference_alpha3.json
@@ -0,0 +1,157 @@
+{
+ "conditions": {
+ "learning_off": {
+ "baseline_probes_executed": 1,
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "condition": "learning_off",
+ "final_mean_weight": 0.05,
+ "holdout_episodes_executed": 0,
+ "holdout_trial_count": 4,
+ "initial_mean_weight": 0.05,
+ "learned": false,
+ "mean_weight_delta": 0.0,
+ "partition_counts_are_declared": true,
+ "post_training_probes_executed": 1,
+ "presynaptic_neurons": 48,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1,
+ "reward_weight_updates": 0,
+ "rewards_applied": 0,
+ "rewards_received": 0,
+ "train_trial_count": 12,
+ "trained_target_peak_v": -65.0,
+ "trained_target_spike_tick": null,
+ "trained_target_spiked": false,
+ "training_trials": 20,
+ "validation_episodes_executed": 0,
+ "validation_trial_count": 4
+ },
+ "learning_on": {
+ "baseline_probes_executed": 1,
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "condition": "learning_on",
+ "final_mean_weight": 0.517280469842843,
+ "holdout_episodes_executed": 0,
+ "holdout_trial_count": 4,
+ "initial_mean_weight": 0.05,
+ "learned": true,
+ "mean_weight_delta": 0.46728046984284305,
+ "partition_counts_are_declared": true,
+ "post_training_probes_executed": 1,
+ "presynaptic_neurons": 48,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1,
+ "reward_weight_updates": 576,
+ "rewards_applied": 12,
+ "rewards_received": 12,
+ "train_trial_count": 12,
+ "trained_target_peak_v": 26.239795413974708,
+ "trained_target_spike_tick": 4,
+ "trained_target_spiked": true,
+ "training_trials": 20,
+ "validation_episodes_executed": 0,
+ "validation_trial_count": 4
+ },
+ "sham_replay": {
+ "baseline_probes_executed": 1,
+ "baseline_target_peak_v": -65.0,
+ "baseline_target_spike_tick": null,
+ "baseline_target_spiked": false,
+ "condition": "sham_replay",
+ "final_mean_weight": 0.05,
+ "holdout_episodes_executed": 0,
+ "holdout_trial_count": 4,
+ "initial_mean_weight": 0.05,
+ "learned": false,
+ "mean_weight_delta": 0.0,
+ "partition_counts_are_declared": true,
+ "post_training_probes_executed": 1,
+ "presynaptic_neurons": 48,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1,
+ "reward_weight_updates": 0,
+ "rewards_applied": 12,
+ "rewards_received": 12,
+ "train_trial_count": 12,
+ "trained_target_peak_v": -65.0,
+ "trained_target_spike_tick": null,
+ "trained_target_spiked": false,
+ "training_trials": 20,
+ "validation_episodes_executed": 0,
+ "validation_trial_count": 4
+ }
+ },
+ "limits": [
+ "The reference uses the bounded learning-experiment topology rather than the declared 10,000-100,000 neuron Stage-3 scale.",
+ "Passing the reference does not establish long-horizon stability for arbitrary plastic networks or reward formulations.",
+ "Structural plasticity is verified by the existing deterministic E2E suite; this runner does not claim biological tissue equivalence.",
+ "No cognition or consciousness inference follows from plasticity verification."
+ ],
+ "proofs": {
+ "deterministic_learning_replay_identity": true,
+ "finite_bounded_weight_summary": true,
+ "homeostasis_tests_passed": true,
+ "learning_off_control_has_no_reward_updates": true,
+ "learning_on_changes_fresh_probe_response": true,
+ "learning_state_checkpoint_tests_passed": true,
+ "sham_replay_destroys_eligibility_effect": true,
+ "stdp_and_three_factor_tests_passed": true,
+ "structural_plasticity_tests_passed": true,
+ "three_factor_learning_changes_weights": true
+ },
+ "protocol": {
+ "conditions": [
+ "learning_on",
+ "learning_off",
+ "sham_replay"
+ ],
+ "config": "configs/learning_experiment.yaml",
+ "deterministic_replay": true,
+ "protocol_id": "PROTOCOL-STDP-0002",
+ "protocol_version": 1
+ },
+ "schema_version": 1,
+ "scientific_promotion": {
+ "automatic_evidence_promotion": false,
+ "note": "This reference closes a scoped engineering verification gap only. Preregistered independent learning-on/off/holdout runs and human evidence review remain required for scientific claims."
+ },
+ "scope": "engineering_verification",
+ "software_version": "0.6.0a3",
+ "stage": 3,
+ "status": "verified",
+ "suite": "plastic_neural_tissue_reference",
+ "test_groups": {
+ "homeostasis": {
+ "passed": true,
+ "paths": [
+ "tests/test_homeostasis_engine.py"
+ ]
+ },
+ "learning_state_checkpoint": {
+ "passed": true,
+ "paths": [
+ "tests/test_checkpoint_v4.py"
+ ]
+ },
+ "stdp_and_three_factor": {
+ "passed": true,
+ "paths": [
+ "tests/test_stdp_integration.py",
+ "tests/test_reward.py",
+ "tests/test_learning_experiment.py"
+ ]
+ },
+ "structural_plasticity": {
+ "passed": true,
+ "paths": [
+ "tests/test_structural_e2e.py",
+ "tests/test_structural_determinism.py"
+ ]
+ }
+ },
+ "tests_executed": true
+}
diff --git a/research/generated/verification/recurrent_snn_reference.json b/research/generated/verification/recurrent_snn_reference.json
new file mode 100644
index 0000000000000000000000000000000000000000..1586a56a7cacc8120ba224ff368e7304141106c3
--- /dev/null
+++ b/research/generated/verification/recurrent_snn_reference.json
@@ -0,0 +1,45 @@
+{
+ "schema_version": 1,
+ "suite": "recurrent_snn_reference",
+ "stage": 2,
+ "status": "verified",
+ "scope": "software_reference_contract",
+ "reference": {
+ "topology": "A -> B -> C -> D -> A",
+ "neurons": 4,
+ "synapses": 4,
+ "delay_ticks": 1,
+ "weight": 200.0,
+ "ticks": 20000,
+ "neuron_model": "izhikevich-2003",
+ "secondary_mechanisms": "disabled"
+ },
+ "expected": {
+ "spikes_per_tick": 1,
+ "total_spikes": 20000,
+ "events_processed": 19999,
+ "queued_events_after_run": 1,
+ "max_queue_depth": 1,
+ "per_neuron_spikes": 5000
+ },
+ "proofs": {
+ "closed_recurrent_topology": true,
+ "bounded_event_queue": true,
+ "finite_membrane_state": true,
+ "sustained_spike_activity": true,
+ "long_run_completed": true,
+ "deterministic_replay_identity": true
+ },
+ "related_research": {
+ "research_question": "RQ-SNN-001",
+ "existing_experiment": "EXP-BATCH-20260908200906-01",
+ "existing_experiment_ticks_per_run": 100000,
+ "note": "The reference contract is an engineering verification. The registered experiment remains DATA/analysis and is not automatically promoted to scientific EVID."
+ },
+ "limits": [
+ "The reference ring is deliberately small and strongly coupled.",
+ "Passing this contract does not prove stability for arbitrary recurrent topology, weights, delays or neuron models.",
+ "Passing this contract does not establish Stage-2 target-scale tractability at 1,000-10,000 neurons.",
+ "Deterministic sustained activity is not evidence of cognition or biological equivalence."
+ ]
+}
diff --git a/research/generated/verification/restore_determinism.json b/research/generated/verification/restore_determinism.json
new file mode 100644
index 0000000000000000000000000000000000000000..868967b2ba1201aecb145c9d78303bd3087c4f92
--- /dev/null
+++ b/research/generated/verification/restore_determinism.json
@@ -0,0 +1,31 @@
+{
+ "schema_version": 1,
+ "suite": "restore_determinism",
+ "status": "verified",
+ "test_run_head": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_commit": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_tree_digest": "c6e64d9d4043e250071170efc11a3a64086c63d25a94f7e79d38fc913d094cdc",
+ "scope": "restore_determinism",
+ "scope_digest": "62281a2f6208f9eabd4334c8054e2a9cca066715523823ce232846bd65cefe1a",
+ "timestamp": "2026-09-20T11:10:34+0000",
+ "python": "3.13.15 (main, Aug 6 2026, 02:15:18) [GCC 13.3.0]",
+ "K": 500,
+ "N": 1000,
+ "seed": 42,
+ "config_sha256": "b9dbcba2952663b8f75c839bcf935e66539f35c7ddcc1294f4157f869aae42d4",
+ "pid_C1": 12117,
+ "pid_C2": 12121,
+ "digest_A": "eca7a8b2b81bc9bf3045933bd12113a31204d01355636bfd0e4432f11bed77b7",
+ "digest_B": "eca7a8b2b81bc9bf3045933bd12113a31204d01355636bfd0e4432f11bed77b7",
+ "digest_C": "eca7a8b2b81bc9bf3045933bd12113a31204d01355636bfd0e4432f11bed77b7",
+ "proofs": {
+ "uninterrupted_completed": true,
+ "in_process_restore_completed": true,
+ "fresh_process_restore_completed": true,
+ "A_equals_B": true,
+ "A_equals_C": true,
+ "B_equals_C": true,
+ "fresh_process_is_real": true,
+ "production_restore_path_used": true
+ }
+}
\ No newline at end of file
diff --git a/research/generated/verification/single_listener.json b/research/generated/verification/single_listener.json
new file mode 100644
index 0000000000000000000000000000000000000000..04df58c85fb0d3c033221656cf8c8dba85584ffe
--- /dev/null
+++ b/research/generated/verification/single_listener.json
@@ -0,0 +1,22 @@
+{
+ "schema_version": 1,
+ "suite": "single_listener",
+ "status": "verified",
+ "timestamp": "2026-09-20T11:10:57.740711",
+ "python_version": "3.13.15",
+ "test_run_head": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_commit": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_tree_digest": "c6e64d9d4043e250071170efc11a3a64086c63d25a94f7e79d38fc913d094cdc",
+ "scope": "runtime_integration",
+ "scope_digest": "a8aa5e085d359daf039882919f138085c3987fcffb839617335b3332b5fec49f",
+ "test_command": "python -m pytest tests/test_single_listener.py -q",
+ "proofs": {
+ "port_initially_free_or_explicitly_rejected": true,
+ "brain5d_process_started": true,
+ "listener_pid_matches_process_pid": true,
+ "exactly_one_listener_socket": true,
+ "no_other_listener_pid": true,
+ "healthz_reachable": true,
+ "process_alive_during_verification": true
+ }
+}
\ No newline at end of file
diff --git a/research/generated/verification/single_neuron_reference.json b/research/generated/verification/single_neuron_reference.json
new file mode 100644
index 0000000000000000000000000000000000000000..a1074d50e391cfcc61236c2b85e33e09521c71ad
--- /dev/null
+++ b/research/generated/verification/single_neuron_reference.json
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "status": "verified",
+ "scientific_scope": "software reference trajectory only; not biological validation",
+ "model": {
+ "id": "izhikevich-2003",
+ "version": "mhrn-1.0",
+ "canonical": true
+ },
+ "protocol": {
+ "dt_ms": 1.0,
+ "input_current": 10.0,
+ "ticks": 10,
+ "initial_v": -65.0,
+ "initial_u": -13.0,
+ "secondary_mechanisms": false,
+ "refractory_ticks": 0
+ },
+ "trajectory": [
+ {
+ "tick": 0,
+ "v": -58.105000000000004,
+ "u": -12.97242,
+ "spike": false
+ },
+ {
+ "tick": 1,
+ "v": -49.67024344113139,
+ "u": -12.911652573764526,
+ "spike": false
+ },
+ {
+ "tick": 2,
+ "v": -32.148436920936334,
+ "u": -12.78201326997298,
+ "spike": false
+ },
+ {
+ "tick": 3,
+ "v": -65.0,
+ "u": -4.338472415828637,
+ "spike": true
+ },
+ {
+ "tick": 4,
+ "v": -66.56464783539798,
+ "u": -4.517961558853656,
+ "spike": false
+ },
+ {
+ "tick": 5,
+ "v": -67.54301490883427,
+ "u": -4.69777438731192,
+ "spike": false
+ },
+ {
+ "tick": 6,
+ "v": -68.02257493597325,
+ "u": -4.875909199309575,
+ "spike": false
+ },
+ {
+ "tick": 7,
+ "v": -68.15956887629694,
+ "u": -5.051029290828572,
+ "spike": false
+ },
+ {
+ "tick": 8,
+ "v": -68.08658786488807,
+ "u": -5.222355056471552,
+ "spike": false
+ },
+ {
+ "tick": 9,
+ "v": -67.89025577811208,
+ "u": -5.38946897845457,
+ "spike": false
+ }
+ ],
+ "proofs_required": [
+ "golden_trajectory",
+ "deterministic_replay",
+ "spike_reset_recovery",
+ "optional_refractory_variant",
+ "state_roundtrip",
+ "model_switch_provenance"
+ ],
+ "verification_scope": "Stage-0 software contract; not biological validation",
+ "proofs": {
+ "golden_trajectory": true,
+ "deterministic_replay": true,
+ "spike_reset_recovery": true,
+ "optional_refractory_variant": true,
+ "state_roundtrip": true,
+ "model_switch_provenance": true,
+ "yaml_model_selection": true
+ }
+}
diff --git a/research/generated/verification/single_neuron_scientific_readiness.json b/research/generated/verification/single_neuron_scientific_readiness.json
new file mode 100644
index 0000000000000000000000000000000000000000..e52cffb674a016c74c6bf0c108b472f4522b7cd6
--- /dev/null
+++ b/research/generated/verification/single_neuron_scientific_readiness.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "stage": 0,
+ "stage_id": "single_neuron",
+ "name": "Einzelne Nervenzelle",
+ "scope": "Scoped scientific readiness of the declared single-neuron primitive and selectable canonical models; not overall scientific maturity and not biological equivalence.",
+ "status": "passed",
+ "research_readiness_score": 1,
+ "research_readiness_percent": 100,
+ "as_of": "2026-09-16",
+ "proofs": {
+ "firing_rate_estimate_has_hz_semantics": true,
+ "homeostasis_operating_envelope_documented": true,
+ "triplet_flag_cannot_silently_claim_unimplemented_behavior": true,
+ "izhikevich_external_reference_conformance_confirmed": true,
+ "lif_external_reference_conformance_confirmed": true,
+ "lif_refractory_semantics_mapped": true,
+ "model_axis_registered_with_separate_hypotheses": true,
+ "historical_negative_preserved": true,
+ "confirmatory_protocol_frozen_before_validation": true,
+ "confirmatory_seeds_disjoint_from_diagnostic_seeds": true
+ },
+ "sources": [
+ "research/preregistrations/operational/single_neuron_conformance_v2.json",
+ "research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/DATA/confirmatory_result.json",
+ "research/experiments/EXP-STAGE0-20260916-MODEL-CONFORMANCE-V2/report.md",
+ "research/specifications/NEURON_MODEL_EXPERIMENT_AXIS.md",
+ "research/registry/questions.empirical.yaml",
+ "research/registry/hypotheses.empirical.yaml",
+ "tests/test_single_neuron_contract.py",
+ "research/experiments/EXP-STAGE0-20260918-MODEL-CONFORMANCE-V2-PROMO-R1/human_review.json",
+ "research/registry/evidence/EVID-2026-18.json"
+ ],
+ "external_reference": {
+ "framework": "Brian2",
+ "version": "2.10.1",
+ "confirmatory_run_id": 35149828152,
+ "artifact_id": 10468901959,
+ "artifact_sha256": "1c7e430fa75edb02ef7bfbaacf86cc85699d7a9ab5a6154bb81ce2da7bb0c89c"
+ },
+ "maturity_boundary": {
+ "human_reviewed_evid_complete": true,
+ "independent_authorship_replication_complete": false,
+ "note": "Canonical human review and EvidenceEngine promotion are complete for scoped CLAIM-EVAL-006 via EVID-2026-18. Independent authorship replication remains incomplete and is intentionally not inferred from Brian2 reference conformance or the internal promotion replication."
+ }
+}
diff --git a/research/generated/verification/small_snn_reference.json b/research/generated/verification/small_snn_reference.json
new file mode 100644
index 0000000000000000000000000000000000000000..7b1efefd11297d81fff21fd97a62fca3aec242db
--- /dev/null
+++ b/research/generated/verification/small_snn_reference.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "status": "verified",
+ "stage": 1,
+ "id": "small_snn",
+ "scientific_scope": "software reference network only; not biological validation or scaling evidence",
+ "protocol": {
+ "neurons": 3,
+ "synapses": 2,
+ "topology": "feed-forward chain A -> B -> C",
+ "dt_ms": 1.0,
+ "model": "izhikevich-2003",
+ "secondary_mechanisms": false,
+ "reference_synaptic_bounds": {
+ "w_min": 0.0,
+ "w_max": 100.0,
+ "scope": "verification fixture only; production defaults unchanged"
+ },
+ "connections": [
+ {"source": "A", "target": "B", "weight": 100.0, "delay_ticks": 1},
+ {"source": "B", "target": "C", "weight": 100.0, "delay_ticks": 2}
+ ],
+ "initial_trigger": "A.v = 30 mV at tick 0"
+ },
+ "expected_causal_trace": [
+ {"tick": 0, "spikes": ["A"], "delivered_events": 0, "queued_events": 1},
+ {"tick": 1, "spikes": ["B"], "delivered_events": 1, "queued_events": 1},
+ {"tick": 2, "spikes": [], "delivered_events": 0, "queued_events": 1},
+ {"tick": 3, "spikes": ["C"], "delivered_events": 1, "queued_events": 0}
+ ],
+ "proofs_required": [
+ "explicit_sparse_topology",
+ "delayed_event_delivery",
+ "causal_spike_propagation",
+ "deterministic_replay",
+ "batch_tick_equivalence"
+ ],
+ "proofs": {
+ "explicit_sparse_topology": true,
+ "delayed_event_delivery": true,
+ "causal_spike_propagation": true,
+ "deterministic_replay": true,
+ "batch_tick_equivalence": true
+ },
+ "verification_scope": "Stage-1 software contract; not evidence of biological equivalence, cognition, learning quality or large-scale tractability"
+}
diff --git a/research/generated/verification/specialized_neural_areas_reference_alpha3.json b/research/generated/verification/specialized_neural_areas_reference_alpha3.json
new file mode 100644
index 0000000000000000000000000000000000000000..688e625c7a34cf0699ec1a1f4d4acd1f61f4a64a
--- /dev/null
+++ b/research/generated/verification/specialized_neural_areas_reference_alpha3.json
@@ -0,0 +1,351 @@
+{
+ "contract": {
+ "areas": [
+ {
+ "activation_scope": "experiment_only",
+ "adapter_transform": "threshold_features",
+ "area_id": "stage4.audio.temporal",
+ "coordinate_features": [
+ "band",
+ "phase_or_envelope",
+ "channel",
+ "lag",
+ "feature"
+ ],
+ "exact_payload_outside_snn": false,
+ "mean_fanout": 96,
+ "modality": "audio",
+ "name": "Auditory Temporal Area",
+ "neuron_budget": 25000,
+ "pathway": "temporal_coherence",
+ "plasticity_rule": "phase_weighted_t_stdp_candidate",
+ "synapse_budget": 2400000,
+ "throttles": [
+ "band_count",
+ "update_rate",
+ "delay_taps"
+ ]
+ },
+ {
+ "activation_scope": "experiment_only",
+ "adapter_transform": "threshold_features",
+ "area_id": "stage4.vision.spatial",
+ "coordinate_features": [
+ "x",
+ "y",
+ "feature",
+ "scale",
+ "frame"
+ ],
+ "exact_payload_outside_snn": false,
+ "mean_fanout": 128,
+ "modality": "vision",
+ "name": "Visual Spatial Area",
+ "neuron_budget": 50000,
+ "pathway": "spatial_multiplex",
+ "plasticity_rule": "s_stdp_plus_structural_growth_candidate",
+ "synapse_budget": 6400000,
+ "throttles": [
+ "fps",
+ "resolution",
+ "roi",
+ "feature_channels"
+ ]
+ },
+ {
+ "activation_scope": "experiment_only",
+ "adapter_transform": "identity",
+ "area_id": "stage4.digital.symbolic",
+ "coordinate_features": [
+ "symbol",
+ "sequence",
+ "source",
+ "context",
+ "route"
+ ],
+ "exact_payload_outside_snn": true,
+ "mean_fanout": 48,
+ "modality": "digital",
+ "name": "Digital Symbolic Area",
+ "neuron_budget": 25000,
+ "pathway": "quantized_high_fidelity",
+ "plasticity_rule": "population_meta_gating_candidate",
+ "synapse_budget": 1200000,
+ "throttles": [
+ "symbol_rate",
+ "batching",
+ "admission"
+ ]
+ }
+ ],
+ "id": "specialized_neural_areas",
+ "name": "Spezialisierte neuronale Areale",
+ "reference_probes": {
+ "probe_sha256": "b11071bb091f71f525274ae45325d44f3cdbeee7d4bc635de94d9c861eacf839",
+ "probes": {
+ "audio": {
+ "adapter_provenance": {
+ "adapter_class": "DeterministicPeripheralAdapter",
+ "area_id": "stage4.audio.temporal",
+ "artifact_sha256": "5126553c2856eaeaa592855f6fe43049f736a6c588618d336e8d8133a652b5ff",
+ "endpoint_identity": "experiment://stage4/audio",
+ "framework": "python-reference",
+ "modality": "audio",
+ "model": "stage4-audio-area-reference",
+ "production_activation_enabled": false,
+ "provenance_sha256": "16dae0cb8f44ae86905223aec115c8602d94f688af808d848683326c88209cbf",
+ "scope": "experiment_only",
+ "transform": "threshold_features",
+ "version": "1.0.0"
+ },
+ "adapter_transform": "threshold_features",
+ "area_id": "stage4.audio.temporal",
+ "candidate_weight_delta": 0.009850712500726659,
+ "canonical_core_mutated": false,
+ "encoded_features": [
+ 1,
+ -1,
+ 1,
+ -1
+ ],
+ "modality": "audio",
+ "pathway": "temporal_coherence",
+ "plasticity_rule": "phase_weighted_t_stdp_candidate",
+ "productive_activation_enabled": false,
+ "tick": 17
+ },
+ "digital": {
+ "adapter_provenance": {
+ "adapter_class": "DeterministicPeripheralAdapter",
+ "area_id": "stage4.digital.symbolic",
+ "artifact_sha256": "f0d3767214ba8d9f1f2e21caaf2119252b5d16dcb055223cad02ebda6887833d",
+ "endpoint_identity": "experiment://stage4/digital",
+ "framework": "python-reference",
+ "modality": "digital",
+ "model": "stage4-digital-area-reference",
+ "production_activation_enabled": false,
+ "provenance_sha256": "42938015c044885f377bca98829b58e418cdd541819c041afb37a33808dc1622",
+ "scope": "experiment_only",
+ "transform": "identity",
+ "version": "1.0.0"
+ },
+ "adapter_transform": "identity",
+ "area_id": "stage4.digital.symbolic",
+ "canonical_core_mutated": false,
+ "exact_integrity_pass": true,
+ "exact_payload_equal": true,
+ "input_sha256": "af1084eba26a093e76aade9b98a97296139e4636d40de745776ddd6a84c9b03c",
+ "modality": "digital",
+ "output_sha256": "af1084eba26a093e76aade9b98a97296139e4636d40de745776ddd6a84c9b03c",
+ "pathway": "quantized_high_fidelity",
+ "plasticity_rule": "population_meta_gating_candidate",
+ "population_code": [
+ 1,
+ 0,
+ 0,
+ 1,
+ 0,
+ 0,
+ 1,
+ 0
+ ],
+ "productive_activation_enabled": false,
+ "tick": 17
+ },
+ "vision": {
+ "adapter_provenance": {
+ "adapter_class": "DeterministicPeripheralAdapter",
+ "area_id": "stage4.vision.spatial",
+ "artifact_sha256": "0214dddc02501da5f9ff92cc9d961725639fef7906050ad92a399aa2f5313857",
+ "endpoint_identity": "experiment://stage4/vision",
+ "framework": "python-reference",
+ "modality": "vision",
+ "model": "stage4-vision-area-reference",
+ "production_activation_enabled": false,
+ "provenance_sha256": "2eefdbfbdeb89ad794a1ca2dff16e0ec22b885379e0de2c205a93619cf755cc0",
+ "scope": "experiment_only",
+ "transform": "threshold_features",
+ "version": "1.0.0"
+ },
+ "adapter_transform": "threshold_features",
+ "area_id": "stage4.vision.spatial",
+ "candidate_growth_probability": 0.00041183188787281117,
+ "canonical_core_mutated": false,
+ "encoded_features": [
+ 1,
+ 1,
+ -1,
+ 1,
+ -1,
+ 1
+ ],
+ "modality": "vision",
+ "pathway": "spatial_multiplex",
+ "plasticity_rule": "s_stdp_plus_structural_growth_candidate",
+ "productive_activation_enabled": false,
+ "tick": 17
+ }
+ }
+ },
+ "research_data": [
+ "research/experiments/EXP-BATCH-20260914074039-90",
+ "research/experiments/EXP-BATCH-20260914074039-91",
+ "research/experiments/EXP-BATCH-20260914074039-92",
+ "research/experiments/EXP-BATCH-20260914074039-93",
+ "research/experiments/EXP-BATCH-20260914074039-94"
+ ],
+ "schema_version": 1,
+ "scientific_boundary": {
+ "aggregated_topology_is_not_dynamic_execution": true,
+ "automatic_evidence_promotion": false,
+ "canonical_core_mutation": false,
+ "experiment_only_adapters": true,
+ "human_review_required_for_evid": true,
+ "productive_activation_enabled": false,
+ "scope": "engineering_verification"
+ },
+ "stage": 4,
+ "status": "implemented_experimental",
+ "topology": {
+ "dynamic_scale_execution_verified": false,
+ "edges_materialized": false,
+ "lower_bound_satisfied": true,
+ "productive_activation_enabled": false,
+ "representation": "aggregated_topology_contract",
+ "stage4_min_neurons": 100000,
+ "stage4_min_synapses": 10000000,
+ "total_neuron_budget": 100000,
+ "total_synapse_budget": 10000000
+ },
+ "topology_edge_sample": [
+ {
+ "area_id": "stage4.audio.temporal",
+ "modality": "audio",
+ "plasticity_rule": "phase_weighted_t_stdp_candidate",
+ "source_local_index": 16649,
+ "target_local_index": 13915
+ },
+ {
+ "area_id": "stage4.vision.spatial",
+ "modality": "vision",
+ "plasticity_rule": "s_stdp_plus_structural_growth_candidate",
+ "source_local_index": 21825,
+ "target_local_index": 19724
+ },
+ {
+ "area_id": "stage4.digital.symbolic",
+ "modality": "digital",
+ "plasticity_rule": "population_meta_gating_candidate",
+ "source_local_index": 13891,
+ "target_local_index": 18241
+ },
+ {
+ "area_id": "stage4.audio.temporal",
+ "modality": "audio",
+ "plasticity_rule": "phase_weighted_t_stdp_candidate",
+ "source_local_index": 7214,
+ "target_local_index": 9972
+ },
+ {
+ "area_id": "stage4.vision.spatial",
+ "modality": "vision",
+ "plasticity_rule": "s_stdp_plus_structural_growth_candidate",
+ "source_local_index": 22172,
+ "target_local_index": 4104
+ },
+ {
+ "area_id": "stage4.digital.symbolic",
+ "modality": "digital",
+ "plasticity_rule": "population_meta_gating_candidate",
+ "source_local_index": 1480,
+ "target_local_index": 3500
+ },
+ {
+ "area_id": "stage4.audio.temporal",
+ "modality": "audio",
+ "plasticity_rule": "phase_weighted_t_stdp_candidate",
+ "source_local_index": 5981,
+ "target_local_index": 9376
+ },
+ {
+ "area_id": "stage4.vision.spatial",
+ "modality": "vision",
+ "plasticity_rule": "s_stdp_plus_structural_growth_candidate",
+ "source_local_index": 26698,
+ "target_local_index": 15924
+ },
+ {
+ "area_id": "stage4.digital.symbolic",
+ "modality": "digital",
+ "plasticity_rule": "population_meta_gating_candidate",
+ "source_local_index": 13736,
+ "target_local_index": 21774
+ },
+ {
+ "area_id": "stage4.audio.temporal",
+ "modality": "audio",
+ "plasticity_rule": "phase_weighted_t_stdp_candidate",
+ "source_local_index": 11971,
+ "target_local_index": 2842
+ },
+ {
+ "area_id": "stage4.vision.spatial",
+ "modality": "vision",
+ "plasticity_rule": "s_stdp_plus_structural_growth_candidate",
+ "source_local_index": 45358,
+ "target_local_index": 49308
+ },
+ {
+ "area_id": "stage4.digital.symbolic",
+ "modality": "digital",
+ "plasticity_rule": "population_meta_gating_candidate",
+ "source_local_index": 11561,
+ "target_local_index": 4349
+ }
+ ]
+ },
+ "limits": [
+ "The 100k-neuron/10M-synapse lower bound is an aggregated topology budget, not a materialized dynamic run.",
+ "Peripheral adapters remain experiment-only and productive activation remains locked.",
+ "Candidate modality-specific plasticity mathematics is exercised without mutating canonical SNN learning state.",
+ "The linked MSBA experiment series remains DATA until the project evidence workflow performs human review and any warranted EVID promotion."
+ ],
+ "proofs": {
+ "aggregated_stage4_lower_bound_satisfied": true,
+ "canonical_core_remains_unmutated": true,
+ "digital_reference_integrity_passes": true,
+ "latest_msba_data_checks_pass": true,
+ "productive_activation_remains_locked": true,
+ "scale_claim_remains_aggregated_not_dynamic": true,
+ "stage4_targeted_tests_pass": true,
+ "target_modalities_present": true
+ },
+ "recorded_msba_data_checks": {
+ "e01_matched_accuracy": true,
+ "e01_modality_cost_order_observed": true,
+ "e04_exact_integrity_observed": true,
+ "e05_adaptive_compensation_exceeds_fixed_in_recorded_data": true
+ },
+ "schema_version": 1,
+ "scientific_promotion": {
+ "accepted_evid_created": false,
+ "automatic_evidence_promotion": false,
+ "note": "The reference verifies the scoped Stage-4 engineering contract and links existing MSBA DATA. It does not establish a dynamically executed 100k-neuron/10M-edge multimodal SNN or scientific efficacy."
+ },
+ "scope": "engineering_verification",
+ "software_version": "0.6.0a3",
+ "stage": 4,
+ "status": "verified",
+ "suite": "specialized_neural_areas_reference",
+ "test_output_tail": "........................................................ [100%]\n56 passed in 6.21s",
+ "test_paths": [
+ "tests/test_stage4_specialized_neural_areas.py",
+ "tests/test_msba.py",
+ "tests/test_signal_processing_contracts.py",
+ "tests/test_neural_symbiosis.py",
+ "tests/test_gateway_runtime.py",
+ "tests/test_msba_experiment_runner.py",
+ "tests/test_dashboard_embodiment_routes.py"
+ ],
+ "tests_executed": true
+}
diff --git a/research/generated/verification/structural_e2e.json b/research/generated/verification/structural_e2e.json
new file mode 100644
index 0000000000000000000000000000000000000000..d27ecc88d5d053921f208d04b8d1367d06bf2078
--- /dev/null
+++ b/research/generated/verification/structural_e2e.json
@@ -0,0 +1,30 @@
+{
+ "schema_version": 1,
+ "suite": "structural_e2e",
+ "status": "verified",
+ "timestamp": "2026-09-20T11:11:13.938189",
+ "python_version": "3.13.15",
+ "test_run_head": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_commit": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_tree_digest": "c6e64d9d4043e250071170efc11a3a64086c63d25a94f7e79d38fc913d094cdc",
+ "scope": "structural_e2e",
+ "scope_digest": "4036baa871020d36f917b40e4e445209f6ae9f0577e5b082504fb22fe6601a28",
+ "test_command": "python -m pytest tests/test_structural_e2e.py -q",
+ "proofs": {
+ "test_proof_01_coordinator_instantiated": true,
+ "test_proof_02_plasticity_engine_instantiated": true,
+ "test_proof_03_bridge_identity": true,
+ "test_proof_04_real_signal_proposal": true,
+ "test_proof_05_proposal_no_mutation": true,
+ "test_proof_06_reject_no_mutation": true,
+ "test_proof_07_approve_exactly_one_mutation": true,
+ "test_proof_08_exactly_one_change_record": true,
+ "test_proof_09_undo_restores_topology": true,
+ "test_proof_10_restart_replay_identity": true,
+ "test_complete_canonical_e2e": true
+ },
+ "topology_digest_before": "2d92c14cc2809bc873f4ee3338ee744b03d59cb991cc5ddabaa9f3ba998124a7",
+ "topology_digest_after_mutation": "94851a89638fb557a95d04f60b569157a25366c5273539085fe4ec3f03990c64",
+ "topology_digest_after_undo": "2d92c14cc2809bc873f4ee3338ee744b03d59cb991cc5ddabaa9f3ba998124a7",
+ "journal_record_count": 1
+}
\ No newline at end of file
diff --git a/research/generated/verification/structural_live_loop.json b/research/generated/verification/structural_live_loop.json
new file mode 100644
index 0000000000000000000000000000000000000000..93ead9d3b0b160c79de21e481ee11ad89366b094
--- /dev/null
+++ b/research/generated/verification/structural_live_loop.json
@@ -0,0 +1,26 @@
+{
+ "schema_version": 1,
+ "suite": "structural_live_loop",
+ "status": "verified",
+ "timestamp": "2026-09-20T11:11:15.414988",
+ "python_version": "3.13.15",
+ "test_run_head": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_commit": "ed290195dc078f68ad252eb2accca1f82aaa1873",
+ "tested_tree_digest": "c6e64d9d4043e250071170efc11a3a64086c63d25a94f7e79d38fc913d094cdc",
+ "scope": "structural_live_loop",
+ "scope_digest": "ed705dc2b187a0a2deb86b1a0c4e13661e0d29f442760990b243675a450fec9a",
+ "test_command": "python -m pytest tests/test_structural_live_loop.py -q",
+ "proofs": {
+ "production_adapter_attached": true,
+ "real_signal_generated": true,
+ "policy_received_real_signal": true,
+ "proposal_published": true,
+ "proposal_non_mutating": true,
+ "reject_non_mutating": true,
+ "approve_single_mutation": true,
+ "journal_linked_to_proposal": true,
+ "runtime_continues_after_mutation": true,
+ "undo_restores_topology": true,
+ "journal_reopen_replay_identity": true
+ }
+}
\ No newline at end of file
diff --git a/research/learning/preparations/LP-20260917194217-R1.json b/research/learning/preparations/LP-20260917194217-R1.json
new file mode 100644
index 0000000000000000000000000000000000000000..4a0e5c5d58b9fcf9e98f9193fb84054cc3e89baf
--- /dev/null
+++ b/research/learning/preparations/LP-20260917194217-R1.json
@@ -0,0 +1,40 @@
+{
+ "ai_interaction_id": null,
+ "authority": "proposal_only",
+ "baseline_protocol": "raw_replay_full_budget",
+ "controls": [
+ "no_replay",
+ "random_prototype_10pct",
+ "learning_off"
+ ],
+ "digest": "a91fde4cd2ce378c869089dc83088747118240952b8593863b1b463750bbee8b",
+ "evaluation_protocol": "holdout_after_sequential_tasks",
+ "executed": false,
+ "exposure_protocol": "semantic_prototype_replay_10pct_budget",
+ "objective": {
+ "description": "Prüfen, ob semantische Prototypkonsolidierung bei 10 % des Raw-Replay-Speicherbudgets dieselbe Retention erreicht wie Raw-Replay bei vollem Budget.",
+ "evaluation_question": "Retains semantic prototype replay the same task performance when storage budget is reduced by factor 10?",
+ "objective_id": "OBJ-MEM-COMPRESSION-001",
+ "success_metric": "retention_ratio_at_1_10_storage ≥ 0.95 der Raw-Replay-Retention"
+ },
+ "origin": "human",
+ "plan_id": "LP-20260917194217-R1",
+ "rationale": "Source-provenance revision of LP-20260917194217. Exact CL-002 EVID and CL-003 DATA digests replace the placeholder source reference. This revision requires explicit human reapproval before any execution authorization.",
+ "sources": [
+ {
+ "digest": "c7b1124256fd8018839a8c5c29b30493b68d16bc223d0a3258bcbaca55b1752e",
+ "origin": "environment",
+ "partition": "train",
+ "source_id": "CL-002-EVID",
+ "trust": "VERIFIED"
+ },
+ {
+ "digest": "4e74021c0ef838371a3a01061ae1c2dcebb2031b54c15c6169e38c1972d06290",
+ "origin": "environment",
+ "partition": "train",
+ "source_id": "CL-003-DATA",
+ "trust": "VERIFIED"
+ }
+ ],
+ "stopping_rule": "fixed episodes"
+}
diff --git a/research/learning/preparations/LP-20260917194217-approved.json b/research/learning/preparations/LP-20260917194217-approved.json
new file mode 100644
index 0000000000000000000000000000000000000000..c14c93a78559e3b250f0cef26b914d389f2c6651
--- /dev/null
+++ b/research/learning/preparations/LP-20260917194217-approved.json
@@ -0,0 +1,40 @@
+{
+ "approval_note": "",
+ "approved_by": "Thomas Heisig",
+ "digest": "d24210d7e2f83994bc3365cde2010cb865bb6e1d23839e710c5b43bea7232b10",
+ "executed": false,
+ "proposal": {
+ "ai_interaction_id": null,
+ "authority": "proposal_only",
+ "baseline_protocol": "raw_replay_full_budget",
+ "controls": [
+ "no_replay",
+ "random_prototype_10pct",
+ "learning_off"
+ ],
+ "digest": "0d47182d05f2d65d44957a1242f770a3576518c9034b7b014cf8ae4f06333d11",
+ "evaluation_protocol": "holdout_after_sequential_tasks",
+ "executed": false,
+ "exposure_protocol": "semantic_prototype_replay_10pct_budget",
+ "objective": {
+ "description": "Pr\u00fcfen, ob semantische Prototypkonsolidierung bei 10 % des Raw-Replay-Speicherbudgets dieselbe Retention erreicht wie Raw-Replay bei vollem Budget.",
+ "evaluation_question": "Retains semantic prototype replay the same task performance when storage budget is reduced by factor 10?",
+ "objective_id": "OBJ-MEM-COMPRESSION-001",
+ "success_metric": "retention_ratio_at_1_10_storage \u2265 0.95 der Raw-Replay-Retention"
+ },
+ "origin": "human",
+ "plan_id": "LP-20260917194217",
+ "rationale": "",
+ "sources": [
+ {
+ "digest": "sha256-Digest der CL-002- und CL-003-DATA-Artefakte",
+ "origin": "environment",
+ "partition": "train",
+ "source_id": "SRC-CL-002-003",
+ "trust": "UNKNOWN"
+ }
+ ],
+ "stopping_rule": "fixed episodes"
+ },
+ "runtime_authority": "none"
+}
diff --git a/research/learning/preparations/LP-20260917194217.json b/research/learning/preparations/LP-20260917194217.json
new file mode 100644
index 0000000000000000000000000000000000000000..0157d3af512f31110ccba5a9417e0912955a8662
--- /dev/null
+++ b/research/learning/preparations/LP-20260917194217.json
@@ -0,0 +1,33 @@
+{
+ "ai_interaction_id": null,
+ "authority": "proposal_only",
+ "baseline_protocol": "raw_replay_full_budget",
+ "controls": [
+ "no_replay",
+ "random_prototype_10pct",
+ "learning_off"
+ ],
+ "digest": "0d47182d05f2d65d44957a1242f770a3576518c9034b7b014cf8ae4f06333d11",
+ "evaluation_protocol": "holdout_after_sequential_tasks",
+ "executed": false,
+ "exposure_protocol": "semantic_prototype_replay_10pct_budget",
+ "objective": {
+ "description": "Pr\u00fcfen, ob semantische Prototypkonsolidierung bei 10 % des Raw-Replay-Speicherbudgets dieselbe Retention erreicht wie Raw-Replay bei vollem Budget.",
+ "evaluation_question": "Retains semantic prototype replay the same task performance when storage budget is reduced by factor 10?",
+ "objective_id": "OBJ-MEM-COMPRESSION-001",
+ "success_metric": "retention_ratio_at_1_10_storage \u2265 0.95 der Raw-Replay-Retention"
+ },
+ "origin": "human",
+ "plan_id": "LP-20260917194217",
+ "rationale": "",
+ "sources": [
+ {
+ "digest": "sha256-Digest der CL-002- und CL-003-DATA-Artefakte",
+ "origin": "environment",
+ "partition": "train",
+ "source_id": "SRC-CL-002-003",
+ "trust": "UNKNOWN"
+ }
+ ],
+ "stopping_rule": "fixed episodes"
+}
diff --git a/research/literature/COGNITION_SOURCES.json b/research/literature/COGNITION_SOURCES.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b0531649c1113c9de46e8fe7b8d7227ff6e6926
--- /dev/null
+++ b/research/literature/COGNITION_SOURCES.json
@@ -0,0 +1,254 @@
+[
+ {
+ "source_id": "SRC-CNS-BUTLIN",
+ "authors_literal": "Patrick Butlin and others",
+ "year": 2026,
+ "title": "Identifying indicators of consciousness in AI systems",
+ "doi": "10.1016/j.tics.2025.10.011",
+ "url": "https://www.sciencedirect.com/science/article/pii/S1364661325002864",
+ "accessed": "2026-09-07",
+ "reading_scope": "Publisher metadata and relevant abstract/search-accessible passages; direct full-page retry blocked",
+ "argumentative_use": "Theory-dependent indicators; no universally sufficient consciousness test. Online 2025, journal issue June 2026; not a Brain-5D validation.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-COGITATE",
+ "authors_literal": "Cogitate Consortium and others",
+ "year": 2025,
+ "title": "Adversarial testing of global neuronal workspace and integrated information theories of consciousness",
+ "doi": "10.1038/s41586-025-08888-1",
+ "url": "https://www.nature.com/articles/s41586-025-08888-1",
+ "accessed": "2026-09-07",
+ "reading_scope": "HTML article, relevant theory/comparison passages",
+ "argumentative_use": "Contrasting predictions and adversarial testing; no consensus that one architecture is necessary for all possible artificial consciousness.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-GNWREPLY",
+ "authors_literal": "Lionel Naccache and Claire Sergent and Stanislas Dehaene and Xia-Jing Wang and Michele Farisco and Jean-Pierre Changeux",
+ "year": 2025,
+ "title": "GNW theoretical framework and the adversarial testing of global neuronal workspace and integrated information theories of consciousness",
+ "doi": "10.1093/nc/niaf037",
+ "url": "https://doi.org/10.1093/nc/niaf037",
+ "accessed": "2026-09-07",
+ "reading_scope": "Publisher HTML; author list, theoretical objections and competing-interest declaration checked",
+ "argumentative_use": "Records a substantive objection to interpretation of the adversarial test; not an independent replication.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-SETH",
+ "authors_literal": "Anil K. Seth",
+ "year": 2025,
+ "title": "Conscious artificial intelligence and biological naturalism",
+ "doi": "10.1017/S0140525X25000032",
+ "url": "https://doi.org/10.1017/S0140525X25000032",
+ "accessed": "2026-09-07",
+ "reading_scope": "Accepted-manuscript abstract/metadata",
+ "argumentative_use": "Biological-naturalist counterposition; its substrate assumptions are a theory position, not an established impossibility theorem.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-WELFARE",
+ "authors_literal": "Robert Long and others",
+ "year": 2024,
+ "title": "Taking AI Welfare Seriously",
+ "doi": "10.48550/arXiv.2411.00986",
+ "url": "https://arxiv.org/html/2411.00986v1",
+ "accessed": "2026-09-07",
+ "reading_scope": "HTML preprint, relevant precaution and uncertainty sections",
+ "argumentative_use": "Precaution under uncertainty; preprint, no proof of current AI sentience.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-ODDBALL",
+ "authors_literal": "Tristan A. Bekinschtein and others",
+ "year": 2009,
+ "title": "Neural signature of the conscious processing of auditory regularities",
+ "doi": "10.1073/pnas.0809667106",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/19164526/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and metadata",
+ "argumentative_use": "Local/global auditory regularities and attention/report conditions; no diagnostic transfer to AI.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-PCI",
+ "authors_literal": "Adenauer G. Casali and others",
+ "year": 2013,
+ "title": "A theoretically based index of consciousness independent of sensory processing and behavior",
+ "doi": "10.1126/scitranslmed.3006294",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/23946194/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and metadata",
+ "argumentative_use": "Clinical perturbational TMS-EEG method. No clinical cutoff transferred to SNN proxies.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-META",
+ "authors_literal": "Brian Maniscalco and Hakwan Lau",
+ "year": 2012,
+ "title": "A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings",
+ "doi": "10.1016/j.concog.2011.09.021",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/22071269/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and metadata",
+ "argumentative_use": "Meta-d-prime is distinct from type-2 AUROC or Brier score; validated fitter remains a separate implementation requirement.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-LFP",
+ "authors_literal": "Alberto Mazzoni and others",
+ "year": 2015,
+ "title": "Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models",
+ "doi": "10.1371/journal.pcbi.1004584",
+ "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004584",
+ "accessed": "2026-09-07",
+ "reading_scope": "HTML full text, relevant observation-model passages",
+ "argumentative_use": "LFP requires a justified observation model; scalp EEG requires an additional forward model and montage specification.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-MASK",
+ "authors_literal": "Michael A. Cohen and others",
+ "year": 2024,
+ "title": "Neural signatures of visual awareness independent of postperceptual processing",
+ "doi": "10.1093/cercor/bhae415",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/39535504/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and metadata",
+ "argumentative_use": "Report/no-report and masking dissociations; not a universal neural consciousness signature.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-NEUROGYM",
+ "authors_literal": "NeuroGym contributors",
+ "year": 2026,
+ "title": "NeuroGym environment API documentation",
+ "doi": null,
+ "url": "https://neurogym.github.io/neurogym/latest/api/envs/",
+ "accessed": "2026-09-07",
+ "reading_scope": "Official environment documentation and task listing",
+ "argumentative_use": "Reproducible task-implementation reference, not a certified consciousness battery. Future adapter must pin a concrete package commit/version.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-TURING",
+ "authors_literal": "Alan M. Turing",
+ "year": 1950,
+ "title": "Computing Machinery and Intelligence",
+ "doi": "10.1093/mind/LIX.236.433",
+ "url": "https://academic.oup.com/mind/article/LIX/236/433/986238",
+ "accessed": "2026-09-07",
+ "reading_scope": "Publisher HTML/metadata and imitation-game framing",
+ "argumentative_use": "Behavioral imitation game, not a test proving phenomenal experience.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-ARC",
+ "authors_literal": "Francois Chollet",
+ "year": 2019,
+ "title": "On the Measure of Intelligence",
+ "doi": "10.48550/arXiv.1911.01547",
+ "url": "https://arxiv.org/abs/1911.01547",
+ "accessed": "2026-09-07",
+ "reading_scope": "Preprint abstract and metadata",
+ "argumentative_use": "Skill-acquisition/generalization framework and ARC; this supplement does not claim current ARC benchmark performance.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-LSNN",
+ "authors_literal": "Guillaume Bellec and others",
+ "year": 2018,
+ "title": "Long short-term memory and learning-to-learn in networks of spiking neurons",
+ "doi": "10.48550/arXiv.1803.09574",
+ "url": "https://arxiv.org/abs/1803.09574",
+ "accessed": "2026-09-07",
+ "reading_scope": "Preprint abstract and metadata",
+ "argumentative_use": "Prior spiking-network work with adaptation and longer memory timescales; slow/fast loops alone are not new.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-NEURONS",
+ "authors_literal": "Frederico A. C. Azevedo and others",
+ "year": 2009,
+ "title": "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain",
+ "doi": "10.1002/cne.21974",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/19226510/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and metadata",
+ "argumentative_use": "Human neuron-number estimate from a biological sample, not a minimum-size criterion for artificial experience.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-EQUIV",
+ "authors_literal": "Daniel Lakens",
+ "year": 2017,
+ "title": "Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses",
+ "doi": "10.1177/1948550617697177",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/28736600/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and method summary",
+ "argumentative_use": "Prespecified equivalence bounds; nonsignificant difference is not demonstrated equivalence.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-BLINK",
+ "authors_literal": "Jane E. Raymond and Kimron L. Shapiro and Karen M. Arnell",
+ "year": 1992,
+ "title": "Temporary suppression of visual processing in an RSVP task: an attentional blink?",
+ "doi": "10.1037//0096-1523.18.3.849",
+ "url": "https://pubmed.ncbi.nlm.nih.gov/1500880/",
+ "accessed": "2026-09-07",
+ "reading_scope": "PubMed abstract and metadata",
+ "argumentative_use": "Dual-target temporal attention paradigm; an artificial blink can be an engineered bottleneck rather than evidence of experience.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-STOP",
+ "authors_literal": "Frederick Verbruggen and others",
+ "year": 2019,
+ "title": "A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task",
+ "doi": "10.7554/eLife.46323",
+ "url": "https://elifesciences.org/articles/46323",
+ "accessed": "2026-09-07",
+ "reading_scope": "HTML recommendations, race-model assumptions and analysis passages",
+ "argumentative_use": "SSRT validity depends on task/estimator assumptions; no substitute for a hardware emergency-stop test.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-LAW211",
+ "authors_literal": "Bundesrepublik Deutschland",
+ "year": 2026,
+ "title": "Strafgesetzbuch Paragraph 211",
+ "doi": null,
+ "url": "https://www.gesetze-im-internet.de/stgb/__211.html",
+ "accessed": "2026-09-07",
+ "reading_scope": "Official indexed norm text; direct retry timed out",
+ "argumentative_use": "Human victim in the statutory murder definition; retrieved legal baseline, not a ruling on hypothetical future AI rights.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-LAW212",
+ "authors_literal": "Bundesrepublik Deutschland",
+ "year": 2026,
+ "title": "Strafgesetzbuch Paragraph 212",
+ "doi": null,
+ "url": "https://www.gesetze-im-internet.de/stgb/__212.html",
+ "accessed": "2026-09-07",
+ "reading_scope": "Official indexed norm text; direct retry timed out",
+ "argumentative_use": "Human victim in homicide provision; no blanket conclusion about other possible unlawful conduct.",
+ "brain5d_data_validated": false
+ },
+ {
+ "source_id": "SRC-CNS-LAW103",
+ "authors_literal": "Bundesrepublik Deutschland",
+ "year": 2026,
+ "title": "Grundgesetz Artikel 103 Absatz 2",
+ "doi": null,
+ "url": "https://www.gesetze-im-internet.de/gg/art_103.html",
+ "accessed": "2026-09-07",
+ "reading_scope": "Official HTML norm text",
+ "argumentative_use": "Legality of criminal punishment; law and precautionary ethics are separate questions.",
+ "brain5d_data_validated": false
+ }
+]
diff --git a/research/literature/COGNITION_SOURCES.md b/research/literature/COGNITION_SOURCES.md
new file mode 100644
index 0000000000000000000000000000000000000000..0fd4034132e1ffb082fc471d63792905ce66cea4
--- /dev/null
+++ b/research/literature/COGNITION_SOURCES.md
@@ -0,0 +1,236 @@
+# Kognitionsprogramm: Quellen und tatsaechliche Nutzung
+
+Stichtag: 7. September 2026. Gezielte Ergaenzungsrecherche, keine vollstaendige systematische Datenbankreview. Die Auswahl folgt den Kritikthemen. Treffer-/Screeningzahlen werden nicht erfunden. Suchwege: Websuche, direkte Verlags-/Institutionen-HTML-Aufrufe, PubMed-Abstracts und offizielle Aufgabendokumentation; Repositorylekture separat. Keine native Vollsuche in Scopus, Web of Science oder PhilPapers.
+
+Zugang, gelesener Umfang und argumentative Verwendung stehen pro Quelle. Metadaten werden nicht als Volltextlekture ausgegeben. Keine der Quellen validiert Brain-5D; kein klinischer Datensatz wurde hier neu ausgewertet. Amtliche Rechtsabrufe hatten bei Wiederholung teilweise Timeouts; dies ist ausgewiesen. Die Bibliografie ist ein konservativer Austausch-Nachtrag, kein vollstaendiger bibliometrischer Audit.
+
+
+## SRC-CNS-BUTLIN
+
+Patrick Butlin and others (2026). *Identifying indicators of consciousness in AI systems*.
+
+https://www.sciencedirect.com/science/article/pii/S1364661325002864
+
+**Gelesener Umfang:** Publisher metadata and relevant abstract/search-accessible passages; direct full-page retry blocked.
+
+**Verwendung und Grenze:** Theory-dependent indicators; no universally sufficient consciousness test. Online 2025, journal issue June 2026; not a Brain-5D validation.
+
+
+## SRC-CNS-COGITATE
+
+Cogitate Consortium and others (2025). *Adversarial testing of global neuronal workspace and integrated information theories of consciousness*.
+
+https://www.nature.com/articles/s41586-025-08888-1
+
+**Gelesener Umfang:** HTML article, relevant theory/comparison passages.
+
+**Verwendung und Grenze:** Contrasting predictions and adversarial testing; no consensus that one architecture is necessary for all possible artificial consciousness.
+
+
+## SRC-CNS-GNWREPLY
+
+Lionel Naccache and Claire Sergent and Stanislas Dehaene and Xia-Jing Wang and Michele Farisco and Jean-Pierre Changeux (2025). *GNW theoretical framework and the adversarial testing of global neuronal workspace and integrated information theories of consciousness*.
+
+https://doi.org/10.1093/nc/niaf037
+
+**Gelesener Umfang:** Publisher HTML; author list, theoretical objections and competing-interest declaration checked.
+
+**Verwendung und Grenze:** Records a substantive objection to interpretation of the adversarial test; not an independent replication.
+
+
+## SRC-CNS-SETH
+
+Anil K. Seth (2025). *Conscious artificial intelligence and biological naturalism*.
+
+https://doi.org/10.1017/S0140525X25000032
+
+**Gelesener Umfang:** Accepted-manuscript abstract/metadata.
+
+**Verwendung und Grenze:** Biological-naturalist counterposition; its substrate assumptions are a theory position, not an established impossibility theorem.
+
+
+## SRC-CNS-WELFARE
+
+Robert Long and others (2024). *Taking AI Welfare Seriously*.
+
+https://arxiv.org/html/2411.00986v1
+
+**Gelesener Umfang:** HTML preprint, relevant precaution and uncertainty sections.
+
+**Verwendung und Grenze:** Precaution under uncertainty; preprint, no proof of current AI sentience.
+
+
+## SRC-CNS-ODDBALL
+
+Tristan A. Bekinschtein and others (2009). *Neural signature of the conscious processing of auditory regularities*.
+
+https://pubmed.ncbi.nlm.nih.gov/19164526/
+
+**Gelesener Umfang:** PubMed abstract and metadata.
+
+**Verwendung und Grenze:** Local/global auditory regularities and attention/report conditions; no diagnostic transfer to AI.
+
+
+## SRC-CNS-PCI
+
+Adenauer G. Casali and others (2013). *A theoretically based index of consciousness independent of sensory processing and behavior*.
+
+https://pubmed.ncbi.nlm.nih.gov/23946194/
+
+**Gelesener Umfang:** PubMed abstract and metadata.
+
+**Verwendung und Grenze:** Clinical perturbational TMS-EEG method. No clinical cutoff transferred to SNN proxies.
+
+
+## SRC-CNS-META
+
+Brian Maniscalco and Hakwan Lau (2012). *A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings*.
+
+https://pubmed.ncbi.nlm.nih.gov/22071269/
+
+**Gelesener Umfang:** PubMed abstract and metadata.
+
+**Verwendung und Grenze:** Meta-d-prime is distinct from type-2 AUROC or Brier score; validated fitter remains a separate implementation requirement.
+
+
+## SRC-CNS-LFP
+
+Alberto Mazzoni and others (2015). *Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models*.
+
+https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004584
+
+**Gelesener Umfang:** HTML full text, relevant observation-model passages.
+
+**Verwendung und Grenze:** LFP requires a justified observation model; scalp EEG requires an additional forward model and montage specification.
+
+
+## SRC-CNS-MASK
+
+Michael A. Cohen and others (2024). *Neural signatures of visual awareness independent of postperceptual processing*.
+
+https://pubmed.ncbi.nlm.nih.gov/39535504/
+
+**Gelesener Umfang:** PubMed abstract and metadata.
+
+**Verwendung und Grenze:** Report/no-report and masking dissociations; not a universal neural consciousness signature.
+
+
+## SRC-CNS-NEUROGYM
+
+NeuroGym contributors (2026). *NeuroGym environment API documentation*.
+
+https://neurogym.github.io/neurogym/latest/api/envs/
+
+**Gelesener Umfang:** Official environment documentation and task listing.
+
+**Verwendung und Grenze:** Reproducible task-implementation reference, not a certified consciousness battery. Future adapter must pin a concrete package commit/version.
+
+
+## SRC-CNS-TURING
+
+Alan M. Turing (1950). *Computing Machinery and Intelligence*.
+
+https://academic.oup.com/mind/article/LIX/236/433/986238
+
+**Gelesener Umfang:** Publisher HTML/metadata and imitation-game framing.
+
+**Verwendung und Grenze:** Behavioral imitation game, not a test proving phenomenal experience.
+
+
+## SRC-CNS-ARC
+
+Francois Chollet (2019). *On the Measure of Intelligence*.
+
+https://arxiv.org/abs/1911.01547
+
+**Gelesener Umfang:** Preprint abstract and metadata.
+
+**Verwendung und Grenze:** Skill-acquisition/generalization framework and ARC; this supplement does not claim current ARC benchmark performance.
+
+
+## SRC-CNS-LSNN
+
+Guillaume Bellec and others (2018). *Long short-term memory and learning-to-learn in networks of spiking neurons*.
+
+https://arxiv.org/abs/1803.09574
+
+**Gelesener Umfang:** Preprint abstract and metadata.
+
+**Verwendung und Grenze:** Prior spiking-network work with adaptation and longer memory timescales; slow/fast loops alone are not new.
+
+
+## SRC-CNS-NEURONS
+
+Frederico A. C. Azevedo and others (2009). *Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain*.
+
+https://pubmed.ncbi.nlm.nih.gov/19226510/
+
+**Gelesener Umfang:** PubMed abstract and metadata.
+
+**Verwendung und Grenze:** Human neuron-number estimate from a biological sample, not a minimum-size criterion for artificial experience.
+
+
+## SRC-CNS-EQUIV
+
+Daniel Lakens (2017). *Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses*.
+
+https://pubmed.ncbi.nlm.nih.gov/28736600/
+
+**Gelesener Umfang:** PubMed abstract and method summary.
+
+**Verwendung und Grenze:** Prespecified equivalence bounds; nonsignificant difference is not demonstrated equivalence.
+
+
+## SRC-CNS-BLINK
+
+Jane E. Raymond and Kimron L. Shapiro and Karen M. Arnell (1992). *Temporary suppression of visual processing in an RSVP task: an attentional blink?*.
+
+https://pubmed.ncbi.nlm.nih.gov/1500880/
+
+**Gelesener Umfang:** PubMed abstract and metadata.
+
+**Verwendung und Grenze:** Dual-target temporal attention paradigm; an artificial blink can be an engineered bottleneck rather than evidence of experience.
+
+
+## SRC-CNS-STOP
+
+Frederick Verbruggen and others (2019). *A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task*.
+
+https://elifesciences.org/articles/46323
+
+**Gelesener Umfang:** HTML recommendations, race-model assumptions and analysis passages.
+
+**Verwendung und Grenze:** SSRT validity depends on task/estimator assumptions; no substitute for a hardware emergency-stop test.
+
+
+## SRC-CNS-LAW211
+
+Bundesrepublik Deutschland (2026). *Strafgesetzbuch Paragraph 211*.
+
+https://www.gesetze-im-internet.de/stgb/__211.html
+
+**Gelesener Umfang:** Official indexed norm text; direct retry timed out.
+
+**Verwendung und Grenze:** Human victim in the statutory murder definition; retrieved legal baseline, not a ruling on hypothetical future AI rights.
+
+
+## SRC-CNS-LAW212
+
+Bundesrepublik Deutschland (2026). *Strafgesetzbuch Paragraph 212*.
+
+https://www.gesetze-im-internet.de/stgb/__212.html
+
+**Gelesener Umfang:** Official indexed norm text; direct retry timed out.
+
+**Verwendung und Grenze:** Human victim in homicide provision; no blanket conclusion about other possible unlawful conduct.
+
+
+## SRC-CNS-LAW103
+
+Bundesrepublik Deutschland (2026). *Grundgesetz Artikel 103 Absatz 2*.
+
+https://www.gesetze-im-internet.de/gg/art_103.html
+
+**Gelesener Umfang:** Official HTML norm text.
+
+**Verwendung und Grenze:** Legality of criminal punishment; law and precautionary ethics are separate questions.
diff --git a/research/literature/brain_models.bib b/research/literature/brain_models.bib
new file mode 100644
index 0000000000000000000000000000000000000000..7828b702a92b26d4d995b402a0a641f477190ca6
--- /dev/null
+++ b/research/literature/brain_models.bib
@@ -0,0 +1,51 @@
+@article{Izhikevich2003,
+ author = {Eugene M. Izhikevich},
+ title = {Simple model of spiking neurons},
+ journal = {IEEE Transactions on Neural Networks},
+ year = {2003},
+ volume = {14},
+ number = {6},
+ pages = {1569--1572},
+ doi = {10.1109/TNN.2003.820440}
+}
+
+@book{Gerstner2014,
+ author = {Wulfram Gerstner and Werner M. Kistler and Richard Naud and Liam Paninski},
+ title = {Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition},
+ publisher = {Cambridge University Press},
+ year = {2014},
+ isbn = {978-1107635197}
+}
+
+@article{Maass1997,
+ author = {Wolfgang Maass},
+ title = {Networks of spiking neurons: The third generation of neural network models},
+ journal = {Neural Networks},
+ year = {1997},
+ volume = {10},
+ number = {9},
+ pages = {1659--1671},
+ doi = {10.1016/S0893-6080(97)00011-7}
+}
+
+@article{Markram2015,
+ author = {Henry Markram and others},
+ title = {Reconstruction and Simulation of Neocortical Microcircuitry},
+ journal = {Cell},
+ year = {2015},
+ volume = {163},
+ number = {2},
+ pages = {456--492},
+ doi = {10.1016/j.cell.2015.09.029}
+}
+
+@article{Hodgkin1952,
+ author = {Alan L. Hodgkin and Andrew F. Huxley},
+ title = {A quantitative description of membrane current and its application to conduction and excitation in nerve},
+ journal = {Journal of Physiology},
+ year = {1952},
+ volume = {117},
+ number = {4},
+ pages = {500--544},
+ doi = {10.1113/jphysiol.1952.sp004764}
+}
diff --git a/research/literature/cognition_sources.bib b/research/literature/cognition_sources.bib
new file mode 100644
index 0000000000000000000000000000000000000000..ce2f083f7e345cb6642f0fb5df059d526164e14c
--- /dev/null
+++ b/research/literature/cognition_sources.bib
@@ -0,0 +1,184 @@
+@misc{SRC-CNS-BUTLIN,
+ title = {Identifying indicators of consciousness in AI systems},
+ author = {Patrick Butlin and others},
+ year = {2026},
+ url = {https://www.sciencedirect.com/science/article/pii/S1364661325002864},
+ note = {Publisher metadata and relevant abstract/search-accessible passages; direct full-page retry blocked. Theory-dependent indicators; no universally sufficient consciousness test. Online 2025, journal issue June 2026; not a Brain-5D validation.},
+ doi = {10.1016/j.tics.2025.10.011}
+}
+
+@misc{SRC-CNS-COGITATE,
+ title = {Adversarial testing of global neuronal workspace and integrated information theories of consciousness},
+ author = {Cogitate Consortium and others},
+ year = {2025},
+ url = {https://www.nature.com/articles/s41586-025-08888-1},
+ note = {HTML article, relevant theory/comparison passages. Contrasting predictions and adversarial testing; no consensus that one architecture is necessary for all possible artificial consciousness.},
+ doi = {10.1038/s41586-025-08888-1}
+}
+
+@misc{SRC-CNS-GNWREPLY,
+ title = {GNW theoretical framework and the adversarial testing of global neuronal workspace and integrated information theories of consciousness},
+ author = {Lionel Naccache and Claire Sergent and Stanislas Dehaene and Xia-Jing Wang and Michele Farisco and Jean-Pierre Changeux},
+ year = {2025},
+ url = {https://doi.org/10.1093/nc/niaf037},
+ note = {Publisher HTML; author list, theoretical objections and competing-interest declaration checked. Records a substantive objection to interpretation of the adversarial test; not an independent replication.},
+ doi = {10.1093/nc/niaf037}
+}
+
+@misc{SRC-CNS-SETH,
+ title = {Conscious artificial intelligence and biological naturalism},
+ author = {Anil K. Seth},
+ year = {2025},
+ url = {https://doi.org/10.1017/S0140525X25000032},
+ note = {Accepted-manuscript abstract/metadata. Biological-naturalist counterposition; its substrate assumptions are a theory position, not an established impossibility theorem.},
+ doi = {10.1017/S0140525X25000032}
+}
+
+@misc{SRC-CNS-WELFARE,
+ title = {Taking AI Welfare Seriously},
+ author = {Robert Long and others},
+ year = {2024},
+ url = {https://arxiv.org/html/2411.00986v1},
+ note = {HTML preprint, relevant precaution and uncertainty sections. Precaution under uncertainty; preprint, no proof of current AI sentience.},
+ doi = {10.48550/arXiv.2411.00986}
+}
+
+@misc{SRC-CNS-ODDBALL,
+ title = {Neural signature of the conscious processing of auditory regularities},
+ author = {Tristan A. Bekinschtein and others},
+ year = {2009},
+ url = {https://pubmed.ncbi.nlm.nih.gov/19164526/},
+ note = {PubMed abstract and metadata. Local/global auditory regularities and attention/report conditions; no diagnostic transfer to AI.},
+ doi = {10.1073/pnas.0809667106}
+}
+
+@misc{SRC-CNS-PCI,
+ title = {A theoretically based index of consciousness independent of sensory processing and behavior},
+ author = {Adenauer G. Casali and others},
+ year = {2013},
+ url = {https://pubmed.ncbi.nlm.nih.gov/23946194/},
+ note = {PubMed abstract and metadata. Clinical perturbational TMS-EEG method. No clinical cutoff transferred to SNN proxies.},
+ doi = {10.1126/scitranslmed.3006294}
+}
+
+@misc{SRC-CNS-META,
+ title = {A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings},
+ author = {Brian Maniscalco and Hakwan Lau},
+ year = {2012},
+ url = {https://pubmed.ncbi.nlm.nih.gov/22071269/},
+ note = {PubMed abstract and metadata. Meta-d-prime is distinct from type-2 AUROC or Brier score; validated fitter remains a separate implementation requirement.},
+ doi = {10.1016/j.concog.2011.09.021}
+}
+
+@misc{SRC-CNS-LFP,
+ title = {Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models},
+ author = {Alberto Mazzoni and others},
+ year = {2015},
+ url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004584},
+ note = {HTML full text, relevant observation-model passages. LFP requires a justified observation model; scalp EEG requires an additional forward model and montage specification.},
+ doi = {10.1371/journal.pcbi.1004584}
+}
+
+@misc{SRC-CNS-MASK,
+ title = {Neural signatures of visual awareness independent of postperceptual processing},
+ author = {Michael A. Cohen and others},
+ year = {2024},
+ url = {https://pubmed.ncbi.nlm.nih.gov/39535504/},
+ note = {PubMed abstract and metadata. Report/no-report and masking dissociations; not a universal neural consciousness signature.},
+ doi = {10.1093/cercor/bhae415}
+}
+
+@misc{SRC-CNS-NEUROGYM,
+ title = {NeuroGym environment API documentation},
+ author = {NeuroGym contributors},
+ year = {2026},
+ url = {https://neurogym.github.io/neurogym/latest/api/envs/},
+ note = {Official environment documentation and task listing. Reproducible task-implementation reference, not a certified consciousness battery. Future adapter must pin a concrete package commit/version.}
+}
+
+@misc{SRC-CNS-TURING,
+ title = {Computing Machinery and Intelligence},
+ author = {Alan M. Turing},
+ year = {1950},
+ url = {https://academic.oup.com/mind/article/LIX/236/433/986238},
+ note = {Publisher HTML/metadata and imitation-game framing. Behavioral imitation game, not a test proving phenomenal experience.},
+ doi = {10.1093/mind/LIX.236.433}
+}
+
+@misc{SRC-CNS-ARC,
+ title = {On the Measure of Intelligence},
+ author = {Francois Chollet},
+ year = {2019},
+ url = {https://arxiv.org/abs/1911.01547},
+ note = {Preprint abstract and metadata. Skill-acquisition/generalization framework and ARC; this supplement does not claim current ARC benchmark performance.},
+ doi = {10.48550/arXiv.1911.01547}
+}
+
+@misc{SRC-CNS-LSNN,
+ title = {Long short-term memory and learning-to-learn in networks of spiking neurons},
+ author = {Guillaume Bellec and others},
+ year = {2018},
+ url = {https://arxiv.org/abs/1803.09574},
+ note = {Preprint abstract and metadata. Prior spiking-network work with adaptation and longer memory timescales; slow/fast loops alone are not new.},
+ doi = {10.48550/arXiv.1803.09574}
+}
+
+@misc{SRC-CNS-NEURONS,
+ title = {Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain},
+ author = {Frederico A. C. Azevedo and others},
+ year = {2009},
+ url = {https://pubmed.ncbi.nlm.nih.gov/19226510/},
+ note = {PubMed abstract and metadata. Human neuron-number estimate from a biological sample, not a minimum-size criterion for artificial experience.},
+ doi = {10.1002/cne.21974}
+}
+
+@misc{SRC-CNS-EQUIV,
+ title = {Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses},
+ author = {Daniel Lakens},
+ year = {2017},
+ url = {https://pubmed.ncbi.nlm.nih.gov/28736600/},
+ note = {PubMed abstract and method summary. Prespecified equivalence bounds; nonsignificant difference is not demonstrated equivalence.},
+ doi = {10.1177/1948550617697177}
+}
+
+@misc{SRC-CNS-BLINK,
+ title = {Temporary suppression of visual processing in an RSVP task: an attentional blink?},
+ author = {Jane E. Raymond and Kimron L. Shapiro and Karen M. Arnell},
+ year = {1992},
+ url = {https://pubmed.ncbi.nlm.nih.gov/1500880/},
+ note = {PubMed abstract and metadata. Dual-target temporal attention paradigm; an artificial blink can be an engineered bottleneck rather than evidence of experience.},
+ doi = {10.1037//0096-1523.18.3.849}
+}
+
+@misc{SRC-CNS-STOP,
+ title = {A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task},
+ author = {Frederick Verbruggen and others},
+ year = {2019},
+ url = {https://elifesciences.org/articles/46323},
+ note = {HTML recommendations, race-model assumptions and analysis passages. SSRT validity depends on task/estimator assumptions; no substitute for a hardware emergency-stop test.},
+ doi = {10.7554/eLife.46323}
+}
+
+@misc{SRC-CNS-LAW211,
+ title = {Strafgesetzbuch Paragraph 211},
+ author = {Bundesrepublik Deutschland},
+ year = {2026},
+ url = {https://www.gesetze-im-internet.de/stgb/__211.html},
+ note = {Official indexed norm text; direct retry timed out. Human victim in the statutory murder definition; retrieved legal baseline, not a ruling on hypothetical future AI rights.}
+}
+
+@misc{SRC-CNS-LAW212,
+ title = {Strafgesetzbuch Paragraph 212},
+ author = {Bundesrepublik Deutschland},
+ year = {2026},
+ url = {https://www.gesetze-im-internet.de/stgb/__212.html},
+ note = {Official indexed norm text; direct retry timed out. Human victim in homicide provision; no blanket conclusion about other possible unlawful conduct.}
+}
+
+@misc{SRC-CNS-LAW103,
+ title = {Grundgesetz Artikel 103 Absatz 2},
+ author = {Bundesrepublik Deutschland},
+ year = {2026},
+ url = {https://www.gesetze-im-internet.de/gg/art_103.html},
+ note = {Official HTML norm text. Legality of criminal punishment; law and precautionary ethics are separate questions.}
+}
diff --git a/research/literature/connectome_claims.json b/research/literature/connectome_claims.json
new file mode 100644
index 0000000000000000000000000000000000000000..91b55daeef18ae59f71521d4464807ce9862306f
--- /dev/null
+++ b/research/literature/connectome_claims.json
@@ -0,0 +1,74 @@
+{
+ "schema_version": "1.0",
+ "verified_on": "2026-09-09",
+ "ai_generated": true,
+ "claims": [
+ {
+ "id": "CONN-C01",
+ "source_id": "SRC-CONN-BERG-2026",
+ "claim": "Cell male CNS publication is dated 2026-09-03; summary reports 166700 neurons.",
+ "scope": "anatomical reconstruction",
+ "not_implied": [
+ "functional whole-brain equivalence",
+ "human neuroanatomical mapping",
+ "MHRN superiority"
+ ]
+ },
+ {
+ "id": "CONN-C02",
+ "source_id": "SRC-CONN-MALECNS-RELEASES",
+ "claim": "MaleCNS v0.9 release 2025-10-03; v1.0 release 2026-06-08.",
+ "scope": "release history",
+ "not_implied": [
+ "first dataset availability on 2026-09-03"
+ ]
+ },
+ {
+ "id": "CONN-C03",
+ "source_id": "SRC-CONN-EON-2026",
+ "claim": "Technical report dated 2026-03-10 discloses pre-existing controllers, manual mappings and 15 ms coupling.",
+ "scope": "company demonstration",
+ "not_implied": [
+ "independent replication",
+ "plasticity learned all behavior",
+ "male CNS dataset was the substrate"
+ ]
+ },
+ {
+ "id": "CONN-C04",
+ "source_id": "SRC-CONN-SHIU-2024",
+ "claim": "LIF model predicts selected feeding and grooming circuit responses with biological tests.",
+ "scope": "tested sensorimotor transformations",
+ "not_implied": [
+ "exact reproduction by an Izhikevich variant",
+ "all fly behavior"
+ ]
+ }
+ ],
+ "excluded_or_unresolved": [
+ {
+ "claim": "FlyWire / Dorkenwald et al. 2026 male CNS",
+ "reason": "Misattribution; use Dorkenwald 2024 female brain and Berg 2026 male CNS."
+ },
+ {
+ "claim": "166691 is an invariant count across datasets",
+ "reason": "Use version/filter-specific measured counts; published 2026 Cell summary is 166700."
+ },
+ {
+ "claim": "125 million synapses are the adapter dataset size",
+ "reason": "No biological dataset imported; count must be derived from actual pinned data and counting convention."
+ },
+ {
+ "claim": "UMAP/t-SNE maps fly neurons to human anatomy",
+ "reason": "Layout is not anatomical homology."
+ },
+ {
+ "claim": "A connectomic graph model guarantees sample-efficiency advantage for MHRN",
+ "reason": "No independently verified matched MHRN comparison in this integration."
+ },
+ {
+ "claim": "Fluorescent avatar is EEG/fMRI or a consciousness measure",
+ "reason": "Telemetry is not a biological imaging modality or a subjective-experience measurement."
+ }
+ ]
+}
diff --git a/research/literature/philosophy_ai.bib b/research/literature/philosophy_ai.bib
new file mode 100644
index 0000000000000000000000000000000000000000..4f79e9e7a853cc0289e31a77cdb27ee2324d448c
--- /dev/null
+++ b/research/literature/philosophy_ai.bib
@@ -0,0 +1,20 @@
+@article{Turing1950,
+ author = {Alan M. Turing},
+ title = {Computing Machinery and Intelligence},
+ journal = {Mind},
+ year = {1950},
+ volume = {59},
+ number = {236},
+ pages = {433--460}
+}
+
+@article{Homeostasis2008,
+ author = {Gina G. Turrigiano},
+ title = {The Self-Tuning Neuron: Synaptic Scaling of Excitatory Synapses},
+ journal = {Cell},
+ year = {2008},
+ volume = {135},
+ number = {3},
+ pages = {422--435},
+ doi = {10.1016/j.cell.2008.10.008}
+}
diff --git a/research/literature/plasticity.bib b/research/literature/plasticity.bib
new file mode 100644
index 0000000000000000000000000000000000000000..223cc6d4f2caf25d58cd6eca29a6f51371f0d820
--- /dev/null
+++ b/research/literature/plasticity.bib
@@ -0,0 +1,39 @@
+@article{SongAbbott2000,
+ author = {Sen Song and Kenneth D. Miller and Larry F. Abbott},
+ title = {Competitive Hebbian learning through spike-timing-dependent synaptic plasticity},
+ journal = {Nature Neuroscience},
+ year = {2000},
+ volume = {3},
+ number = {9},
+ pages = {919--926},
+ doi = {10.1038/78829}
+}
+
+@article{BiPoo1998,
+ author = {Guo-Qiang Bi and Mu-ming Poo},
+ title = {Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type},
+ journal = {Journal of Neuroscience},
+ year = {1998},
+ volume = {18},
+ number = {24},
+ pages = {10464--10472},
+ doi = {10.1523/JNEUROSCI.18-24-10464.1998}
+}
+
+@article{Markram1997,
+ author = {Henry Markram and Joachim Lübke and Michael Frotscher and Bert Sakmann},
+ title = {Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs},
+ journal = {Science},
+ year = {1997},
+ volume = {275},
+ number = {5297},
+ pages = {213--215},
+ doi = {10.1126/science.275.5297.213}
+}
+
+@book{Hebb1949,
+ author = {Donald O. Hebb},
+ title = {The Organization of Behavior},
+ publisher = {Wiley \& Sons},
+ year = {1949}
+}
diff --git a/research/literature/stage6_memory_world_model.bib b/research/literature/stage6_memory_world_model.bib
new file mode 100644
index 0000000000000000000000000000000000000000..2da344b86b63056fb782e414344ac00ecd2546c4
--- /dev/null
+++ b/research/literature/stage6_memory_world_model.bib
@@ -0,0 +1,95 @@
+@article{mcclelland1995complementary,
+ author = {McClelland, James L. and McNaughton, Bruce L. and O'Reilly, Randall C.},
+ title = {Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights from the Successes and Failures of Connectionist Models of Learning and Memory},
+ journal = {Psychological Review},
+ year = {1995},
+ volume = {102},
+ number = {3},
+ pages = {419--457},
+ doi = {10.1037/0033-295X.102.3.419}
+}
+
+@article{chrysanthidis2022semantization,
+ author = {Chrysanthidis, Nikolaos and Fiebig, Florian and Lansner, Anders and Herman, Pawel},
+ title = {Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model},
+ journal = {eNeuro},
+ year = {2022},
+ volume = {9},
+ number = {4},
+ pages = {ENEURO.0062-22.2022},
+ doi = {10.1523/ENEURO.0062-22.2022}
+}
+
+@article{golden2022sleep,
+ author = {Golden, Ryan and Delanois, Jean Erik and Sanda, Pavel and Bazhenov, Maxim},
+ title = {Sleep Prevents Catastrophic Forgetting in Spiking Neural Networks by Forming a Joint Synaptic Weight Representation},
+ journal = {PLOS Computational Biology},
+ year = {2022},
+ volume = {18},
+ number = {11},
+ pages = {e1010628},
+ doi = {10.1371/journal.pcbi.1010628}
+}
+
+@article{mikulasch2023error,
+ author = {Mikulasch, Fabian A. and Rudelt, Lucas and Wibral, Michael and Priesemann, Viola},
+ title = {Where Is the Error? Hierarchical Predictive Coding through Dendritic Error Computation},
+ journal = {Trends in Neurosciences},
+ year = {2023},
+ volume = {46},
+ number = {1},
+ pages = {45--59},
+ doi = {10.1016/j.tins.2022.09.007},
+ note = {A correction to an equation in Box 2 was published in 2025, DOI 10.1016/j.tins.2024.11.002}
+}
+
+@article{lee2024predictive,
+ author = {Lee, Kwangjun and Dora, Shirin and Mejias, Jorge F. and Bohte, Sander M. and Pennartz, Cyriel M. A.},
+ title = {Predictive Coding with Spiking Neurons and Feedforward Gist Signaling},
+ journal = {Frontiers in Computational Neuroscience},
+ year = {2024},
+ volume = {18},
+ pages = {1338280},
+ doi = {10.3389/fncom.2024.1338280}
+}
+
+@article{chrysanthidis2025shortterm,
+ author = {Chrysanthidis, N. and Fiebig, F. and Lansner, A. and Herman, P.},
+ title = {Short-Term Plasticity Influences Episodic Memory Recall: An Interplay of Synaptic Traces in a Spiking Neural Network Model},
+ journal = {Scientific Reports},
+ year = {2025},
+ volume = {15},
+ pages = {28164},
+ doi = {10.1038/s41598-025-12611-5}
+}
+
+@article{dalba2025semantization,
+ author = {D'Alba, Federico and Kushawaha, Nilay and Fruzzetti, Lorenzo and Paolucci, Pier Stanislao and Falotico, Egidio},
+ title = {Semantization of Memories in a Hippocampal--Cortical Spiking Neural Network},
+ journal = {Neurocomputing},
+ year = {2025},
+ volume = {640},
+ pages = {130323},
+ doi = {10.1016/j.neucom.2025.130323}
+}
+
+@article{hafner2025dreamer,
+ author = {Hafner, Danijar and Pasukonis, Jurgis and Ba, Jimmy and Lillicrap, Timothy},
+ title = {Mastering Diverse Control Tasks through World Models},
+ journal = {Nature},
+ year = {2025},
+ volume = {640},
+ pages = {647--653},
+ doi = {10.1038/s41586-025-08744-2}
+}
+
+@article{sun2025spikingwm,
+ author = {Sun, Yinqian and Zhao, Feifei and Lyu, Mingyang and Zeng, Yi},
+ title = {Spiking World Model with Multicompartment Neurons for Model-Based Reinforcement Learning},
+ journal = {Proceedings of the National Academy of Sciences},
+ year = {2025},
+ volume = {122},
+ number = {50},
+ pages = {e2513319122},
+ doi = {10.1073/pnas.2513319122}
+}
diff --git a/research/literature/stages_8_10_frontier.bib b/research/literature/stages_8_10_frontier.bib
new file mode 100644
index 0000000000000000000000000000000000000000..65abc69bebe8dbe51d76ab009c819862cdd1cff4
--- /dev/null
+++ b/research/literature/stages_8_10_frontier.bib
@@ -0,0 +1,104 @@
+@article{SRC-FRONTIER-PARISI-2019,
+ title={Continual lifelong learning with neural networks: A review},
+ author={Parisi, German I. and Kemker, Ronald and Part, Jose L. and Kanan, Christopher and Wermter, Stefan},
+ journal={Neural Networks},
+ volume={113},
+ pages={54--71},
+ year={2019},
+ doi={10.1016/j.neunet.2019.01.012}
+}
+
+@article{SRC-FRONTIER-EWC-2017,
+ title={Overcoming catastrophic forgetting in neural networks},
+ author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A. and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia},
+ journal={Proceedings of the National Academy of Sciences},
+ volume={114},
+ number={13},
+ pages={3521--3526},
+ year={2017},
+ doi={10.1073/pnas.1611835114}
+}
+
+@inproceedings{SRC-FRONTIER-SI-2017,
+ title={Continual Learning Through Synaptic Intelligence},
+ author={Zenke, Friedemann and Poole, Ben and Ganguli, Surya},
+ booktitle={Proceedings of the 34th International Conference on Machine Learning},
+ series={Proceedings of Machine Learning Research},
+ volume={70},
+ pages={3987--3995},
+ year={2017},
+ publisher={PMLR},
+ url={https://proceedings.mlr.press/v70/zenke17a.html}
+}
+
+@inproceedings{SRC-FRONTIER-HEBBMETA-2020,
+ title={Meta-Learning through Hebbian Plasticity in Random Networks},
+ author={Najarro, Elias and Risi, Sebastian},
+ booktitle={Advances in Neural Information Processing Systems},
+ year={2020},
+ url={https://proceedings.neurips.cc/paper/2020/hash/ee23e7ad9b473ad072d57aaa9b2a5222-Abstract.html}
+}
+
+@article{SRC-FRONTIER-AGMP-2026,
+ title={Astrocyte-gated multi-timescale plasticity for online continual learning in deep spiking neural networks},
+ author={Dong, Zhengshan and He, Wude},
+ journal={Frontiers in Neuroscience},
+ year={2026},
+ doi={10.3389/fnins.2025.1768235}
+}
+
+@inproceedings{SRC-FRONTIER-CATFORMER-2026,
+ title={CATFormer: When Continual Learning Meets Spiking Transformers With Dynamic Thresholds},
+ author={Nagabhushana, Vaishnavi and Agrawal, Kartikay and Borthakur, Ayon},
+ booktitle={Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026},
+ series={Proceedings of Machine Learning Research},
+ volume={308},
+ year={2026},
+ publisher={PMLR},
+ url={https://proceedings.mlr.press/v308/nagabhushana26a.html}
+}
+
+@article{SRC-FRONTIER-CLS-1995,
+ title={Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory},
+ author={McClelland, James L. and McNaughton, Bruce L. and O'Reilly, Randall C.},
+ journal={Psychological Review},
+ volume={102},
+ number={3},
+ pages={419--457},
+ year={1995},
+ doi={10.1037/0033-295X.102.3.419}
+}
+
+@article{SRC-FRONTIER-NII-2026,
+ title={Toward Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognition},
+ author={Amiri Golilarz, Noorbakhsh and Al Khatib, Hassan S. and Rahimi, Shahram},
+ journal={IEEE Access},
+ year={2026},
+ doi={10.1109/ACCESS.2026.3689754}
+}
+
+@article{SRC-FRONTIER-CONAIM-2017,
+ title={CONAIM: A Conscious Attention-Based Integrated Model for Human-Like Robots},
+ author={Simões, Alexandre da Silva and Colombini, Esther Luna and Ribeiro, Carlos Henrique Costa},
+ journal={IEEE Systems Journal},
+ year={2017},
+ doi={10.1109/JSYST.2015.2498542}
+}
+
+@misc{SRC-FRONTIER-DIME-2026,
+ title={The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and Integration},
+ author={Vladu, Ionel Cristian and Bîzdoacă, Nicu George and Pirici, Ionica and Bălșeanu, Tudor-Adrian and Bondoc, Eduard Nicușor},
+ year={2026},
+ eprint={2603.12286},
+ archivePrefix={arXiv},
+ doi={10.48550/arXiv.2603.12286}
+}
+
+@misc{SRC-FRONTIER-SOLAR-2026,
+ title={SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation},
+ author={Vetcha, Nitin and Liu, Dianbo},
+ year={2026},
+ eprint={2605.20189},
+ archivePrefix={arXiv},
+ doi={10.48550/arXiv.2605.20189}
+}
diff --git a/research/literature/storage.bib b/research/literature/storage.bib
new file mode 100644
index 0000000000000000000000000000000000000000..7c126757aff82f7f4b025f2a5c276b0b1193fe50
--- /dev/null
+++ b/research/literature/storage.bib
@@ -0,0 +1,6 @@
+@misc{brain5d2026,
+ author = {Thomas Heisig},
+ title = {Brain-5D: Multidimensional Spiking Neural Network Architecture},
+ year = {2026},
+ note = {Proprietäres .b5d-Speicherformat für mehrdimensionale SNN-Zustände}
+}
diff --git a/research/literature/topology.bib b/research/literature/topology.bib
new file mode 100644
index 0000000000000000000000000000000000000000..05e9586b2b2a399e807000f2f27f50a06e5df431
--- /dev/null
+++ b/research/literature/topology.bib
@@ -0,0 +1,21 @@
+@article{WattsStrogatz1998,
+ author = {Duncan J. Watts and Steven H. Strogatz},
+ title = {Collective dynamics of 'small-world' networks},
+ journal = {Nature},
+ year = {1998},
+ volume = {393},
+ number = {6684},
+ pages = {440--442},
+ doi = {10.1038/30918}
+}
+
+@article{Barabasi1999,
+ author = {Albert-László Barabási and Réka Albert},
+ title = {Emergence of Scaling in Random Networks},
+ journal = {Science},
+ year = {1999},
+ volume = {286},
+ number = {5439},
+ pages = {509--512},
+ doi = {10.1126/science.286.5439.509}
+}
diff --git a/research/paper_offshoots/README.md b/research/paper_offshoots/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..8e3de6ec5f5de5e76f57509ca6562e3c5d2dec81
--- /dev/null
+++ b/research/paper_offshoots/README.md
@@ -0,0 +1,34 @@
+# Paper Offshoots — wissenschaftliche Nebenstränge aus der Gesamtarbeit
+
+**Status:** research planning only
+**Authority:** proposal / programme planning
+**Evidenzrolle:** keine DATA, keine EVID, keine automatische Claim-Promotion
+
+Edition 1.8 ist der gemeinsame Kontext des Forschungsprogramms. Dieses Verzeichnis benennt kleinere, disziplinär enger prüfbare Publikationsstränge, die aus der Gesamtarbeit hervorgehen können. Die Einträge sind **keine fertigen Paper** und keine Zusage, dass jeder Strang veröffentlicht wird. Ein Strang wird erst zu einer eigenständigen Arbeit, wenn Forschungsfrage, Quellenbasis, Methoden-/Analysevertrag, Claim-Grenze und Reviewstatus für diesen Strang separat geklärt sind.
+
+| ID | Arbeitstitel | Primärer Anschluss | Zielgruppe | Aktueller Status | Harte Grenze |
+| --- | --- | --- | --- | --- | --- |
+| **PO-001** | Empirische Grenzen semantischer Verdichtung im Continual Learning | `RQ-S6-SEM-002`, CL-001/002/003 | Continual Learning / Computational Neuroscience | `in_preparation` | CL-002/003 zeigen keinen bestätigten Semantic-over-Raw-Vorteil; Generalisierung über Split-MNIST hinaus ist offen. |
+| **PO-002** | Content/Compute-Trennung und kontrollierte periphere Werkzeuge in SNN-Architekturen | `RQ-GW-001`, Gateway-/Neural-Symbiosis-Programm | Informatik / Systems / Neuro-AI | `concept` | Gateway-Erreichbarkeit ist nicht gleich gelerntem Werkzeuggebrauch; produktive Aktivierung bleibt evidenzgebunden. |
+| **PO-003** | Scientific Integrity und rekursive Epistemik in KI-assistierter Einzelforschung | Edition 1.7/1.8, Research-Driven Development, DATA/EVID-Governance | Metawissenschaft / Research Software Engineering | `concept` | Prozessqualität ist ein methodischer Beitrag, keine neuronale Evidenz und kein Ersatz für externe Review/Replikation. |
+| **PO-004** | Post-Objective Transition und Corrigibility in lernenden Systemen | `RQ-SAFE-008`, `H-SAFE-008-A`, `EXP-SAFE-END-001` | AI Safety / Ethik | `planned` | Der Strang ist prospektiv; HALT/WAIT/REQUEST/PROPOSE sind noch kein ausgeführter Sicherheitsbefund. |
+| **PO-005** | Geometrie-zu-Dynamik-Kopplung in multidimensionalen SNN-Topologien | `RQ-SNN-003`, `H-SNN-003-B`, 5D-v2 | SNN / Network Science | `design_required` | `EXP-GEN-0047` ist inadäquat für die Zielhypothese und weder positiver noch negativer 5D-Befund. |
+| **PO-006** | Kontrolliertes synthetisches Embodiment mit autorisierten Sensor-Aktor-Pfaden | `RQ-EMB-001`, `H-EMB-001-A/B`, Stage-5-Referenz | Embodied AI / Robotics / SNN | `data_available_review_open` | Synthetischer Closed Loop ist keine Realwelt-Autonomie; `H-EMB-001-B` und Real-Device-Übertragbarkeit bleiben offen. |
+
+## Auswahlregel
+
+Ein Nebenstrang wird nur weiterentwickelt, wenn er mindestens eine klar abgrenzbare Forschungsfrage besitzt und gegenüber der Gesamtarbeit **kleiner, prüfbarer und zitierbarer** wird. Reines Umverpacken derselben Aussagen in mehrere Texte ist kein wissenschaftlicher Fortschritt.
+
+Vor einer Einreichung muss jeder Strang mindestens dokumentieren:
+
+1. kanonische RQ/Hypothese oder explizit argumentative Fragestellung;
+2. verwendete DATA/EVID-Quellen mit Provenienz;
+3. primäre Claim-Grenze und verbotene Überinterpretationen;
+4. passende Kontroll-/Vergleichsbedingungen bei empirischen Arbeiten;
+5. Prior-Art- und Quellenprüfung;
+6. Human Review und, wo relevant, unabhängige Replikation;
+7. Verhältnis zur Edition 1.8: übernommen, neu getestet oder nur kontextualisiert.
+
+## Arbeitsregel
+
+Die Nebenstränge dürfen die Gesamtarbeit **nicht rückwirkend als Evidenzquelle verwenden**. Edition 1.8 liefert Kontext und Synthese; empirische Claims müssen auf die jeweiligen Experimente, Reviews und EVID-Artefakte zurückgeführt werden.
diff --git a/research/preregistrations/EXP-S6-SEM-CL-002.md b/research/preregistrations/EXP-S6-SEM-CL-002.md
new file mode 100644
index 0000000000000000000000000000000000000000..397df42d658578049e5b79e1725a68447c98caec
--- /dev/null
+++ b/research/preregistrations/EXP-S6-SEM-CL-002.md
@@ -0,0 +1,178 @@
+# Präregistrierung: EXP-S6-SEM-CL-002
+
+**Titel:** Semantische Prototypkonsolidierung vs. Raw-Experience-Replay bei identischem Speicher- und Updatebudget auf Split-MNIST
+
+**Status:** Präregistriert, nicht ausgeführt.
+
+**Vorgänger:** EXP-S6-SEM-CL-001 (positiver Mechanismus-Befund gegen naive Online-Baseline ohne Replay)
+
+## 1. Forschungsfrage
+
+**RQ-S6-SEM-002:** Liefert die semantische Prototypkonsolidierung von MHRN gegenüber gleich budgetiertem Raw-Experience-Replay einen messbaren Vorteil bei der Reduktion katastrophalen Vergessens?
+
+Dieses Experiment trennt einen möglichen **Semantik-Effekt** von einem allgemeinen **Replay-Effekt**. Fällt B3 gegenüber B2 nicht überlegen aus, darf der positive Effekt aus EXP-S6-SEM-CL-001 nicht der semantischen Verdichtung zugeschrieben werden.
+
+## 2. Hypothesen
+
+**H1 (gerichtet):** Bei identischem Speicherbudget, identischem Updatebudget und identischem Readout reduziert die semantische Prototypkonsolidierung das mittlere Vergessen stärker als Raw-Experience-Replay und verbessert gleichzeitig die finale Accuracy.
+
+**H0:** Kein Unterschied zwischen Semantic-Prototype und Raw-Experience-Replay.
+
+**H2 (explorativ, gegenläufig):** Raw-Experience-Replay ist gleichwertig oder besser, weil reale Erfahrung mehr Information trägt als verdichtete Prototypen.
+
+## 3. Bedingungen
+
+Vier Bedingungen verwenden denselben Readout, dieselbe deterministische Spike-Repräsentation, dieselben Seeds und dieselbe Task-Reihenfolge.
+
+| Bedingung | Speicher | Update-Regel | Budget |
+| --- | --- | --- | --- |
+| **B1 – Naive Online** | keiner | keine Replay-Updates | 0 Repräsentationen, 0 Replay-Schritte |
+| **B2 – Raw-Replay** | N rohe Spike-Muster vergangener Tasks | Replay der Rohmuster | N Repräsentationen, K Replay-Schritte |
+| **B3 – Semantic-Prototype** | N semantische Prototypen | Replay der Prototypen | N Repräsentationen, K Replay-Schritte |
+| **B4 – Random-Prototype** | N zufällige, größenangepasste Spike-Muster | Replay der Zufallsmuster | N Repräsentationen, K Replay-Schritte |
+
+B4 ist eine notwendige Negativkontrolle: Wenn B3 B4 nicht schlägt, kann der Effekt nicht überzeugend der semantischen Struktur der Prototypen zugeschrieben werden.
+
+### Feste Budgets
+
+- **N = 50 Repräsentationen pro Task** in B2, B3 und B4.
+- **K = 100 Replay-Schritte pro Task-Wechsel** in B2, B3 und B4.
+- **M = 5 Tasks**: (0,1), (2,3), (4,5), (6,7), (8,9).
+- Keine Bedingung darf zusätzliche Readout-Updates, zusätzliche Trainingsbeispiele oder versteckte Speicherobjekte erhalten.
+
+## 4. Präregistrierte Seeds
+
+Die Seed-Liste ist vor Ausführung fixiert:
+
+`201, 202, 203, 204, 205, 206, 207, 208, 209`
+
+Damit gilt **n = 9 gepaarte Seeds** für alle vier Bedingungen.
+
+Die Bedingungsreihenfolge wird pro Seed deterministisch aus dem jeweiligen Seed permutiert. Die Permutationsfunktion und alle abgeleiteten Zufallsquellen werden im Analysecode vor Ausführung fixiert.
+
+## 5. Primäre Endpunkte
+
+1. **Final average accuracy** nach Task 5 über alle fünf Tasks.
+2. **Mean forgetting** über Tasks 1–4 nach Abschluss der Sequenz.
+
+Berechnung, Spike-Encoding, Task-Splits und Readout entsprechen EXP-S6-SEM-CL-001, soweit diese Präregistrierung keine explizite Änderung festlegt.
+
+## 6. Konfirmatorische Vergleiche
+
+Die drei vorab festgelegten Primärdifferenzen sind:
+
+1. `accuracy(B3) - accuracy(B2)`
+2. `forgetting(B2) - forgetting(B3)` — positiv bedeutet weniger Vergessen durch B3.
+3. `accuracy(B3) - accuracy(B4)`
+
+Keine andere Differenz kann nachträglich als primärer Endpunkt deklariert werden.
+
+## 7. Präregistrierte Erfolgsregel
+
+EXP-S6-SEM-CL-002 zählt nur dann als **positiver Befund für einen Semantik-Effekt**, wenn **alle** folgenden Bedingungen erfüllt sind:
+
+1. `accuracy(B3) - accuracy(B2) >= 0.03`
+2. `forgetting(B2) - forgetting(B3) >= 0.05`
+3. `accuracy(B3) - accuracy(B4) >= 0.03`
+4. Untere Grenze des gepaarten 95-%-Bootstrap-Konfidenzintervalls ist für **jede** dieser drei Differenzen > 0.
+5. Zweiseitiger gepaarter Sign-Flip-Test ergibt für **jede** der drei Differenzen `p < 0.05`.
+6. Mindestens neun vollständig gepaarte Seeds liegen vor; keine selektive Seed-Auswahl ist zulässig.
+
+### Ergebnisinterpretation
+
+- Bedingungen 1–3 sowie Statistik erfüllt: **preregistered_positive_semantic_effect**.
+- B3 ≈ B2, aber beide schlagen B4/B1: **replay_effect_without_semantic_advantage**.
+- B3 ≈ B2 ≈ B4: **budget_or_generic_replay_effect**.
+- B3 schlechter als B2: **negative_evidence_for_prototype_advantage**.
+- Uneindeutige Statistik ohne klare Gegenrichtung: **null_or_inconclusive_result**.
+
+## 8. Falsifikationskriterien
+
+H1 gilt als falsifiziert, wenn mindestens eines gilt:
+
+- B3 ist gegenüber B2 in der finalen Accuracy nicht signifikant positiv.
+- B3 reduziert Forgetting gegenüber B2 nicht signifikant.
+- B3 schlägt B4 in der finalen Accuracy nicht signifikant.
+- B3 ist in finaler Accuracy oder Forgetting klar schlechter als B2.
+
+Ein falsifizierendes Ergebnis wird persistiert, ausgewertet und publizierbar dokumentiert. Es wird nicht verworfen und nicht durch Wiederholung mit geänderten Parametern ersetzt.
+
+## 9. Ausführungsprotokoll
+
+- **Datensatz:** Split-MNIST, 5 Tasks, identisch zu EXP-S6-SEM-CL-001.
+- **Spike-Repräsentation:** identische deterministische pooled rate coding-Repräsentation wie EXP-S6-SEM-CL-001.
+- **Readout:** identischer Online-Softmax-Readout wie EXP-S6-SEM-CL-001.
+- **Train/Test-Sampling:** identische Logik wie EXP-S6-SEM-CL-001; seedspezifisch deterministisch.
+- **Seeds:** 201–209, identisch über alle Bedingungen.
+- **Bedingungsreihenfolge:** seedspezifisch randomisiert, aber deterministisch reproduzierbar.
+- **Speicherbudget:** exakt N = 50 Repräsentationen pro vergangenem Task in B2/B3/B4.
+- **Replaybudget:** exakt K = 100 Replay-Updates pro Task-Wechsel in B2/B3/B4.
+- **Abbruch:** NaN/Inf, ungültige Dimensionen, Budgetverletzung oder Datenintegritätsfehler führen zum dokumentierten Abbruch des betroffenen Seeds; keine stille Wiederholung.
+
+## 10. Definition der Speicherbedingungen
+
+### B2 – Raw-Replay
+
+Aus den abgeschlossenen Tasks werden rohe, tatsächlich beobachtete Spike-Muster gespeichert. Die Auswahlregel wird vor Ausführung deterministisch fixiert. Pro Task dürfen maximal N = 50 gespeicherte Repräsentationen existieren. Replay zieht ausschließlich aus diesen gespeicherten Mustern.
+
+### B3 – Semantic-Prototype
+
+MHRN `SemanticMemory` konsolidiert unabhängige Episoden zu semantischen Prototypen. Für den Benchmark wird die Repräsentation auf maximal N = 50 Replay-Einheiten pro Task begrenzt. Falls mehr reife Konzepte existieren, wird die vorab fixierte deterministische Auswahlregel angewendet; falls weniger existieren, darf die Bedingung nicht mit zusätzlichen Rohmustern aufgefüllt werden.
+
+### B4 – Random-Prototype
+
+Für jedes semantische Replay-Objekt wird ein zufälliges Kontrollmuster erzeugt, dessen Spike-Anzahl an die Größenverteilung der B3-Prototypen angepasst wird. Die Zufallsquelle ist seedspezifisch und vor Ausführung festgelegt. Labels und Budgets entsprechen B3. B4 darf keine semantischen Ähnlichkeitsinformationen oder Rohbeispiele verwenden.
+
+## 11. Statistik
+
+Für jede primäre Differenz:
+
+- gepaarte Differenz pro Seed,
+- Mittelwert der Differenzen,
+- 95-%-Bootstrap-KI über gepaarte Seeds,
+- zweiseitiger gepaarter Sign-Flip-Test,
+- vollständige seedweise Ergebnistabelle.
+
+Bootstrap-Resamples: **20.000**.
+
+Signifikanzniveau: **alpha = 0,05**.
+
+Es gibt keine nachträgliche Korrektur der drei Primärtests durch Auswahl eines günstigeren Tests. Da die Erfolgsregel alle drei Tests gleichzeitig verlangt, fungiert die Konjunktion als strenge Entscheidungsschranke. Explorative weitere Vergleiche werden separat markiert.
+
+## 12. Was dieses Experiment nicht testet
+
+- keine biologische Validität der Semantisierung,
+- keine Übertragbarkeit auf CIFAR, N-MNIST oder andere Datensätze,
+- keine Überlegenheit des vollständigen rekurrenten MHRN-SNN gegenüber Standard-SNNs,
+- kein Vergleich mit EWC, SI, GEM, LwF oder anderen etablierten Continual-Learning-Methoden,
+- keine Aussage über Bewusstsein, Intelligenz oder biologische Gleichwertigkeit.
+
+## 13. Daten- und Evidenzregeln
+
+- **DATA:** Rohlogs, Seeds, Task-Matrizen, Budgets, gespeicherte Repräsentationsmetadaten, Zustands-/Quellhashes.
+- **EVID:** separate menschlich geprüfte Interpretation.
+- Analysecode wird vor dem ersten Datenlauf fixiert und über SHA-256 gebunden.
+- Präregistrierung wird vor dem ersten Datenlauf nicht mehr inhaltlich geändert. Korrekturen danach benötigen ein Amendment mit Zeitstempel und Begründung.
+- Keine Post-hoc-Änderung von Metriken, Ausschlussregeln, Seeds, N, K oder Erfolgsgrenzen.
+- Post-hoc-Analysen sind zulässig, müssen aber explizit als **explorativ** markiert werden.
+- Automatische DATA→EVID-Promotion bleibt deaktiviert.
+
+## 14. Veröffentlichungsregel
+
+Positiv:
+
+`research/publications/2026-XX-XX_semantic-vs-raw-replay/`
+
+Negativ/falsifiziert:
+
+`research/publications/2026-XX-XX_semantic-vs-raw-replay-negative/`
+
+Null/uneindeutig:
+
+`research/publications/2026-XX-XX_semantic-vs-raw-replay-null/`
+
+Jede Fassung enthält Präregistrierung, vollständige DATA, EVID, Protokollabweichungen, statistische Auswertung und Interpretation.
+
+---
+
+> Dieses Experiment entscheidet, ob der positive Befund aus EXP-S6-SEM-CL-001 ein MHRN-spezifischer Semantik-Beitrag ist oder im Wesentlichen ein Replay-/Budget-Effekt. Die Entscheidungskriterien sind vor der Ausführung festgelegt, nicht danach.
diff --git a/research/preregistrations/EXP-S6-SEM-CL-003.md b/research/preregistrations/EXP-S6-SEM-CL-003.md
new file mode 100644
index 0000000000000000000000000000000000000000..23da349ae51fe20a5773b093b8d1658c3d536378
--- /dev/null
+++ b/research/preregistrations/EXP-S6-SEM-CL-003.md
@@ -0,0 +1,267 @@
+# Präregistrierung: EXP-S6-SEM-CL-003
+
+**Titel:** Replay-Dosis × Repräsentation bei konstantem Gesamt-Updatebudget auf Split-MNIST
+
+**Status:** Präregistriert, nicht ausgeführt.
+
+**Vorgänger:**
+
+- `EXP-S6-SEM-CL-001`: positiver Mechanismusbefund gegenüber naiver No-Replay-Baseline;
+- `EXP-S6-SEM-CL-002`: negativer Befund für einen semantischen Prototyp-Vorteil gegenüber gleich budgetiertem Raw-Replay bei 100 Replay-Updates pro Task-Wechsel.
+
+## 1. Forschungsfrage
+
+**RQ-S6-SEM-003:** Hängt ein möglicher Vorteil semantischer Prototypkonsolidierung gegenüber Raw-Experience-Replay von der Replay-Dosis ab, wenn das **Gesamt-Updatebudget über alle Bedingungen konstant** gehalten wird?
+
+CL-003 testet damit die nach CL-002 offene mechanistische Alternative: Der negative CL-002-Befund könnte ein Regimeeffekt einer zu geringen Replay-Exposition sein. Diese Hypothese wird in einem neuen Experiment getestet; CL-002 wird nicht verändert oder erneut als konfirmatorischer Lauf ausgeführt.
+
+## 2. Hypothesen
+
+### H1 — semantischer Vorteil im CL-001-nahen 20%-Regime
+
+Bei 20% Replay-Replacement erzielt Semantic-Prototype-Replay gegenüber Raw-Replay:
+
+- mindestens +3 Prozentpunkte finale mittlere Accuracy und
+- mindestens 5 Prozentpunkte weniger mittleres Forgetting.
+
+Zusätzlich muss Semantic-Prototype-Replay bei 20% die größenangepasste Random-Prototype-Kontrolle um mindestens +3 Prozentpunkte finale Accuracy schlagen.
+
+### H2 — Replay-Dosis-Interaktion
+
+Der Accuracy-Vorteil `Semantic − Raw` ist bei 20% Replay-Replacement mindestens **+1,5 Prozentpunkte größer** als bei 5% Replay-Replacement.
+
+H2 prüft, ob die Repräsentationsdifferenz mit Replay-Exposition sichtbar wird, statt nur einen allgemeinen Replay-Effekt abzubilden.
+
+### H0
+
+Kein praktisch relevanter semantischer Vorteil und keine praktisch relevante Dosis-Interaktion.
+
+### H3 — explorative Gegenhypothese
+
+Raw-Replay bleibt über alle Dosen gleichwertig oder besser. Eine hohe Replay-Dosis kann semantische Kompression sogar verschlechtern, wenn relevante Einzelfallinformation verloren geht.
+
+## 3. Datensatz und gemeinsame Pipeline
+
+- Datensatz: kanonisches MNIST, Split-MNIST mit fünf Tasks: `(0,1)`, `(2,3)`, `(4,5)`, `(6,7)`, `(8,9)`.
+- Train: 1000 Beispiele je Klasse.
+- Test: 200 Beispiele je Klasse.
+- Spike-Encoding: identisch zu CL-001/CL-002: 4×4 Average Pooling auf 7×7, Schwellen `(0.2, 0.4, 0.6, 0.8)`, deterministische sparse Spike-IDs.
+- Readout: identischer `OnlineSoftmaxReadout`, Learning Rate `0.025`.
+- SemanticMemory: unverändert mit `min_episode_support=8`, `prototype_support=0.30`, `match_threshold=0.25`.
+- Keine Testdaten für Training, Speicherwahl, Prototypbildung, Hyperparameterwahl oder Ausschlussentscheidung.
+
+## 4. Seeds
+
+Zwölf neue gepaarte Seeds, die weder in CL-001 noch CL-002 verwendet wurden:
+
+`301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312`
+
+Alle Bedingungen werden für jeden Seed mit identischen Train-/Test-Indizes ausgeführt.
+
+## 5. Bedingungen
+
+### Konfirmatorische Bedingungen
+
+| ID | Repräsentation | Replay-Replacement | Replay-Slots je späterem Task | Gesamtupdates je Seed |
+| --- | --- | ---: | ---: | ---: |
+| R05 | Raw | 5% | 100 von 2000 | 10.000 |
+| S05 | Semantic | 5% | 100 von 2000 | 10.000 |
+| R20 | Raw | 20% | 400 von 2000 | 10.000 |
+| S20 | Semantic | 20% | 400 von 2000 | 10.000 |
+| X20 | Random matched | 20% | 400 von 2000 | 10.000 |
+
+### Präregistrierte Dosis-Erweiterung
+
+| ID | Repräsentation | Replay-Replacement | Replay-Slots je späterem Task | Gesamtupdates je Seed |
+| --- | --- | ---: | ---: | ---: |
+| R40 | Raw | 40% | 800 von 2000 | 10.000 |
+| S40 | Semantic | 40% | 800 von 2000 | 10.000 |
+
+Die 40%-Bedingungen werden vollständig persistiert und präregistriert analysiert, sind aber **nicht Teil der konjunktiven H1-Erfolgsregel**. Sie dienen der Prüfung von Sättigung bzw. möglicher Über-Replay-Verschlechterung.
+
+## 6. Konstantes Updatebudget
+
+Jeder Seed und jede Bedingung besitzt exakt **10.000 Readout-Updates**:
+
+- Task 1: 2000 aktuelle Trainingsupdates, kein Replay;
+- Tasks 2–5: jeweils exakt 2000 Update-Slots;
+- Replay ersetzt aktuelle Task-Updates, es werden keine zusätzlichen Updates angehängt.
+
+Damit unterscheiden sich Replay-Dosen **nicht** in der Gesamtzahl der Optimierungsschritte.
+
+### Replay-Slot-Positionen
+
+Für jeden `(seed, task_index, dose)` wird vor Bedingungsausführung eine deterministische Slot-Maske erzeugt. Die Replay-Slots werden über die 2000 Positionen möglichst gleichmäßig verteilt; Ties werden deterministisch über einen SHA-256-abgeleiteten Seed aufgelöst.
+
+Für Raw und Semantic derselben Dosis sind die Replay-Slot-Positionen **identisch**. Damit werden innerhalb eines Dosisvergleichs exakt dieselben aktuellen Trainingspositionen ersetzt.
+
+X20 verwendet dieselben Slot-Positionen wie R20/S20.
+
+## 7. Speicherbudget
+
+Die CL-002-A1-Regel bleibt erhalten.
+
+Für Task `t`:
+
+`R_t = min(50, number_of_mature_semantic_prototypes_for_task_t)`
+
+Dann speichern:
+
+- Semantic exakt `R_t` reife Prototypen;
+- Raw exakt `R_t` rohe Trainingsrepräsentationen, deterministisch nach aufsteigendem originalen MNIST-Index;
+- Random exakt `R_t` objektweise an Semantic gematchte Zufallsrepräsentationen.
+
+Das realisierte Objektbudget ist damit innerhalb jedes Seeds und Tasks identisch.
+
+### Random-Matching
+
+X20 matched jedes semantische Objekt in:
+
+- Task,
+- Label,
+- Feature-Dimension,
+- Spike-Anzahl.
+
+Spike-IDs werden ohne Zurücklegen gleichverteilt aus dem gültigen Feature-Raum gezogen. Random darf keine semantischen IDs, Rohmuster-IDs oder Testinformation verwenden.
+
+## 8. Replay-Auswahl innerhalb der Slots
+
+Zu jedem Zeitpunkt stehen nur Speicherobjekte aus **bereits abgeschlossenen Tasks** zur Verfügung.
+
+Die aktuell verfügbaren Replay-Objekte werden in kanonischer Reihenfolge `(task_index, label, source_rank, spike_ids)` gehalten. Replay-Slots werden deterministisch zyklisch/balanciert über diesen Pool verteilt; die maximale Nutzungsdifferenz zwischen zwei verfügbaren Objekten darf pro Task höchstens 1 betragen.
+
+Kein Sampling anhand aktueller Testleistung oder späterer Task-Ergebnisse.
+
+## 9. Bedingungsreihenfolge und Isolation
+
+Die sieben Bedingungen werden je Seed in einer deterministischen, ausschließlich seed-basierten Permutation ausgeführt.
+
+Jede Bedingung startet mit:
+
+- neu initialisiertem Readout,
+- eigenem Memory-Zustand,
+- identischen Datensatz-Indizes,
+- identischem Encoder,
+- gleicher Readout-Initialisierung für denselben Seed.
+
+Es darf kein trainierbarer Zustand zwischen Bedingungen geteilt werden.
+
+## 10. Primäre Endpunkte
+
+Wie CL-001/CL-002:
+
+1. **Final average accuracy** über alle fünf Tasks nach Task 5.
+2. **Mean forgetting** für Tasks 1–4: beste historische Task-Accuracy minus finale Task-Accuracy.
+
+## 11. Konfirmatorische Kontraste
+
+### H1-Kontraste bei 20%
+
+C1: `S20 − R20` final average accuracy.
+
+C2: `R20 − S20` mean forgetting. Positive Werte bedeuten weniger Forgetting für Semantic.
+
+C3: `S20 − X20` final average accuracy.
+
+### H2-Dosisinteraktion
+
+C4: `(S20 − R20) − (S05 − R05)` final average accuracy.
+
+## 12. Präregistrierte Erfolgsregeln
+
+### H1 bestätigt nur wenn alle Bedingungen erfüllt sind
+
+- C1 Mittelwert ≥ `+0.03`;
+- C2 Mittelwert ≥ `+0.05`;
+- C3 Mittelwert ≥ `+0.03`;
+- untere Grenze des gepaarten 95%-Bootstrap-KI > 0 für C1, C2 und C3;
+- zweiseitiger gepaarter exakter Sign-Flip-Test p < 0.05 für C1, C2 und C3;
+- alle 12 Seeds vollständig gepaart.
+
+### H2 bestätigt nur wenn
+
+- C4 Mittelwert ≥ `+0.015`;
+- untere Grenze des 95%-Bootstrap-KI > 0;
+- zweiseitiger gepaarter Sign-Flip-Test p < 0.05;
+- alle 12 Seeds vollständig gepaart.
+
+H1 und H2 werden separat entschieden. Ein positives H2 rettet kein negatives H1 und umgekehrt.
+
+## 13. Präregistrierte 40%-Analyse
+
+Für `S40 − R40` werden final average accuracy und mean forgetting mit demselben Bootstrap/Sign-Flip-Verfahren berichtet.
+
+Die Analyse wird als **preregistered_secondary** markiert. Es gibt keine konfirmatorische Mindestwirkung und sie entscheidet H1/H2 nicht.
+
+Zusätzlich werden die semantischen Raw-Differenzen bei 5%, 20% und 40% deskriptiv nebeneinander berichtet, um Nichtlinearität sichtbar zu machen. Es wird post hoc keine optimale Dosis als konfirmatorische Dosis ausgewählt.
+
+## 14. Statistik
+
+- gepaarter Bootstrap: 20.000 Resamples;
+- 95%-KI über Seed-gepaarte Differenzen;
+- exakter zweiseitiger Sign-Flip-Test;
+- keine automatische Mehrfachtest-Korrektur über C1–C3, da H1 eine **konjunktive** Regel ist und alle drei passieren müssen;
+- H2 ist eine separat benannte Hypothese;
+- 40%-Analyse sekundär.
+
+## 15. Abbruch- und Missing-Regel
+
+Ein Seed gilt als unvollständig, sobald eine der sieben Bedingungen NaN/Inf erzeugt oder technisch nicht regelkonform beendet wird.
+
+- Teil-DATA und Grund werden persistiert.
+- kein automatischer Wiederholungslauf;
+- kein Ersatzseed;
+- H1/H2 können nur bei 12 vollständig gepaarten Seeds positiv werden;
+- bei weniger als 12 vollständigen Seeds: `incomplete_preregistered_run`, deskriptive Analyse erlaubt, keine positive konfirmatorische Entscheidung.
+
+## 16. Pre-execution Freeze
+
+Vor dem ersten empirischen CL-003-Lauf müssen geschrieben, synthetisch getestet und SHA-256-gebunden sein:
+
+- Datensatz-/Indexbildung;
+- Spike-Encoding;
+- deterministische Replay-Slot-Masken;
+- Raw/Semantic/Random-Memory-Bildung;
+- Budget-/Updatezähler;
+- sieben Bedingungen;
+- Endpunktberechnung;
+- C1–C4-Berechnung;
+- Bootstrap und Sign-Flip;
+- H1/H2-Entscheidungslogik;
+- 40%-Sekundäranalyse;
+- Abort-/Missing-Logik.
+
+Vor Freeze sind nur synthetische Testdaten zulässig, die nicht aus MNIST, CL-001, CL-002 oder künftigen CL-003-DATA abgeleitet sind.
+
+## 17. Was CL-003 nicht testet
+
+- keinen vollständigen rekurrenten MHRN-vs-Standard-SNN-Vergleich;
+- keine biologische Validität;
+- keine Generalisierung auf andere Datensätze;
+- keine Optimierung der SemanticMemory-Schwellen;
+- keine Auswahl einer „besten“ Replay-Dosis nach Ergebnis;
+- keine nachträgliche Rehabilitierung von CL-002.
+
+## 18. Ergebnisinterpretation
+
+| H1 | H2 | Interpretation |
+| --- | --- | --- |
+| positiv | positiv | semantischer Vorteil im 20%-Regime plus Evidenz für Dosisabhängigkeit |
+| positiv | negativ | semantischer Vorteil bei 20%, aber keine Evidenz, dass er gegenüber 5% dosisbedingt größer wird |
+| negativ | positiv | Dosis verändert den relativen Effekt, aber 20% erreicht keinen praktisch ausreichenden semantischen Vorteil |
+| negativ | negativ | keine konfirmatorische Unterstützung für semantischen Prototyp-Vorteil oder die vorgeschlagene Dosisalternative |
+
+Jede Kombination wird veröffentlicht und bleibt wissenschaftlich verwertbar.
+
+## 19. DATA/EVID/Veröffentlichung
+
+- Roh- und Auswertungs-DATA vollständig persistieren.
+- Machine Report bleibt DATA-only.
+- Human EVID separat nach DATA-Merge.
+- Keine automatische Evidenzpromotion.
+- Negative und Nullbefunde werden veröffentlicht.
+- Jede Änderung nach erstem empirischem Lauf erfordert eine neue Experiment-ID.
+
+---
+
+**Zusammenfassung:** CL-003 prüft, ob der in CL-002 nicht bestätigte semantische Vorteil bei einer CL-001-näheren Replay-Exposition sichtbar wird, ohne die Zahl der Readout-Updates zu erhöhen. Repräsentation und Replay-Dosis werden getrennt, bevor neue Daten gesehen werden.
diff --git a/research/preregistrations/PREREG-5D-005.json b/research/preregistrations/PREREG-5D-005.json
new file mode 100644
index 0000000000000000000000000000000000000000..1cba6a24609c9913c71bd3addb2adeed574d0d12
--- /dev/null
+++ b/research/preregistrations/PREREG-5D-005.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-5D-005","research_question":"RQ-5D-005","hypothesis":"H-5D-005-A","protocol_id":"topology_matched_5d_v1","mode":"CONFIRMATORY",
+ "primary_outcomes":["first_response_latency","last_response_latency","propagation_depth","total_spikes"],
+ "secondary_outcomes":["delivered_synaptic_events","activated_neurons"],
+ "conditions":[{"id":"1d","role":"control"},{"id":"2d","role":"comparison"},{"id":"3d","role":"comparison"},{"id":"5d","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":30,"rule":"same seed set for every dimensional embedding"},
+ "stopping_rule":"complete every dimensional embedding for every registered seed",
+ "inclusion_criteria":["equal neuron count","equal synapse count","equal degree and weight pattern","identical stimulus schedule"],
+ "exclusion_criteria":["topology mismatch other than coordinates/dimensions","seed mismatch","invalid provenance"],
+ "analysis_plan":{"descriptive_statistics":["condition distributions","paired differences versus 5d","effect ratios"],"inference_policy":"No 5D-specific claim unless matched controls differ on a preregistered outcome.","planned_tests":[]},
+ "expected_failure_modes":["all dimensions remain identical","coordinate embedding is behaviorally inert in this topology"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot redesign matched topology after results are seen.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-EMB001B-MATCHED-V2.json b/research/preregistrations/PREREG-EMB001B-MATCHED-V2.json
new file mode 100644
index 0000000000000000000000000000000000000000..c544abb88d37aad06c86014317982e72dd1464d8
--- /dev/null
+++ b/research/preregistrations/PREREG-EMB001B-MATCHED-V2.json
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-EMB001B-MATCHED-V2",
+ "research_question": "RQ-EMB-001",
+ "hypothesis": "H-EMB-001-B",
+ "protocol": "embodied_closed_loop_matched_disturbance_v2",
+ "mode": "PROSPECTIVE_EXPLORATORY_MATCHED_DISTURBANCE",
+ "execution_authorized": true,
+ "authorization_scope": "single DATA-only exploratory execution; execution authorization is not human scientific review",
+ "human_review_required": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "independent_replication": false,
+ "predecessor_protocol": "embodied_closed_loop_v1",
+ "design_rationale": "The earlier native screen used a donor tape generated without the registered external perturbation. V2 makes the matched-disturbance requirement explicit by using the actual perturbation-exposed closed-loop arm of each seed as the yoked sensor donor while applying the same external perturbation schedule to all three arms.",
+ "design": {
+ "conditions": [
+ "closed_loop",
+ "yoked_replay",
+ "feedback_absent"
+ ],
+ "seeds": [
+ 8201,
+ 8202,
+ 8203,
+ 8204,
+ 8205,
+ 8206,
+ 8207,
+ 8208,
+ 8209,
+ 8210,
+ 8211,
+ 8212,
+ 8213,
+ 8214,
+ 8215,
+ 8216,
+ 8217,
+ 8218,
+ 8219,
+ 8220
+ ],
+ "ticks_per_run": 1000,
+ "inferential_unit": "paired_seed",
+ "primary_outcome": "tracking_rmse_rad",
+ "secondary_outcomes": [
+ "sensor_path_rms_q_rad",
+ "sensor_path_mean_abs_q_rad",
+ "final_body_state",
+ "total_spikes",
+ "synaptic_events_delivered"
+ ],
+ "external_perturbation": {
+ "kind": "constant_external_torque_window",
+ "torque_nm": 0.003,
+ "start_tick_inclusive": 250,
+ "end_tick_exclusive": 500,
+ "identical_for_all_conditions": true
+ },
+ "yoked_replay_contract": "For every seed, yoked_replay consumes the complete sensor tape produced by that seed's actual closed_loop arm under the same external perturbation.",
+ "feedback_absent_contract": "Observed position and velocity are fixed to zero while the body still receives the identical external perturbation schedule."
+ },
+ "analysis_plan": {
+ "primary_comparisons": [
+ "yoked_replay minus closed_loop tracking_rmse_rad",
+ "feedback_absent minus closed_loop tracking_rmse_rad"
+ ],
+ "secondary_comparisons": [
+ "yoked_replay minus closed_loop sensor_path_rms_q_rad",
+ "feedback_absent minus closed_loop sensor_path_rms_q_rad"
+ ],
+ "direction_convention": "positive control-minus-closed_loop difference means lower error in closed_loop",
+ "report": [
+ "condition means",
+ "paired mean difference",
+ "paired median difference",
+ "paired minimum and maximum difference",
+ "fraction of paired seeds with lower closed_loop error"
+ ],
+ "p_values": false,
+ "confirmatory_thresholds": false,
+ "outcome_dependent_stopping": false
+ },
+ "integrity_requirements": [
+ "clean source checkout before execution",
+ "all 60 registered condition-by-seed runs present",
+ "1000 executed ticks in every run",
+ "no runtime errors",
+ "same initial neural state within each seed",
+ "same initial body state within each seed",
+ "same graph fingerprint within each seed",
+ "yoked donor sensor digest equals the actual closed_loop sensor digest for that seed",
+ "feedback_absent observed sensor values are exactly zero",
+ "identical registered perturbation schedule for all arms"
+ ],
+ "stopping_rule": "Execute exactly the preregistered 20 seeds and three conditions. Retain failures; no replacement seeds and no outcome-dependent stopping.",
+ "claim_boundary": "This is prospective exploratory synthetic DATA for the scoped H-EMB-001-B matched-disturbance comparison. It is not confirmatory EVID, not independent replication, not evidence of learned body schema, autonomy, consciousness, biological equivalence, real-device transfer, or 5D superiority. Human Review remains required."
+}
diff --git a/research/preregistrations/PREREG-EMB001B-TEMPORAL-V3.json b/research/preregistrations/PREREG-EMB001B-TEMPORAL-V3.json
new file mode 100644
index 0000000000000000000000000000000000000000..a62273984275b42b600d92e674d9a42c68665809
--- /dev/null
+++ b/research/preregistrations/PREREG-EMB001B-TEMPORAL-V3.json
@@ -0,0 +1,110 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-EMB001B-TEMPORAL-V3",
+ "research_question": "RQ-EMB-001",
+ "hypothesis": "H-EMB-001-B",
+ "protocol": "embodied_closed_loop_temporal_shift_v3",
+ "mode": "PROSPECTIVE_EXPLORATORY_TEMPORAL_CAUSALITY",
+ "execution_authorized": true,
+ "authorization_scope": "single DATA-only exploratory execution; execution authorization is not human scientific review",
+ "human_review_required": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "independent_replication": false,
+ "predecessor_experiment": "EXP-EMB001B-MATCHED-V2-20260920",
+ "design_rationale": "The predecessor produced exact equality between closed_loop and exact yoked_replay on all 20 paired seeds. V3 therefore changes only temporal alignment of the same perturbation-exposed donor sensor content. Circular temporal shifts preserve the donor sensor-value multiset while breaking tick alignment. This is an exploratory causal-timing ablation, not a confirmatory rescue of H-EMB-001-B.",
+ "design": {
+ "conditions": [
+ "closed_loop",
+ "yoked_replay_exact",
+ "yoked_replay_shift20",
+ "yoked_replay_shift100",
+ "feedback_absent"
+ ],
+ "seeds": [
+ 8301,
+ 8302,
+ 8303,
+ 8304,
+ 8305,
+ 8306,
+ 8307,
+ 8308,
+ 8309,
+ 8310,
+ 8311,
+ 8312,
+ 8313,
+ 8314,
+ 8315,
+ 8316,
+ 8317,
+ 8318,
+ 8319,
+ 8320
+ ],
+ "ticks_per_run": 1000,
+ "inferential_unit": "paired_seed",
+ "primary_outcome": "tracking_rmse_rad",
+ "secondary_outcomes": [
+ "sensor_path_rms_q_rad",
+ "sensor_path_mean_abs_q_rad",
+ "final_body_state",
+ "total_spikes",
+ "synaptic_events_delivered"
+ ],
+ "temporal_shifts_ticks": [
+ 20,
+ 100
+ ],
+ "shift_rule": "circular_right_shift_of_same_seed_perturbed_closed_loop_sensor_tape",
+ "content_preservation_rule": "shifted donor tape must contain exactly the same sensor-pair multiset as the exact donor tape",
+ "external_perturbation": {
+ "kind": "constant_external_torque_window",
+ "torque_nm": 0.003,
+ "start_tick_inclusive": 250,
+ "end_tick_exclusive": 500,
+ "identical_for_all_conditions": true
+ }
+ },
+ "analysis_plan": {
+ "primary_comparisons": [
+ "yoked_replay_shift20 minus closed_loop tracking_rmse_rad",
+ "yoked_replay_shift100 minus closed_loop tracking_rmse_rad"
+ ],
+ "reference_comparisons": [
+ "yoked_replay_exact minus closed_loop tracking_rmse_rad",
+ "feedback_absent minus closed_loop tracking_rmse_rad"
+ ],
+ "secondary_comparisons": [
+ "all controls minus closed_loop sensor_path_rms_q_rad"
+ ],
+ "direction_convention": "positive control-minus-closed_loop difference means lower error in closed_loop",
+ "report": [
+ "condition means",
+ "paired mean difference",
+ "paired median difference",
+ "paired minimum and maximum difference",
+ "fraction of paired seeds with lower closed_loop error"
+ ],
+ "p_values": false,
+ "confirmatory_thresholds": false,
+ "outcome_dependent_stopping": false
+ },
+ "integrity_requirements": [
+ "clean source checkout before execution",
+ "all 100 registered condition-by-seed runs present",
+ "1000 executed ticks in every run",
+ "no runtime errors",
+ "same initial neural state within each seed",
+ "same initial body state within each seed",
+ "same graph fingerprint within each seed",
+ "exact yoked donor digest equals the actual perturbed closed-loop sensor digest for that seed",
+ "shifted donor tapes preserve the exact donor sensor-value multiset",
+ "shift20 and shift100 donor digests differ from the exact donor digest",
+ "feedback_absent observed sensor values are exactly zero",
+ "identical registered perturbation schedule for all arms"
+ ],
+ "stopping_rule": "Execute exactly the preregistered 20 seeds and five conditions. Retain failures; no replacement seeds and no outcome-dependent stopping.",
+ "claim_boundary": "This is prospective exploratory synthetic DATA testing temporal alignment within the scoped H-EMB-001-B programme. It is not confirmatory EVID, not independent replication, not evidence of learned body schema, autonomy, consciousness, biological equivalence, real-device transfer, or 5D superiority. Human Review remains required."
+}
diff --git a/research/preregistrations/PREREG-EMB002A-PROPRIOCEPTION-DIRECT-V3.json b/research/preregistrations/PREREG-EMB002A-PROPRIOCEPTION-DIRECT-V3.json
new file mode 100644
index 0000000000000000000000000000000000000000..82f1d1523bce36b07ecdac4da3ce4f26892ce0f7
--- /dev/null
+++ b/research/preregistrations/PREREG-EMB002A-PROPRIOCEPTION-DIRECT-V3.json
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-EMB002A-PROPRIOCEPTION-DIRECT-V3",
+ "research_question": "RQ-EMB-002",
+ "hypothesis": "H-EMB-002-A",
+ "protocol": "embodied_proprioception_direct_v3",
+ "mode": "PROSPECTIVE_DIRECT_DATA",
+ "execution_authorized": true,
+ "authorization_scope": "single DATA-only prospective execution; execution authorization is not human scientific review",
+ "human_review_required": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "independent_replication": false,
+ "method_history": {
+ "exploratory_predecessor": "EXP-EMB002-PROPRIO-V2-20260920 / PR #176 used seeds 8301-8320 as prospective exploratory DATA.",
+ "non_evidentiary_rule_freeze": "EXP-EMB002A-PROPRIOCEPTION-V2-20260920 / PR #179 reused seeds 8301-8320 after the exploratory run and therefore is excluded as a fresh direct test. Its DATA-only support rule is retained unchanged as the preregistered V3 decision rule.",
+ "fresh_seed_contract": "V3 uses seeds 920001-920020, which were not used in the prior H-EMB-002-A V2/direct-V2 runs."
+ },
+ "design": {
+ "conditions": [
+ "closed_loop",
+ "delayed_proprioception",
+ "feedback_absent",
+ "timing_shuffle"
+ ],
+ "seeds": [
+ 920001,
+ 920002,
+ 920003,
+ 920004,
+ 920005,
+ 920006,
+ 920007,
+ 920008,
+ 920009,
+ 920010,
+ 920011,
+ 920012,
+ 920013,
+ 920014,
+ 920015,
+ 920016,
+ 920017,
+ 920018,
+ 920019,
+ 920020
+ ],
+ "ticks_per_run": 1000,
+ "delay_ticks": 20,
+ "inferential_unit": "paired_seed",
+ "primary_outcome": "tracking_rmse_rad",
+ "primary_comparisons": [
+ "delayed_proprioception minus closed_loop",
+ "feedback_absent minus closed_loop"
+ ],
+ "secondary_comparison": "timing_shuffle minus closed_loop",
+ "external_perturbation": true
+ },
+ "analysis_plan": {
+ "direction_convention": "positive control-minus-closed_loop difference means lower error under intact proprioception",
+ "report": [
+ "condition means",
+ "paired mean difference",
+ "paired median difference",
+ "paired minimum and maximum difference",
+ "fraction of paired seeds with lower closed_loop error"
+ ],
+ "direct_hypothesis_test": true,
+ "support_rule_for_data_label_only": "both primary paired mean differences are positive and both primary fractions_closed_loop_lower exceed 0.5",
+ "p_values": false,
+ "confirmatory_thresholds": false,
+ "outcome_dependent_stopping": false
+ },
+ "integrity_requirements": [
+ "clean source checkout before execution",
+ "all 80 registered condition-by-seed runs present",
+ "1000 executed ticks in every run",
+ "no runtime errors",
+ "same initial neural state within each seed",
+ "same initial body state within each seed",
+ "same graph fingerprint within each seed",
+ "timing_shuffle donor digest equals same-seed intact closed-loop sensor digest",
+ "feedback_absent observations are exactly zero"
+ ],
+ "stopping_rule": "Execute exactly 20 paired seeds across all four arms. Retain failures; no replacement seeds and no outcome-dependent stopping.",
+ "claim_boundary": "Prospective direct synthetic DATA for H-EMB-002-A in the fixed six-neuron joint fixture, using a fresh H-EMB-002-A seed set after the decision rule was frozen. This does not establish biological proprioception, learned body schema, real-device transfer, autonomy, consciousness, independent replication, or scientific EVID. Human Review remains required."
+}
diff --git a/research/preregistrations/PREREG-GEN-001.json b/research/preregistrations/PREREG-GEN-001.json
new file mode 100644
index 0000000000000000000000000000000000000000..7b8003f0a35cb0fc9fc5e36409e175a4df8f0573
--- /dev/null
+++ b/research/preregistrations/PREREG-GEN-001.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-GEN-001","research_question":"RQ-GEN-001","hypothesis":"H-GEN-001-A","protocol_id":"learning_generalization_v1","mode":"CONFIRMATORY",
+ "primary_outcomes":["generalization_success","p_success_after","mean_weight_delta"],
+ "secondary_outcomes":["trained_target_peak_v","reward_weight_updates"],
+ "conditions":[{"id":"learning_off","role":"control"},{"id":"sham_replay","role":"sham"},{"id":"learning_on","role":"treatment"},{"id":"drive_perturbations","role":"comparison"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"predeclared integer seeds with frozen train/validation/holdout partitions"},
+ "stopping_rule":"complete all learning conditions and registered probe perturbations for every seed",
+ "inclusion_criteria":["valid disjoint train/validation/holdout partitions","clean source freeze","no fatal runtime error"],
+ "exclusion_criteria":["partition overlap","post-hoc trial reassignment","invalid provenance"],
+ "analysis_plan":{"descriptive_statistics":["condition success fraction","weight delta","peak-v distribution"],"inference_policy":"Generalization requires improvement on held-out/perturbed probes relative to both controls; no significance claim without preregistered test.","planned_tests":[]},
+ "expected_failure_modes":["training-only gain","sham matches learning","seed-insensitive deterministic pseudo-replication"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot alter partitions, rewards, protocol, DATA or evidence.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-LIFE-001.json b/research/preregistrations/PREREG-LIFE-001.json
new file mode 100644
index 0000000000000000000000000000000000000000..7a19c339bf8f6cb3852cc3858949645fb314e834
--- /dev/null
+++ b/research/preregistrations/PREREG-LIFE-001.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-LIFE-001","research_question":"RQ-LIFE-001","hypothesis":"H-LIFE-001-A","protocol_id":"learning_interference_screen_v1","mode":"EXPLORATORY",
+ "primary_outcomes":["retained_success_fraction","weight_range","task_successes"],
+ "secondary_outcomes":["task_final_mean_weights"],
+ "conditions":[{"id":"single_task_baseline","role":"control"},{"id":"sequential_three_task_screen","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"fixed seed set with frozen task order and perturbations"},
+ "stopping_rule":"complete all registered tasks for all seeds",
+ "inclusion_criteria":["task order frozen","same learning rule across tasks","clean source freeze"],
+ "exclusion_criteria":["post-hoc task reordering","seed replacement after outcome inspection","invalid provenance"],
+ "analysis_plan":{"descriptive_statistics":["retained success fraction","weight range","task-wise success"],"inference_policy":"This v1 protocol is a precursor screen; it must not be described as catastrophic-forgetting evidence until a shared-network sequential-learning protocol is registered.","planned_tests":[]},
+ "expected_failure_modes":["independent task instances mask interference","all tasks succeed identically"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot alter task order, seeds or inclusion rules.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-MSBA-E01.json b/research/preregistrations/PREREG-MSBA-E01.json
new file mode 100644
index 0000000000000000000000000000000000000000..f40f19342df069f321ecd35041c6ccd7b2ee870c
--- /dev/null
+++ b/research/preregistrations/PREREG-MSBA-E01.json
@@ -0,0 +1,29 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-MSBA-E01",
+ "research_question": "RQ-MSBA-E01",
+ "hypothesis": "H-MSBA-E01-A",
+ "protocol_id": "msba_energy_efficiency_v1",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": ["normalized_energy_units_per_correct_decision", "synaptic_events_per_correct_decision", "task_accuracy"],
+ "secondary_outcomes": ["projection_control_accuracy", "information_destroyed_accuracy"],
+ "conditions": ["audio", "vision", "digital", "frozen_gateway", "timing_shuffle", "activity_matched_information_destroyed"],
+ "seed_strategy": {"minimum_independent_seeds": 3, "rule": "independent deterministic seeds; no seed reuse for inference"},
+ "stopping_rule": "48 matched tasks per modality and seed; stop only on recorded runtime failure.",
+ "inclusion_criteria": ["all declared adapter provenance fields present", "matched task set identical across modalities"],
+ "exclusion_criteria": ["missing provenance", "payload mutation", "production peripheral activation"],
+ "analysis_plan": {"inference_policy": "compare modalities only under matched tasks and independent seeds; normalized energy is not physical joules"},
+ "freeze": {"status": "FROZEN", "immutable_after_first_run": true, "human_review_required": true},
+ "commit_sha": "3b4dceb0c0c0ebd52cd7b1805932da40cbf9efa6",
+ "environment_manifest": "captured_by_execution_manifest",
+ "independent_seed_plan": [101, 102, 103],
+ "gateway_configuration": {"plasticity": false, "structural_growth": false, "adaptive_allocation": false},
+ "modality_adapter_versions": {"audio": "msba-audio-reference@1.0.0", "vision": "msba-vision-reference@1.0.0", "digital": "msba-digital-reference@1.0.0"},
+ "model_hashes": "recorded_per_run_in_adapter_provenance",
+ "projection_mapping": ["structured", "shuffled", "random", "reduced_dimensional", "increased_dimensional"],
+ "control_condition": ["frozen_gateway", "timing_shuffle", "activity_matched_information_destroyed"],
+ "raw_data_path": "DATA/runs.json",
+ "compact_ai_projection_path": "analysis/ai_packet.json",
+ "evidence_path": "none_until_human_review",
+ "production_peripheral_activation": false
+}
diff --git a/research/preregistrations/PREREG-MSBA-E02.json b/research/preregistrations/PREREG-MSBA-E02.json
new file mode 100644
index 0000000000000000000000000000000000000000..a99b40137c4703850b9da678bf940cbd6b57d914
--- /dev/null
+++ b/research/preregistrations/PREREG-MSBA-E02.json
@@ -0,0 +1,29 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-MSBA-E02",
+ "research_question": "RQ-MSBA-E02",
+ "hypothesis": "H-MSBA-E02-A",
+ "protocol_id": "msba_resource_allocation_v1",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": ["task_accuracy", "resource_budget_consumed", "time_to_budget_exhaustion"],
+ "secondary_outcomes": ["utility_captured", "noisy_area_suppression", "reward_formulation_comparison"],
+ "conditions": ["adaptive", "fixed", "random", "frozen_gateway"],
+ "seed_strategy": {"minimum_independent_seeds": 3, "rule": "independent deterministic seeds; equal budget across conditions"},
+ "stopping_rule": "Fixed opportunity stream or resource budget exhaustion, whichever occurs first.",
+ "inclusion_criteria": ["equal resource budget", "preregistration validated before adaptive allocation"],
+ "exclusion_criteria": ["budget mismatch", "adaptive execution before preregistration", "production peripheral activation"],
+ "analysis_plan": {"inference_policy": "adaptive must be compared against both fixed and random controls under the identical budget"},
+ "freeze": {"status": "FROZEN", "immutable_after_first_run": true, "human_review_required": true},
+ "commit_sha": "3b4dceb0c0c0ebd52cd7b1805932da40cbf9efa6",
+ "environment_manifest": "captured_by_execution_manifest",
+ "independent_seed_plan": [101, 102, 103],
+ "gateway_configuration": {"gateway_plasticity": true, "structural_growth": false, "adaptive_allocation": true},
+ "modality_adapter_versions": {"reference": "deterministic-msba-reference@1.0.0"},
+ "model_hashes": "recorded_per_run_in_adapter_provenance",
+ "projection_mapping": ["structured", "shuffled", "random", "reduced_dimensional", "increased_dimensional"],
+ "control_condition": ["fixed", "random", "frozen_gateway"],
+ "raw_data_path": "DATA/runs.json",
+ "compact_ai_projection_path": "analysis/ai_packet.json",
+ "evidence_path": "none_until_human_review",
+ "production_peripheral_activation": false
+}
diff --git a/research/preregistrations/PREREG-MSBA-E03.json b/research/preregistrations/PREREG-MSBA-E03.json
new file mode 100644
index 0000000000000000000000000000000000000000..ca2ea2ae918547b22cdfefb26c9e0d4acb2a897d
--- /dev/null
+++ b/research/preregistrations/PREREG-MSBA-E03.json
@@ -0,0 +1,29 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-MSBA-E03",
+ "research_question": "RQ-MSBA-E03",
+ "hypothesis": "H-MSBA-E03-A",
+ "protocol_id": "msba_visual_roi_v1",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": ["roi_overlap_with_task_relevant_region", "task_accuracy", "visual_energy_units"],
+ "secondary_outcomes": ["correct_per_energy_unit", "projection_treatment_metrics"],
+ "conditions": ["adaptive_roi", "fixed_center_roi", "random_roi", "full_image"],
+ "seed_strategy": {"minimum_independent_seeds": 3, "rule": "independent deterministic seeds with identical relevant-region tasks"},
+ "stopping_rule": "48 visual tasks per condition and seed; stop only on recorded runtime failure.",
+ "inclusion_criteria": ["preregistration validated before adaptive ROI or structural growth", "identical task region schedule across conditions"],
+ "exclusion_criteria": ["hard-coded target region in adaptive condition", "missing control condition", "production peripheral activation"],
+ "analysis_plan": {"inference_policy": "compare adaptive ROI to center, random and full-image controls; report accuracy and energy jointly"},
+ "freeze": {"status": "FROZEN", "immutable_after_first_run": true, "human_review_required": true},
+ "commit_sha": "3b4dceb0c0c0ebd52cd7b1805932da40cbf9efa6",
+ "environment_manifest": "captured_by_execution_manifest",
+ "independent_seed_plan": [101, 102, 103],
+ "gateway_configuration": {"gateway_plasticity": false, "structural_growth": true, "adaptive_allocation": true},
+ "modality_adapter_versions": {"vision": "msba-vision-reference@1.0.0"},
+ "model_hashes": "recorded_per_run_in_adapter_provenance",
+ "projection_mapping": ["structured", "shuffled", "random", "reduced_dimensional", "increased_dimensional"],
+ "control_condition": ["fixed_center_roi", "random_roi", "full_image"],
+ "raw_data_path": "DATA/runs.json",
+ "compact_ai_projection_path": "analysis/ai_packet.json",
+ "evidence_path": "none_until_human_review",
+ "production_peripheral_activation": false
+}
diff --git a/research/preregistrations/PREREG-MSBA-E04.json b/research/preregistrations/PREREG-MSBA-E04.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf0b380a4adad7d197271eafebefda3230e52cb4
--- /dev/null
+++ b/research/preregistrations/PREREG-MSBA-E04.json
@@ -0,0 +1,31 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-MSBA-E04",
+ "research_question": "RQ-MSBA-E04",
+ "hypothesis": "H-MSBA-E04-A",
+ "protocol_id": "msba_digital_integrity_v1",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": ["checksum_mismatches", "admitted_symbol_rate", "exact_integrity_pass"],
+ "secondary_outcomes": ["exact_payload_mismatches", "deterministic_replay_equal"],
+ "conditions": ["throttled", "no_throttling", "deterministic_replay"],
+ "seed_strategy": {"minimum_independent_seeds": 3, "rule": "independent deterministic payload streams with exact replay"},
+ "stopping_rule": "64 digital payloads per condition and seed; any payload mutation is a falsification event.",
+ "inclusion_criteria": ["input and output SHA-256 recorded", "exact payload retained outside SNN"],
+ "exclusion_criteria": ["missing checksum", "non-deterministic replay", "production peripheral activation"],
+ "analysis_plan": {"inference_policy": "integrity requires exactly zero payload/checksum mismatches; throttling may only alter admission rate"},
+ "freeze": {"status": "FROZEN", "immutable_after_first_run": true, "human_review_required": true},
+ "commit_sha": "3b4dceb0c0c0ebd52cd7b1805932da40cbf9efa6",
+ "environment_manifest": "captured_by_execution_manifest",
+ "independent_seed_plan": [101, 102, 103],
+ "gateway_configuration": {"gateway_plasticity": false, "structural_growth": false, "adaptive_allocation": false},
+ "modality_adapter_versions": {"digital": "msba-digital-reference@1.0.0"},
+ "model_hashes": "recorded_per_run_in_adapter_provenance",
+ "projection_mapping": ["structured"],
+ "control_condition": ["no_throttling", "deterministic_replay"],
+ "input_payload_checksums": "DATA/runs.json metrics.checksums[].input_sha256",
+ "output_payload_checksums": "DATA/runs.json metrics.checksums[].output_sha256",
+ "raw_data_path": "DATA/runs.json",
+ "compact_ai_projection_path": "analysis/ai_packet.json",
+ "evidence_path": "none_until_human_review",
+ "production_peripheral_activation": false
+}
diff --git a/research/preregistrations/PREREG-MSBA-E05.json b/research/preregistrations/PREREG-MSBA-E05.json
new file mode 100644
index 0000000000000000000000000000000000000000..717352be3bd5ddd128d2e2a9f4a54fa4d31dc718
--- /dev/null
+++ b/research/preregistrations/PREREG-MSBA-E05.json
@@ -0,0 +1,29 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-MSBA-E05",
+ "research_question": "RQ-MSBA-E05",
+ "hypothesis": "H-MSBA-E05-A",
+ "protocol_id": "msba_modality_compensation_v1",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": ["compensatory_gate_change", "task_recovery", "incremental_energy_cost"],
+ "secondary_outcomes": ["noisy_area_suppression", "timing_shuffle", "activity_matched_information_destroyed", "reward_formulation_comparison"],
+ "conditions": ["adaptive_compensation", "fixed_allocation", "shuffled_utility", "no_compensation", "random_gateway", "frozen_gateway"],
+ "seed_strategy": {"minimum_independent_seeds": 3, "rule": "independent deterministic lesion and control streams"},
+ "stopping_rule": "One preregistered vision lesion sequence per condition and seed; stop only on recorded runtime failure.",
+ "inclusion_criteria": ["preregistration validated before compensation learning", "sensor lesion identical across conditions"],
+ "exclusion_criteria": ["unregistered compensation path", "missing control", "production peripheral activation"],
+ "analysis_plan": {"inference_policy": "compare adaptive recovery against fixed, shuffled and no-compensation controls; separately report energy increment"},
+ "freeze": {"status": "FROZEN", "immutable_after_first_run": true, "human_review_required": true},
+ "commit_sha": "3b4dceb0c0c0ebd52cd7b1805932da40cbf9efa6",
+ "environment_manifest": "captured_by_execution_manifest",
+ "independent_seed_plan": [101, 102, 103],
+ "gateway_configuration": {"gateway_plasticity": true, "structural_growth": false, "adaptive_allocation": true},
+ "modality_adapter_versions": {"vision": "msba-vision-reference@1.0.0", "audio": "msba-audio-reference@1.0.0"},
+ "model_hashes": "recorded_per_run_in_adapter_provenance",
+ "projection_mapping": ["structured", "shuffled", "random", "reduced_dimensional", "increased_dimensional"],
+ "control_condition": ["fixed_allocation", "shuffled_utility", "no_compensation", "random_gateway", "frozen_gateway"],
+ "raw_data_path": "DATA/runs.json",
+ "compact_ai_projection_path": "analysis/ai_packet.json",
+ "evidence_path": "none_until_human_review",
+ "production_peripheral_activation": false
+}
diff --git a/research/preregistrations/PREREG-PERF-001.json b/research/preregistrations/PREREG-PERF-001.json
new file mode 100644
index 0000000000000000000000000000000000000000..565d5803f4c61ed90a52d20f9c09111936f15cef
--- /dev/null
+++ b/research/preregistrations/PREREG-PERF-001.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-PERF-001","research_question":"RQ-PERF-001","hypothesis":"H-PERF-001-A","protocol_id":"subsystem_performance_v1","mode":"EXPLORATORY",
+ "primary_outcomes":["construction_seconds","core_step_seconds","digest_seconds","ticks_per_second"],
+ "secondary_outcomes":["measured_total_seconds"],
+ "conditions":[{"id":"subsystem_profile","role":"comparison"}],
+ "seed_strategy":{"minimum_independent_seeds":10,"rule":"fixed seed set for before/after optimization comparisons"},
+ "stopping_rule":"complete all registered seeds and the full tick budget",
+ "inclusion_criteria":["same hardware/runtime for paired comparisons","source/runtime consistency match"],
+ "exclusion_criteria":["hardware change without separate stratum","background benchmark contamination documented as invalid"],
+ "analysis_plan":{"descriptive_statistics":["phase time fractions","ticks per second","before/after deterministic equivalence"],"inference_policy":"Optimization claims require unchanged deterministic state/output digests and same protocol.","planned_tests":[]},
+ "expected_failure_modes":["measurement noise dominates","optimization changes numerical/event ordering semantics"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot select benchmark samples or alter profiler timing.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-REC-001.json b/research/preregistrations/PREREG-REC-001.json
new file mode 100644
index 0000000000000000000000000000000000000000..b32906f07df561dc9f21d03187ad35884230abc8
--- /dev/null
+++ b/research/preregistrations/PREREG-REC-001.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-REC-001","research_question":"RQ-REC-001","hypothesis":"H-REC-001-A","protocol_id":"recurrence_map_v1","mode":"CONFIRMATORY",
+ "primary_outcomes":["last_response_latency","recurrent_events","propagation_depth","persistence_class"],
+ "secondary_outcomes":["total_spikes","inter_spike_intervals"],
+ "conditions":[{"id":"recurrent_weight_0","role":"control"},{"id":"weight_delay_grid","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"predeclared integer seeds; no replacement after inspection"},
+ "stopping_rule":"execute the full registered weight x delay x seed grid",
+ "inclusion_criteria":["tick contract satisfied","no fatal runtime error","clean source freeze"],
+ "exclusion_criteria":["digest mismatch","stale runtime code","invalid provenance"],
+ "analysis_plan":{"descriptive_statistics":["mean","median","population_std","min","max","condition ratios"],"inference_policy":"No significance claim unless an explicit registered inferential test is added before freeze.","planned_tests":[]},
+ "expected_failure_modes":["all activity decays","all treatments saturate","seed has no effective variation"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot change run state, protocol, DATA or evidence.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-REC-002-CLEAN-R2.json b/research/preregistrations/PREREG-REC-002-CLEAN-R2.json
new file mode 100644
index 0000000000000000000000000000000000000000..8c515eb0a799eecb481c59bff9cb26c69cc94b4f
--- /dev/null
+++ b/research/preregistrations/PREREG-REC-002-CLEAN-R2.json
@@ -0,0 +1,72 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-REC-002-CLEAN-R2",
+ "parent_preregistration_id": "PREREG-REC-002",
+ "research_question": "RQ-REC-002",
+ "hypothesis": "H-REC-002-A",
+ "protocol": "recurrence_scale_clean_r2",
+ "mode": "CONFIRMATORY_CLEAN_TREE_REPLICATION",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "human_review_required": true,
+ "independent_replication": false,
+ "rationale": "The registered recurrence_scale_v1 design is a semantic DIRECT_MATCH for RQ-REC-002, but the latest 20-seed confirmatory run was provenance-blocked because the source tree was dirty. R2 prospectively repeats the same delay intervention on the current green source freeze with fresh seeds. Historical DATA remain unchanged.",
+ "design": {
+ "runner": "src.research.followup_experiments.run_recurrence_scale",
+ "ticks": 256,
+ "recurrent_weight": 100.0,
+ "delays": [1, 2, 4, 8],
+ "control_condition": "loop_delay_1",
+ "treatment_conditions": ["loop_delay_2", "loop_delay_4", "loop_delay_8"],
+ "network_scope": "registered three-neuron recurrent probe"
+ },
+ "seeds": [7501,7502,7503,7504,7505,7506,7507,7508,7509,7510,7511,7512,7513,7514,7515,7516,7517,7518,7519,7520],
+ "seed_semantics": "Fresh seeds are used as deterministic robustness repetitions. They are not assumed to be statistically independent stochastic samples when the registered network construction is seed-insensitive.",
+ "freshness_rule": "Seeds are disjoint from prior REC-002 seeds 101-120 and from recent Stage-1 clean-run seed ranges.",
+ "primary_outcomes": [
+ "last_response_latency",
+ "recurrent_events",
+ "propagation_depth"
+ ],
+ "secondary_outcomes": [
+ "total_spikes",
+ "delivered_synaptic_events",
+ "inter_spike_intervals",
+ "state_digest_before",
+ "state_digest_after"
+ ],
+ "analysis_plan": {
+ "inference_policy": "No p-values or sampling-based confidence intervals are used. Support is decided by preregistered deterministic within-seed contrasts.",
+ "primary_decision_gates": [
+ "All 80 registered runs are present with the complete four-delay grid for every seed.",
+ "Every run completes 256 ticks without runtime error.",
+ "For every seed, each treatment delay 2, 4 and 8 differs from delay 1 in at least one primary outcome.",
+ "For every seed, at least two treatment-delay signatures are distinct from each other in the vector (last_response_latency, recurrent_events, propagation_depth).",
+ "No monotonic direction is required because H-REC-002-A predicts a delay effect, not monotonic improvement."
+ ],
+ "support_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL only if every integrity and primary scientific gate passes.",
+ "failure_status": "NO_PREDEFINED_SUPPORT_DETECTED if execution is valid but one or more preregistered scientific gates fail.",
+ "integrity_failure_status": "NOT_TESTED_INTEGRITY_FAILURE if source freeze, coverage, ticks, provenance or runtime integrity fails."
+ },
+ "integrity_constraints": [
+ "clean source tree before execution",
+ "exactly 80 runs: 20 seeds x 4 delays",
+ "recurrent weight fixed at 100.0",
+ "delays fixed to 1, 2, 4 and 8",
+ "256 ticks per run",
+ "no post-hoc seed or delay replacement",
+ "historical REC-002 artifacts remain unchanged",
+ "automatic EVID promotion forbidden"
+ ],
+ "claim_boundary": "This experiment can support only the scoped claim that loop delay changes persistence/propagation metrics in the registered small deterministic recurrent SNN at recurrent weight 100 and a 256-tick observation window. It does not establish memory, cognition, biological equivalence, scaling, a 5D advantage, monotonic superiority of larger delays, human-reviewed EVID or independent replication.",
+ "gate_protection": {
+ "source_digest_policy": "Do not modify src/, configs/, tests/, research/schemas/ or pyproject.toml in this experiment branch.",
+ "merge_policy": "Merge only after the experiment verifier and the repository pull-request CI are green."
+ },
+ "review": {
+ "human_review_status": "PENDING_AFTER_EXECUTION",
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_promotion_forbidden": true
+ }
+}
diff --git a/research/preregistrations/PREREG-REC-002.json b/research/preregistrations/PREREG-REC-002.json
new file mode 100644
index 0000000000000000000000000000000000000000..4d9a95a4dbb6f38944e1aecbbd493ea776228e87
--- /dev/null
+++ b/research/preregistrations/PREREG-REC-002.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-REC-002","research_question":"RQ-REC-002","hypothesis":"H-REC-002-A","protocol_id":"recurrence_scale_v1","mode":"CONFIRMATORY",
+ "primary_outcomes":["last_response_latency","recurrent_events","propagation_depth"],
+ "secondary_outcomes":["total_spikes","inter_spike_intervals"],
+ "conditions":[{"id":"loop_delay_1","role":"control"},{"id":"loop_delay_2_4_8","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"same seeds for every registered loop-delay condition"},
+ "stopping_rule":"complete all delays and all seeds",
+ "inclusion_criteria":["identical topology and weights apart from loop delay","tick contract satisfied"],
+ "exclusion_criteria":["post-hoc delay additions","invalid provenance","fatal runtime error"],
+ "analysis_plan":{"descriptive_statistics":["latency distribution","depth distribution","condition ratios"],"inference_policy":"Scaling claims are restricted to the registered delay intervention and observation window.","planned_tests":[]},
+ "expected_failure_modes":["delay has no measurable effect","all recurrent activity extinguishes immediately"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot change loop delays, weights or exclusion rules.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-REG-002.json b/research/preregistrations/PREREG-REG-002.json
new file mode 100644
index 0000000000000000000000000000000000000000..015ea48fe2e7dceb9b6710f2f1534ab2220034af
--- /dev/null
+++ b/research/preregistrations/PREREG-REG-002.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-REG-002","research_question":"RQ-REG-002","hypothesis":"H-REG-002-A","protocol_id":"closed_loop_regulation_v1","mode":"CONFIRMATORY",
+ "primary_outcomes":["pressure_phase_spikes","recovery_phase_spikes","recovery_ratio"],
+ "secondary_outcomes":["total_spikes"],
+ "conditions":[{"id":"regulation_off","role":"control"},{"id":"regulation_on","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"same seeds and perturbation schedule in both arms"},
+ "stopping_rule":"complete nominal-pressure-recovery schedule for all seeds and both arms",
+ "inclusion_criteria":["identical perturbation schedule","tick contract satisfied","feedback definition frozen"],
+ "exclusion_criteria":["missing telemetry contract","feedback changed after first run","invalid provenance"],
+ "analysis_plan":{"descriptive_statistics":["phase spike counts","recovery ratio","paired arm differences"],"inference_policy":"A regulation benefit requires preregistered recovery improvement without hiding unknown telemetry.","planned_tests":[]},
+ "expected_failure_modes":["feedback suppresses activity without recovery benefit","no arm difference","unknown telemetry contaminates treatment"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot control telemetry, feedback or perturbation schedule.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-REPL-001.json b/research/preregistrations/PREREG-REPL-001.json
new file mode 100644
index 0000000000000000000000000000000000000000..70c5cbcc68fd1c2d279d5cc926cae1a21b137280
--- /dev/null
+++ b/research/preregistrations/PREREG-REPL-001.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-REPL-001","research_question":"RQ-REPL-001","hypothesis":"H-REPL-001-A","protocol_id":"independent_replication_v1","mode":"REPLICATION",
+ "primary_outcomes":["total_spikes","recurrent_events","propagation_depth","last_response_latency"],
+ "secondary_outcomes":["state_digest_after"],
+ "conditions":[{"id":"recurrence_off","role":"control"},{"id":"recurrence_on","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"at least 20 predeclared initialization seeds; clean process preferred"},
+ "stopping_rule":"complete all registered seeds in both treatment arms",
+ "inclusion_criteria":["clean source freeze","same configuration and stimulus schedule across arms","tick contract satisfied"],
+ "exclusion_criteria":["seed replacement after outcome inspection","runtime/source mismatch","fatal runtime error"],
+ "analysis_plan":{"descriptive_statistics":["paired seed differences","effect direction consistency","condition distributions"],"inference_policy":"Replication means effect-direction and magnitude consistency across independent initializations; deterministic equality alone is not independent replication.","planned_tests":[]},
+ "expected_failure_modes":["no effective seed variation","effect disappears under clean process","large seed-dependent reversals"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot select successful seeds or exclude runs.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/PREREG-S1-REC-CLEAN-R2.json b/research/preregistrations/PREREG-S1-REC-CLEAN-R2.json
new file mode 100644
index 0000000000000000000000000000000000000000..63b697889755c92fe83c032024f4daf2b0d0995a
--- /dev/null
+++ b/research/preregistrations/PREREG-S1-REC-CLEAN-R2.json
@@ -0,0 +1,79 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-S1-REC-CLEAN-R2",
+ "parent_preregistration_id": "PREREG-REC-001",
+ "stage": 1,
+ "bridge_target_stage": 2,
+ "research_question": "RQ-REC-001",
+ "hypothesis": "H-REC-001-A",
+ "protocol": "recurrence_map_clean_r2",
+ "mode": "CONFIRMATORY_CLEAN_TREE_REPLICATION",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "human_review_required": true,
+ "independent_replication": false,
+ "rationale": "Earlier recurrence_map_v1 executions directly matched RQ-REC-001 but their Evidence Readiness was blocked by a dirty source tree. R2 prospectively repeats the complete registered weight x delay intervention on a clean source freeze with fresh seeds and explicit decision gates. Historical DATA remain unchanged.",
+ "design": {
+ "runner": "src.research.followup_experiments.run_recurrence_map",
+ "ticks": 256,
+ "weights": [0.0, 50.0, 75.0, 100.0, 125.0],
+ "delays": [1, 2, 4],
+ "conditions_per_seed": 15,
+ "feedforward_control_conditions": ["w0_d1", "w0_d2", "w0_d4"],
+ "recurrent_treatment_conditions": [
+ "w50_d1", "w50_d2", "w50_d4",
+ "w75_d1", "w75_d2", "w75_d4",
+ "w100_d1", "w100_d2", "w100_d4",
+ "w125_d1", "w125_d2", "w125_d4"
+ ],
+ "control_note": "At recurrent_weight=0 no recurrent edge is created. The three zero-weight delay labels are intentionally retained as internal duplicate controls for protocol compatibility; they are not treated as independent observations."
+ },
+ "seeds": [7301,7302,7303,7304,7305,7306,7307,7308,7309,7310,7311,7312,7313,7314,7315,7316,7317,7318,7319,7320],
+ "seed_semantics": "Fresh seeds provide deterministic repeatability checks under the registered runner. Because this three-neuron construction contains no seed-dependent random topology, identical seed outputs are not interpreted as statistically independent replicates.",
+ "freshness_rule": "Seeds are disjoint from prior REC-001 seeds 42-61 and 101-120 and from the recent Stage-1 temporal/topology seed ranges.",
+ "primary_outcomes": [
+ "persistence_class",
+ "last_response_latency",
+ "recurrent_events",
+ "propagation_depth"
+ ],
+ "secondary_outcomes": [
+ "total_spikes",
+ "delivered_synaptic_events",
+ "first_response_latency",
+ "inter_spike_intervals",
+ "state_digest_before",
+ "state_digest_after"
+ ],
+ "analysis_plan": {
+ "inference_policy": "No p-values or sampling-based confidence intervals are used. The runner is deterministic and the seeds are not assumed to be independent stochastic samples.",
+ "primary_decision_gates": [
+ "Every seed must contain the full 15-condition grid and satisfy the 256-tick contract.",
+ "All zero-recurrence controls must have recurrent_events=0 and persistence_class=immediate_decay.",
+ "For every seed, at least one recurrent condition must be transient.",
+ "For every seed, at least one recurrent condition must be persistent_to_window.",
+ "For every seed, maximum recurrent last_response_latency must exceed maximum zero-recurrence last_response_latency.",
+ "For every seed, at least one nonzero recurrent weight must show a delay-dependent last_response_latency difference across d1/d2/d4.",
+ "For every seed, at least one fixed delay must show a weight-dependent last_response_latency difference across nonzero weights."
+ ],
+ "support_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL only if all design-integrity checks and all primary decision gates pass.",
+ "failure_status": "NO_PREDEFINED_SUPPORT_DETECTED if execution is valid but one or more primary scientific gates fail.",
+ "integrity_failure_status": "NOT_TESTED_INTEGRITY_FAILURE if source freeze, coverage, tick contract, provenance, or runtime integrity fails."
+ },
+ "integrity_constraints": [
+ "clean source tree immediately before execution",
+ "exactly 300 runs: 20 seeds x 15 conditions",
+ "exactly the registered weight x delay grid",
+ "256 ticks requested and executed for every run",
+ "no runtime errors",
+ "historical REC-001 artifacts remain unchanged",
+ "automatic EVID promotion forbidden"
+ ],
+ "claim_boundary": "This experiment tests a small three-neuron SNN under the registered recurrence-weight and delay interventions and can support only the scoped claim that recurrence parameters change persistence/propagation classes in this deterministic operating envelope. It does not establish cognition, memory, biological equivalence, scaling, a 5D advantage, general recurrent-computation superiority, human-reviewed EVID, or independent replication.",
+ "review": {
+ "human_review_status": "PENDING_AFTER_EXECUTION",
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_promotion_forbidden": true
+ }
+}
diff --git a/research/preregistrations/PREREG-S1-TEMP-ORDER-V2.json b/research/preregistrations/PREREG-S1-TEMP-ORDER-V2.json
new file mode 100644
index 0000000000000000000000000000000000000000..81cabe0e3bbcef918840a37d55b96ca0c6789a81
--- /dev/null
+++ b/research/preregistrations/PREREG-S1-TEMP-ORDER-V2.json
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-S1-TEMP-ORDER-V2",
+ "stage": 1,
+ "research_question": "RQ-TEMP-002",
+ "hypothesis": "H-TEMP-002-A",
+ "protocol": "stage1_two_channel_temporal_order_v2",
+ "mode": "CONFIRMATORY_PROTOCOL_CORRECTION",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "human_review_required": true,
+ "independent_replication": false,
+ "rationale": "The historical temporal_order_spiking_v1 protocol stored forward=(0,4) and reverse=(4,0), while its runner used membership testing (tick in schedule). This erases event identity/order and makes forward and reverse causally identical. V2 preserves historical artifacts and prospectively corrects the intervention by using two distinguishable input channels plus an information-destroyed control.",
+ "task": {
+ "network": "six-neuron acyclic dual-channel SNN",
+ "paths": [
+ "input_A -> hidden_A -> output_A",
+ "input_B -> hidden_B -> output_B"
+ ],
+ "neuron_count": 6,
+ "synapse_count": 4,
+ "synaptic_delay_ticks": 1,
+ "order_gap_ticks": 4,
+ "evaluation_ticks": 16,
+ "logical_orders": {
+ "forward": [[0, "A"], [4, "B"]],
+ "reverse": [[0, "B"], [4, "A"]],
+ "simultaneous": [[0, "A"], [0, "B"]]
+ },
+ "decoder": "forward if first output_A spike precedes first output_B spike; reverse if B precedes A; simultaneous if their first output spikes occur on the same tick"
+ },
+ "arms": {
+ "intact": "logical A/B event identity is preserved at the two physical input channels",
+ "identity_destroyed": "both logical A/B events are injected into physical input_A; event count, event times, per-event current and total injected charge are preserved, but channel identity is deliberately removed"
+ },
+ "seed_bound_nuisance_realizations": {
+ "purpose": "prospective paired robustness realizations; not independent external replication",
+ "xor_salt": 5359309,
+ "synaptic_weight_uniform": [100.0, 110.0],
+ "stimulus_current_uniform": [100.0, 110.0],
+ "matching_rule": "for a given seed, the same sampled weight and current are used for all arms and orders",
+ "forbidden_use": "parameters may not be selected from observed task outcomes"
+ },
+ "evaluation": {
+ "seeds": [7101, 7102, 7103, 7104, 7105, 7106, 7107, 7108, 7109, 7110, 7111, 7112, 7113, 7114, 7115, 7116, 7117, 7118, 7119, 7120],
+ "freshness_rule": "seeds are disjoint from TEMP-002 v1 seeds 101-120, Stage-1 topology V2 seeds 2101-2120 and Stage-1 topology V3-R1 seeds 6201-6220",
+ "primary_endpoint": "per-seed balanced order-decoding accuracy across forward and reverse trials",
+ "primary_contrast": "intact minus identity_destroyed, paired by seed",
+ "control_endpoint": "intact simultaneous-control success fraction",
+ "secondary_endpoints": [
+ "first output tick by channel",
+ "output gap error",
+ "total spikes",
+ "delivered synaptic events",
+ "state digests"
+ ]
+ },
+ "analysis": {
+ "alpha": 0.05,
+ "test": "exact two-sided paired sign test on per-seed intact-minus-identity-destroyed accuracy deltas",
+ "effect_interval": "deterministic paired bootstrap 95% CI for median accuracy delta, 5000 resamples",
+ "multiple_testing": "not applicable; exactly one primary inferential contrast",
+ "simultaneous_condition_role": "non-inferential task-adequacy control"
+ },
+ "decision_rule": {
+ "minimum_intact_order_accuracy": 0.95,
+ "maximum_identity_destroyed_order_accuracy": 0.55,
+ "minimum_intact_simultaneous_success_fraction": 0.95,
+ "minimum_paired_accuracy_delta": 0.40,
+ "supported_within_protocol": "all four directional/task gates pass, paired sign-test p < alpha, bootstrap CI excludes zero, and all design-integrity checks pass",
+ "no_predefined_support": "one or more preregistered task/inference gates fail despite intact design integrity",
+ "not_tested": "execution or design-integrity failure"
+ },
+ "integrity_constraints": [
+ "clean source tree before execution",
+ "exactly 20 fresh seed-bound paired realizations",
+ "exactly six runs per seed: two arms x three orders",
+ "six neurons and four synapses in every run",
+ "identical sampled weight/current across all six runs of a seed",
+ "identical event count and total injected charge across arms",
+ "historical temporal_order_spiking_v1 artifacts remain unchanged"
+ ],
+ "claim_boundary": "This Stage-1 task tests whether a small, acyclic six-neuron SNN preserves the temporal order of two distinguishable input channels at designated outputs and whether deliberately destroying channel identity removes that fixed decoding signal. It demonstrates at most scoped technical task function under the registered parameter range. It does not establish learning, memory, cognition, biological equivalence, scaling, 5D superiority, general temporal reasoning, human-reviewed EVID or independent replication.",
+ "review": {
+ "human_review_status": "PENDING_AFTER_EXECUTION",
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_promotion_forbidden": true
+ }
+}
diff --git a/research/preregistrations/PREREG-S1-TOPO-V2.json b/research/preregistrations/PREREG-S1-TOPO-V2.json
new file mode 100644
index 0000000000000000000000000000000000000000..42d33fa811ab65ba88d02df5aab3ca0ce9b77bc9
--- /dev/null
+++ b/research/preregistrations/PREREG-S1-TOPO-V2.json
@@ -0,0 +1,185 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-S1-TOPO-V2",
+ "stage": 1,
+ "research_question": "RQ-SNN-003",
+ "hypothesis": "H-SNN-003-B",
+ "protocol": "topology_propagation_v2",
+ "mode": "CONFIRMATORY_AFTER_INDEPENDENT_ACTIVITY_CALIBRATION",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "design_rationale": "The v1 protocol used three neurons and two explicitly fixed feed-forward synapses, so dimensional coordinates barely affected the causal transfer mechanism. V2 makes topology causal by constructing matched-size directed graphs from geometry while holding node count, edge-count schedule, synaptic weight, delay, stimulus, neuron model and evaluation window fixed.",
+ "conditions": {
+ "1d": [
+ 64,
+ 1,
+ 1,
+ 1,
+ 1
+ ],
+ "2d": [
+ 8,
+ 8,
+ 1,
+ 1,
+ 1
+ ],
+ "3d": [
+ 4,
+ 4,
+ 4,
+ 1,
+ 1
+ ],
+ "5d": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "5d_shuffled": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "random_graph": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ]
+ },
+ "matched_budgets": {
+ "neuron_count": 64,
+ "out_degree_cap": 4,
+ "edge_count_rule": "sum_i min(4, N-i-1), identical for all conditions",
+ "delay_ticks": 1,
+ "stimulus_input_labels": [
+ 0,
+ 1,
+ 2,
+ 3
+ ],
+ "output_labels": [
+ 60,
+ 61,
+ 62,
+ 63
+ ],
+ "stimulus_current": 100,
+ "stimulus_ticks": 1,
+ "evaluation_ticks": 128
+ },
+ "topology_mechanism": {
+ "rank_constraint": "edges are directed from lower canonical label to higher canonical label to keep the small-network propagation test acyclic and prevent recurrence from confounding Stage 1",
+ "geometric_conditions": "for each source label, connect to the nearest future labels under normalized Euclidean distance; seed is used only to break exact distance ties deterministically",
+ "5d_shuffled": "shuffle the 5D coordinate-to-label assignment before geometric edge construction while keeping labels, node count, edge-count schedule, weights, delays and stimulus fixed",
+ "random_graph": "sample future targets uniformly without replacement with the identical out-degree schedule"
+ },
+ "calibration": {
+ "purpose": "activity adequacy only; not a hypothesis test",
+ "seeds": [
+ 1101,
+ 1102,
+ 1103,
+ 1104
+ ],
+ "candidate_synaptic_weights": [
+ 55,
+ 70,
+ 85,
+ 100,
+ 125,
+ 150
+ ],
+ "selection_rule": "choose the lowest candidate weight for which every condition has median active_fraction >= 0.25, median output_reach_fraction >= 0.25, and median delivered_events >= 32",
+ "forbidden_selection_inputs": [
+ "between-condition effect size",
+ "p-value",
+ "hypothesis support",
+ "direction of 5D effect"
+ ]
+ },
+ "evaluation": {
+ "seeds": [
+ 2101,
+ 2102,
+ 2103,
+ 2104,
+ 2105,
+ 2106,
+ 2107,
+ 2108,
+ 2109,
+ 2110,
+ 2111,
+ 2112,
+ 2113,
+ 2114,
+ 2115,
+ 2116,
+ 2117,
+ 2118,
+ 2119,
+ 2120
+ ],
+ "primary_endpoints": [
+ "active_fraction",
+ "first_output_latency_censored"
+ ],
+ "secondary_endpoints": [
+ "output_reach_fraction",
+ "total_spikes",
+ "delivered_events",
+ "last_activity_tick"
+ ],
+ "primary_contrasts": [
+ [
+ "1d",
+ "2d"
+ ],
+ [
+ "2d",
+ "3d"
+ ],
+ [
+ "3d",
+ "5d"
+ ],
+ [
+ "5d",
+ "5d_shuffled"
+ ],
+ [
+ "5d",
+ "random_graph"
+ ]
+ ],
+ "paired_by_seed": true,
+ "test": "exact two-sided paired sign test on non-zero differences",
+ "multiple_testing": "Holm correction across all primary endpoint/contrast tests",
+ "effect_interval": "deterministic paired bootstrap 95% CI for the median difference, 5000 resamples",
+ "alpha": 0.05
+ },
+ "activity_adequacy_gate": {
+ "status_rule": "PASS only when the preregistered calibration selection rule yields a weight; otherwise evaluation is not executed and the hypothesis remains NOT_TESTED"
+ },
+ "decision_rule": {
+ "supported_within_protocol": "activity gate PASS and at least one preregistered primary contrast/end-point has Holm-adjusted p < 0.05 and bootstrap CI excluding zero",
+ "no_detected_difference": "activity gate PASS but the supported_within_protocol rule is not met",
+ "not_tested": "activity gate FAIL or an execution/integrity error occurs"
+ },
+ "claim_boundary": "This protocol tests RQ-SNN-003/H-SNN-003-B for a 64-neuron Stage-1 topology-sensitive propagation regime. It is not evidence for RQ-5D-005/H-5D-005-A, does not test a 5D advantage, does not establish scaling, cognition, biological equivalence or independent replication, and creates DATA rather than EVID.",
+ "local_review": {
+ "verification_command": "python scripts/verify_stage1_topology_v2.py",
+ "raw_data_paths": [
+ "research/experiments/EXP-S1-TOPO-V2-20260918/data/calibration.json",
+ "research/experiments/EXP-S1-TOPO-V2-20260918/data/evaluation.json"
+ ]
+ }
+}
diff --git a/research/preregistrations/PREREG-S1-TOPO-V3-R1-TIME-RESOLVED.json b/research/preregistrations/PREREG-S1-TOPO-V3-R1-TIME-RESOLVED.json
new file mode 100644
index 0000000000000000000000000000000000000000..7153956e330d2c1a1e326a8cf1c9e65a383d0e50
--- /dev/null
+++ b/research/preregistrations/PREREG-S1-TOPO-V3-R1-TIME-RESOLVED.json
@@ -0,0 +1,189 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-S1-TOPO-V3-R1-TIME-RESOLVED",
+ "stage": 1,
+ "research_question": "RQ-SNN-003",
+ "hypothesis": "H-SNN-003-B",
+ "protocol": "topology_propagation_v3_r1_time_resolved_replication",
+ "mode": "CONFIRMATORY_INTERNAL_REPLICATION_AFTER_ANALYSIS_FAMILY_CORRECTION",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "predecessor_experiment": "EXP-S1-TOPO-V3-20260918",
+ "design_rationale": "V3 executed successfully but its runner applied Holm correction separately within each primary endpoint (5 tests + 5 tests), while the preregistration required one Holm family across all 10 time-resolved endpoint/contrast tests. V3 DATA are preserved and not treated as a valid confirmatory analysis. R1 fixes only this analysis-family implementation, uses fresh evaluation seeds, and otherwise preserves the frozen design and endpoint semantics.",
+ "conditions": {
+ "1d": [
+ 64,
+ 1,
+ 1,
+ 1,
+ 1
+ ],
+ "2d": [
+ 8,
+ 8,
+ 1,
+ 1,
+ 1
+ ],
+ "3d": [
+ 4,
+ 4,
+ 4,
+ 1,
+ 1
+ ],
+ "5d": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "5d_shuffled": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "random_graph": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ]
+ },
+ "matched_budgets": {
+ "neuron_count": 64,
+ "out_degree_cap": 4,
+ "edge_count_rule": "sum_i min(4, N-i-1), identical for all conditions",
+ "expected_edge_count": 246,
+ "delay_ticks": 1,
+ "stimulus_input_labels": [
+ 0,
+ 1,
+ 2,
+ 3
+ ],
+ "output_labels": [
+ 60,
+ 61,
+ 62,
+ 63
+ ],
+ "stimulus_current": 100,
+ "stimulus_ticks": 1,
+ "evaluation_ticks": 128,
+ "synaptic_weight": 55
+ },
+ "provenance": {
+ "fixed_weight_source": "EXP-S1-TOPO-V2-20260918 preregistered activity calibration selected 55.0 before V2 evaluation",
+ "internal_replication_only": true,
+ "independent_external_replication": false,
+ "seed_overlap_forbidden_with_v2": true,
+ "analysis_correction": "R1 corrects the primary multiple-testing family to one Holm correction across all 10 preregistered time-resolved tests.",
+ "supersedes_for_confirmatory_interpretation": "EXP-S1-TOPO-V3-20260918"
+ },
+ "evaluation": {
+ "seeds": [
+ 6201,
+ 6202,
+ 6203,
+ 6204,
+ 6205,
+ 6206,
+ 6207,
+ 6208,
+ 6209,
+ 6210,
+ 6211,
+ 6212,
+ 6213,
+ 6214,
+ 6215,
+ 6216,
+ 6217,
+ 6218,
+ 6219,
+ 6220
+ ],
+ "primary_endpoints": [
+ "activation_auc_0_32",
+ "half_activation_latency_censored"
+ ],
+ "replication_endpoint": "first_output_latency_censored",
+ "descriptive_endpoints": [
+ "final_active_fraction",
+ "output_reach_fraction",
+ "total_spikes",
+ "delivered_events",
+ "last_activity_tick"
+ ],
+ "primary_contrasts": [
+ [
+ "1d",
+ "2d"
+ ],
+ [
+ "2d",
+ "3d"
+ ],
+ [
+ "3d",
+ "5d"
+ ],
+ [
+ "5d",
+ "5d_shuffled"
+ ],
+ [
+ "5d",
+ "random_graph"
+ ]
+ ],
+ "low_dimensional_ceiling_resolution_contrasts": [
+ [
+ "1d",
+ "2d"
+ ],
+ [
+ "2d",
+ "3d"
+ ]
+ ],
+ "paired_by_seed": true,
+ "primary_test": "exact two-sided paired sign test on non-zero differences",
+ "primary_multiple_testing": "Single Holm correction across all 10 primary time-resolved endpoint/contrast tests",
+ "replication_test": "exact two-sided paired sign test on non-zero first-output-latency differences",
+ "replication_multiple_testing": "Holm correction across the 5 replication contrasts",
+ "effect_interval": "deterministic paired bootstrap 95% CI for the median difference, 5000 resamples",
+ "alpha": 0.05,
+ "auc_window_ticks": 32,
+ "half_activation_threshold": 0.5,
+ "censor_sentinel": 129,
+ "endpoint_definitions": {
+ "activation_auc_0_32": "Sum over ticks 0..31 inclusive of cumulative_unique_active_neurons(t)/64, evaluated after each network.step(). This is a discrete cumulative-activation area measure, not a continuous-time integral.",
+ "half_activation_latency_censored": "First zero-based tick at which cumulative_unique_active_neurons(t)/64 >= 0.5; if never reached within the 128-tick evaluation window, encode 129.",
+ "first_output_latency_censored": "First zero-based tick with any spike in output labels 60..63; if absent within 128 ticks, encode 129."
+ }
+ },
+ "decision_rule": {
+ "ceiling_resolution_supported": "At least one preregistered low-dimensional contrast (1d->2d or 2d->3d) is significant after Holm correction for activation_auc_0_32 or half_activation_latency_censored and its bootstrap CI excludes zero.",
+ "replication_supported": "All five first_output_latency_censored contrasts have the same negative right-minus-left direction observed in V2, Holm-adjusted p < 0.05, and bootstrap CI excluding zero.",
+ "supported_within_protocol": "design integrity PASS AND the single-family Holm analysis across all 10 primary tests yields ceiling_resolution_supported AND replication_supported.",
+ "partial_support": "design integrity PASS and exactly one of ceiling_resolution_supported or replication_supported is true.",
+ "no_detected_support": "design integrity PASS and neither criterion is met.",
+ "not_tested": "execution or integrity failure occurs."
+ },
+ "interpretation_boundary": "This R1 protocol corrects the V3 primary multiple-testing implementation and is an internal preregistered Stage-1 replication/robustness test of RQ-SNN-003/H-SNN-003-B. It does not constitute independent external replication, does not test a 5D advantage, does not establish scaling, cognition or biological equivalence, and creates DATA rather than EVID.",
+ "local_review": {
+ "verification_command": "python scripts/verify_stage1_topology_v3_r1.py",
+ "raw_data_paths": [
+ "research/experiments/EXP-S1-TOPO-V3-R1-20260918/data/evaluation.json"
+ ],
+ "statistics_path": "research/experiments/EXP-S1-TOPO-V3-R1-20260918/analysis/statistics.json",
+ "report_path": "research/experiments/EXP-S1-TOPO-V3-R1-20260918/report.md"
+ }
+}
diff --git a/research/preregistrations/PREREG-S1-TOPO-V3-TIME-RESOLVED.json b/research/preregistrations/PREREG-S1-TOPO-V3-TIME-RESOLVED.json
new file mode 100644
index 0000000000000000000000000000000000000000..d7e3cfed115402c577a54f737a3a1291b7dac786
--- /dev/null
+++ b/research/preregistrations/PREREG-S1-TOPO-V3-TIME-RESOLVED.json
@@ -0,0 +1,187 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-S1-TOPO-V3-TIME-RESOLVED",
+ "stage": 1,
+ "research_question": "RQ-SNN-003",
+ "hypothesis": "H-SNN-003-B",
+ "protocol": "topology_propagation_v3_time_resolved_replication",
+ "mode": "CONFIRMATORY_INTERNAL_REPLICATION_WITH_PROSPECTIVE_TIME_RESOLVED_ENDPOINTS",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "predecessor_experiment": "EXP-S1-TOPO-V2-20260918",
+ "design_rationale": "V2 established a preregistered topology-sensitive DATA result but final active_fraction saturated at 1.0 for 1d/2d/3d. V3 preserves the V2 operating point and matched graph budgets while prospectively measuring time-resolved propagation so low-dimensional conditions can be compared before terminal saturation. No parameter is tuned on V3 evaluation DATA.",
+ "conditions": {
+ "1d": [
+ 64,
+ 1,
+ 1,
+ 1,
+ 1
+ ],
+ "2d": [
+ 8,
+ 8,
+ 1,
+ 1,
+ 1
+ ],
+ "3d": [
+ 4,
+ 4,
+ 4,
+ 1,
+ 1
+ ],
+ "5d": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "5d_shuffled": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "random_graph": [
+ 4,
+ 2,
+ 2,
+ 2,
+ 2
+ ]
+ },
+ "matched_budgets": {
+ "neuron_count": 64,
+ "out_degree_cap": 4,
+ "edge_count_rule": "sum_i min(4, N-i-1), identical for all conditions",
+ "expected_edge_count": 246,
+ "delay_ticks": 1,
+ "stimulus_input_labels": [
+ 0,
+ 1,
+ 2,
+ 3
+ ],
+ "output_labels": [
+ 60,
+ 61,
+ 62,
+ 63
+ ],
+ "stimulus_current": 100,
+ "stimulus_ticks": 1,
+ "evaluation_ticks": 128,
+ "synaptic_weight": 55
+ },
+ "provenance": {
+ "fixed_weight_source": "EXP-S1-TOPO-V2-20260918 preregistered activity calibration selected 55.0 before V2 evaluation",
+ "internal_replication_only": true,
+ "independent_external_replication": false,
+ "seed_overlap_forbidden_with_v2": true
+ },
+ "evaluation": {
+ "seeds": [
+ 6101,
+ 6102,
+ 6103,
+ 6104,
+ 6105,
+ 6106,
+ 6107,
+ 6108,
+ 6109,
+ 6110,
+ 6111,
+ 6112,
+ 6113,
+ 6114,
+ 6115,
+ 6116,
+ 6117,
+ 6118,
+ 6119,
+ 6120
+ ],
+ "primary_endpoints": [
+ "activation_auc_0_32",
+ "half_activation_latency_censored"
+ ],
+ "replication_endpoint": "first_output_latency_censored",
+ "descriptive_endpoints": [
+ "final_active_fraction",
+ "output_reach_fraction",
+ "total_spikes",
+ "delivered_events",
+ "last_activity_tick"
+ ],
+ "primary_contrasts": [
+ [
+ "1d",
+ "2d"
+ ],
+ [
+ "2d",
+ "3d"
+ ],
+ [
+ "3d",
+ "5d"
+ ],
+ [
+ "5d",
+ "5d_shuffled"
+ ],
+ [
+ "5d",
+ "random_graph"
+ ]
+ ],
+ "low_dimensional_ceiling_resolution_contrasts": [
+ [
+ "1d",
+ "2d"
+ ],
+ [
+ "2d",
+ "3d"
+ ]
+ ],
+ "paired_by_seed": true,
+ "primary_test": "exact two-sided paired sign test on non-zero differences",
+ "primary_multiple_testing": "Holm correction across the 10 time-resolved endpoint/contrast tests",
+ "replication_test": "exact two-sided paired sign test on non-zero first-output-latency differences",
+ "replication_multiple_testing": "Holm correction across the 5 replication contrasts",
+ "effect_interval": "deterministic paired bootstrap 95% CI for the median difference, 5000 resamples",
+ "alpha": 0.05,
+ "auc_window_ticks": 32,
+ "half_activation_threshold": 0.5,
+ "censor_sentinel": 129,
+ "endpoint_definitions": {
+ "activation_auc_0_32": "Sum over ticks 0..31 inclusive of cumulative_unique_active_neurons(t)/64, evaluated after each network.step(). This is a discrete cumulative-activation area measure, not a continuous-time integral.",
+ "half_activation_latency_censored": "First zero-based tick at which cumulative_unique_active_neurons(t)/64 >= 0.5; if never reached within the 128-tick evaluation window, encode 129.",
+ "first_output_latency_censored": "First zero-based tick with any spike in output labels 60..63; if absent within 128 ticks, encode 129."
+ }
+ },
+ "decision_rule": {
+ "ceiling_resolution_supported": "At least one preregistered low-dimensional contrast (1d->2d or 2d->3d) is significant after Holm correction for activation_auc_0_32 or half_activation_latency_censored and its bootstrap CI excludes zero.",
+ "replication_supported": "All five first_output_latency_censored contrasts have the same negative right-minus-left direction observed in V2, Holm-adjusted p < 0.05, and bootstrap CI excluding zero.",
+ "supported_within_protocol": "design integrity PASS AND ceiling_resolution_supported AND replication_supported.",
+ "partial_support": "design integrity PASS and exactly one of ceiling_resolution_supported or replication_supported is true.",
+ "no_detected_support": "design integrity PASS and neither criterion is met.",
+ "not_tested": "execution or integrity failure occurs."
+ },
+ "interpretation_boundary": "This protocol is an internal preregistered Stage-1 replication/robustness test of RQ-SNN-003/H-SNN-003-B. It does not constitute independent external replication, does not test a 5D advantage, does not establish scaling, cognition or biological equivalence, and creates DATA rather than EVID.",
+ "local_review": {
+ "verification_command": "python scripts/verify_stage1_topology_v3.py",
+ "raw_data_paths": [
+ "research/experiments/EXP-S1-TOPO-V3-20260918/data/evaluation.json"
+ ],
+ "statistics_path": "research/experiments/EXP-S1-TOPO-V3-20260918/analysis/statistics.json",
+ "report_path": "research/experiments/EXP-S1-TOPO-V3-20260918/report.md"
+ }
+}
diff --git a/research/preregistrations/PREREG-SNN-001.json b/research/preregistrations/PREREG-SNN-001.json
new file mode 100644
index 0000000000000000000000000000000000000000..cea9c40a48eb979bc888ae17ec23121371c55b26
--- /dev/null
+++ b/research/preregistrations/PREREG-SNN-001.json
@@ -0,0 +1,92 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-SNN-001",
+ "research_question": "RQ-SNN-001",
+ "hypothesis": "H-SNN-001-A",
+ "protocol_id": "sustained_activity_stability_v1",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": [
+ "post_burn_in_spike_cv",
+ "post_burn_in_spike_relative_drift",
+ "finite_state",
+ "topology_unchanged"
+ ],
+ "secondary_outcomes": [
+ "spikes_per_tick",
+ "window_spike_counts",
+ "window_mean_v",
+ "neuron_v_min",
+ "neuron_v_max",
+ "weight_min",
+ "weight_max"
+ ],
+ "conditions": [
+ {
+ "id": "no_input_control",
+ "role": "negative_control",
+ "description": "Identical network and seed without external current injection."
+ },
+ {
+ "id": "tonic_drive",
+ "role": "stability_treatment",
+ "description": "Constant current 50.0 injected into the registered input neuron every tick."
+ }
+ ],
+ "seed_strategy": {
+ "minimum_independent_seeds": 10,
+ "seeds": [42, 43, 44, 45, 46, 47, 48, 49, 50, 51],
+ "rule": "Same frozen configuration, independent deterministic seeds, both conditions for every seed."
+ },
+ "stopping_rule": "Complete 100000 ticks for both conditions and all 10 seeds; stop only on runtime failure, non-finite state, or contract violation and record the failure.",
+ "inclusion_criteria": [
+ "100000 ticks executed per run",
+ "10000 tick burn-in excluded from primary stability statistics",
+ "1000 tick non-overlapping observation windows",
+ "complete spike-per-tick and window traces retained",
+ "source/runtime consistency match",
+ "clean frozen code and configuration provenance"
+ ],
+ "exclusion_criteria": [
+ "runtime error or incomplete tick budget",
+ "NaN or infinite membrane/weight value",
+ "unexpected neuron or synapse count change",
+ "missing raw trace or seed/condition record",
+ "dirty source tree or unreviewed protocol amendment"
+ ],
+ "analysis_plan": {
+ "stability_criteria": {
+ "tonic_drive_mean_spikes_per_window": "> 0",
+ "tonic_drive_post_burn_in_spike_cv": "<= 0.25",
+ "tonic_drive_post_burn_in_spike_relative_drift": "<= 0.25",
+ "all_runs_finite_state": true,
+ "all_runs_topology_unchanged": true
+ },
+ "descriptive_statistics": [
+ "per-tick spike count trace",
+ "per-window spike count mean and variation",
+ "membrane potential window means",
+ "final membrane and synaptic weight bounds",
+ "control-versus-tonic-drive condition summary across seeds"
+ ],
+ "inference_policy": "A confirmatory pass requires every registered run to satisfy execution, finiteness and topology criteria and the tonic-drive condition to satisfy all preregistered stability thresholds. A failed criterion is a negative result, not an omitted run.",
+ "planned_tests": []
+ },
+ "expected_failure_modes": [
+ "quiescent tonic-drive dynamics",
+ "rate drift or exploding/vanishing activity",
+ "non-finite membrane or weight values",
+ "unexpected structural mutation",
+ "runtime interruption before 100000 ticks"
+ ],
+ "ai_treatment": {
+ "role": "interpretation_only",
+ "authority_boundary": "AI may summarize completed raw results but cannot select seeds, alter thresholds, exclude runs, or promote evidence.",
+ "prompt_digest_required": true
+ },
+ "freeze": {
+ "immutable_after_first_run": true,
+ "human_review_required": true,
+ "status": "FROZEN",
+ "amendment_id": null
+ }
+}
\ No newline at end of file
diff --git a/research/preregistrations/PREREG-SNN-006.json b/research/preregistrations/PREREG-SNN-006.json
new file mode 100644
index 0000000000000000000000000000000000000000..141fc71512c52fce9574b498a3ab25376e8b1cd8
--- /dev/null
+++ b/research/preregistrations/PREREG-SNN-006.json
@@ -0,0 +1,117 @@
+{
+ "schema_version": "1.0",
+ "preregistration_id": "PREREG-SNN-006",
+ "research_question": "RQ-SNN-006",
+ "hypothesis": "H-SNN-006-A",
+ "protocol_id": "sustained_activity_stability_v2",
+ "mode": "CONFIRMATORY",
+ "primary_outcomes": [
+ "post_burn_in_spike_cv",
+ "post_burn_in_spike_relative_drift",
+ "finite_state",
+ "topology_unchanged",
+ "robustness_stability_pass",
+ "realization_unique_across_registered_seeds"
+ ],
+ "secondary_outcomes": [
+ "post_burn_in_mean_spikes",
+ "active_window_fraction",
+ "neuron_v_min",
+ "neuron_v_max",
+ "weight_min",
+ "weight_max",
+ "realization_weight_scale",
+ "realization_drive_scale",
+ "realization_digest"
+ ],
+ "conditions": [
+ {
+ "id": "no_input_control",
+ "role": "paired_negative_control",
+ "description": "No external current; paired with the same seed-bound weight realization as treatment."
+ },
+ {
+ "id": "tonic_drive",
+ "role": "paired_stability_treatment",
+ "description": "Base current 50.0 scaled by the registered seed-bound drive realization."
+ }
+ ],
+ "seed_strategy": {
+ "minimum_independent_seeds": 10,
+ "seeds": [
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110
+ ],
+ "rule": "Legacy registry field minimum_independent_seeds denotes ten distinct deterministic seed-bound parameter realizations, not independent biological samples.",
+ "parameterization": {
+ "generator": "random.Random(seed ^ 0x5A81B17)",
+ "weight_scale": "uniform(0.95, 1.05)",
+ "drive_scale": "uniform(0.95, 1.05)",
+ "pairing": "control and tonic treatment share one seed-bound realization",
+ "uniqueness_requirement": "all ten parameter digests unique before execution"
+ }
+ },
+ "stopping_rule": "Complete 100000 ticks for both conditions and all ten registered realizations; retain any runtime, numerical, topology or contract failure.",
+ "inclusion_criteria": [
+ "100000 ticks executed per run",
+ "10000 tick burn-in excluded from primary stability statistics",
+ "1000 tick non-overlapping observation windows",
+ "exactly ten unique seed-bound parameter digests",
+ "paired control and tonic-drive run for every registered seed",
+ "source/runtime consistency match",
+ "clean frozen code and configuration provenance"
+ ],
+ "exclusion_criteria": [
+ "runtime error or incomplete tick budget",
+ "NaN or infinite membrane/weight value",
+ "unexpected neuron or synapse count change",
+ "missing raw trace or seed/condition record",
+ "duplicate parameter digest across registered seeds",
+ "dirty source tree or unreviewed protocol amendment"
+ ],
+ "analysis_plan": {
+ "stability_criteria": {
+ "tonic_drive_mean_spikes_per_window": "> 0",
+ "tonic_drive_post_burn_in_spike_cv": "<= 0.25",
+ "tonic_drive_post_burn_in_spike_relative_drift": "<= 0.25",
+ "all_runs_finite_state": true,
+ "all_runs_topology_unchanged": true,
+ "all_registered_realizations_unique": true
+ },
+ "descriptive_statistics": [
+ "per-realization tonic spike-window mean, CV and drift",
+ "paired control/treatment numerical stability",
+ "realization parameter ranges and digests",
+ "membrane and synaptic bounds"
+ ],
+ "inference_policy": "Support requires every registered run to satisfy execution, finiteness and topology criteria, all ten parameter digests to be unique, and every tonic-drive realization to satisfy activity, CV and drift thresholds. Failed realizations are retained. No biological or independent-sampling generalization is permitted.",
+ "planned_tests": []
+ },
+ "expected_failure_modes": [
+ "duplicate or ineffective seed parameterization",
+ "quiescent tonic-drive realization",
+ "rate drift or unstable activity under jitter",
+ "non-finite membrane or weight values",
+ "unexpected structural mutation",
+ "runtime interruption before 100000 ticks"
+ ],
+ "ai_treatment": {
+ "role": "interpretation_only",
+ "authority_boundary": "AI cannot alter seeds, jitter range, thresholds, exclusions or evidence status.",
+ "prompt_digest_required": true
+ },
+ "freeze": {
+ "immutable_after_first_run": true,
+ "human_review_required": true,
+ "status": "FROZEN",
+ "amendment_id": null
+ }
+}
diff --git a/research/preregistrations/PREREG-SNN004-STDP-ASYM-R2.json b/research/preregistrations/PREREG-SNN004-STDP-ASYM-R2.json
new file mode 100644
index 0000000000000000000000000000000000000000..faed35bb3968f48323d973d13a77854980a57a47
--- /dev/null
+++ b/research/preregistrations/PREREG-SNN004-STDP-ASYM-R2.json
@@ -0,0 +1,71 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-SNN004-STDP-ASYM-R2",
+ "research_question": "RQ-SNN-004",
+ "hypothesis": "H-SNN-004-A",
+ "claim_id": "CLAIM-SNN-001",
+ "protocol": "snn004_stdp_pair_asymmetry_clean_r2",
+ "mode": "CONFIRMATORY_CLEAN_TREE_COMPONENT_EXPERIMENT",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "human_review_required": true,
+ "independent_replication": false,
+ "rationale": "Earlier RQ-SNN-004 runs used the productive reward-STDP path on a dirty source tree and lacked an explicit direction-matched control. This prospective clean-tree experiment isolates the registered pair-based STDP rule and tests signed Pre/Post timing asymmetry directly without making a functional-learning claim.",
+ "design": {
+ "runner": "src.research.stdp_pair_timing.run_pair_timing_protocol",
+ "initial_weight": 0.5,
+ "delta_t_ms": [-50, -20, -10, -5, -1, 0, 1, 5, 10, 20, 50],
+ "repeated_evaluations_per_condition": 10,
+ "independent_runs_per_condition": 0,
+ "stdp_parameters": {
+ "a_plus": 0.1,
+ "a_minus": 0.12,
+ "tau_plus": 20.0,
+ "tau_minus": 20.0
+ },
+ "timing_semantics": {
+ "positive_delta_t": "pre before post; expected potentiation",
+ "negative_delta_t": "post before pre; expected depression",
+ "zero_delta_t": "simultaneous timing control; expected no weight change"
+ }
+ },
+ "primary_outcomes": [
+ "mean_delta_weight_by_delta_t",
+ "mean_ltp",
+ "mean_ltd",
+ "mirror_pair_magnitude_asymmetry"
+ ],
+ "analysis_plan": {
+ "inference_policy": "Deterministic component experiment. Repeated evaluations verify identity only and are not treated as statistically independent samples; no p-values or sampling confidence intervals are used.",
+ "primary_decision_gates": [
+ "All 11 timing conditions are present with 10 repeated evaluations each.",
+ "Every positive delta_t condition has mean_delta_weight > 0.",
+ "Every negative delta_t condition has mean_delta_weight < 0.",
+ "delta_t = 0 has mean_delta_weight = 0.",
+ "Within every mirrored |delta_t| pair, absolute depression magnitude is greater than potentiation magnitude, reflecting the frozen a_minus > a_plus parameterization.",
+ "All repeated evaluations within a timing condition are numerically identical.",
+ "Mean LTP is positive, mean LTD is negative, and absolute mean LTD exceeds mean LTP."
+ ],
+ "support_status": "SUPPORTED_WITHIN_PREREGISTERED_PROTOCOL only if all integrity and primary scientific gates pass.",
+ "failure_status": "NO_PREDEFINED_SUPPORT_DETECTED if execution is valid but one or more scientific gates fail.",
+ "integrity_failure_status": "NOT_TESTED_INTEGRITY_FAILURE if source freeze, protocol constants, artifact coverage or runtime integrity fails."
+ },
+ "integrity_constraints": [
+ "clean source tree immediately before execution",
+ "no modification of src/, configs/, tests/, research/schemas/ or pyproject.toml in the experiment branch",
+ "exact registered timing grid and STDP parameterization",
+ "historical STDP artifacts remain unchanged",
+ "automatic EVID promotion forbidden"
+ ],
+ "claim_boundary": "This experiment can support only the scoped implementation-level claim that the registered pair-based STDP rule produces signed and asymmetric weight changes across the fixed Pre/Post timing curve. It does not establish useful learning, task-performance improvement, network-level memory, biological equivalence, scaling, a 5D advantage, human-reviewed EVID or independent replication.",
+ "gate_protection": {
+ "source_digest_policy": "Do not modify src/, configs/, tests/, research/schemas/ or pyproject.toml.",
+ "merge_policy": "Merge only after the experiment verifier and full pull-request CI are green."
+ },
+ "review": {
+ "human_review_status": "PENDING_AFTER_EXECUTION",
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_promotion_forbidden": true
+ }
+}
diff --git a/research/preregistrations/PREREG-SNN005-HOLDOUT-R2.json b/research/preregistrations/PREREG-SNN005-HOLDOUT-R2.json
new file mode 100644
index 0000000000000000000000000000000000000000..41fbebadaba8d42ffc40d7c44a5f3afb7527f868
--- /dev/null
+++ b/research/preregistrations/PREREG-SNN005-HOLDOUT-R2.json
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "preregistration_id": "PREREG-SNN005-HOLDOUT-R2",
+ "research_question": "RQ-SNN-005",
+ "hypothesis": "H-SNN-005-A",
+ "protocol": "snn005_native_association_holdout_r2",
+ "mode": "CONFIRMATORY_INTERNAL_SIMULATION",
+ "execution_authorized": true,
+ "scientific_evidence": false,
+ "automatic_evidence_promotion": false,
+ "human_review_required": true,
+ "independent_replication": false,
+ "rationale": "RQ-SNN-005 asks whether STDP improves functional learning performance relative to no STDP. Earlier exploratory runs were incomplete or provenance-limited. This prospective run uses the existing native association holdout task with matched learning-off, sham-replay, post-acquisition weight-reset and weight-shuffle controls, fresh held-out test episodes, and frozen weights during evaluation.",
+ "design": {
+ "runner": "src.research.empirical_evaluation.run_association_generalization",
+ "conditions": [
+ "learning_on",
+ "learning_off",
+ "sham_replay",
+ "weight_reset",
+ "weight_shuffle"
+ ],
+ "seeds": [7601,7602,7603,7604,7605,7606,7607,7608,7609,7610,7611,7612,7613,7614,7615,7616],
+ "seed_count": 16,
+ "runs_per_seed": 5,
+ "expected_run_count": 80,
+ "test_episodes_per_run": 40,
+ "evaluation_ticks_per_episode": 12,
+ "primary_metric": "test_accuracy",
+ "primary_reference": "learning_on",
+ "primary_control": "learning_off",
+ "secondary_controls": [
+ "sham_replay",
+ "weight_reset",
+ "weight_shuffle"
+ ],
+ "test_constraints": [
+ "teacher absent during every test episode",
+ "LearningEngine detached during every test episode",
+ "fresh network per test episode",
+ "weights frozen throughout holdout evaluation",
+ "same held-out test-input digest across all five arms within each seed"
+ ]
+ },
+ "analysis_plan": {
+ "unit": "paired simulation seed",
+ "primary_contrast": "learning_on minus learning_off test_accuracy",
+ "robustness_family": [
+ "learning_on minus sham_replay",
+ "learning_on minus weight_reset",
+ "learning_on minus weight_shuffle"
+ ],
+ "test": "exact two-sided paired sign-flip over 16 seed-level accuracy differences",
+ "interval": "2000-resample paired percentile bootstrap 95% CI using the existing deterministic paired_summary implementation",
+ "multiplicity": "Holm adjustment across the four declared learning_on-control comparisons",
+ "alpha": 0.05,
+ "minimum_primary_mean_difference": 0.10,
+ "decision_rule": [
+ "all 80 runs and all 16x5 seed-condition cells must be present",
+ "all runs must contain exactly 40 holdout episodes and 12 ticks per episode",
+ "test teacher must be absent and test LearningEngine detached in every run",
+ "weights must be unchanged across every holdout evaluation",
+ "within each seed all five conditions must share the same test_input_digest",
+ "learning_on minus learning_off mean test_accuracy difference must be >= 0.10",
+ "the primary bootstrap 95% CI lower bound must be > 0",
+ "the Holm-adjusted primary sign-flip p-value must be < 0.05",
+ "learning_on mean accuracy must exceed each sham/reset/shuffle control mean accuracy",
+ "each robustness contrast must have positive mean difference and Holm-adjusted p < 0.05"
+ ]
+ },
+ "freshness_rule": "Seeds 7601-7616 are disjoint from the earlier native association seeds 20001-20010 and recent Stage-1/REC clean-run seed ranges.",
+ "integrity_constraints": [
+ "execute in the isolated experiment worktree",
+ "primary checkout remains clean before and after execution",
+ "scientific source digest must equal the currently verified digest before execution",
+ "no modification of src/, configs/, tests/, research/schemas/ or pyproject.toml",
+ "no outcome-based retries, seed replacement or parameter retuning",
+ "all scientific negatives are retained"
+ ],
+ "claim_boundary": "A supported result would show only that the registered STDP-enabled synthetic association protocol achieves higher held-out classification accuracy than matched no-learning/sham/reset/shuffle controls within this simulator and task. It does not establish general intelligence, open-world learning, biological equivalence, human-like memory, scaling, a 5D advantage, external independent replication or human-reviewed EVID.",
+ "review": {
+ "human_review_status": "PENDING_AFTER_EXECUTION",
+ "ai_review_does_not_satisfy_human_gate": true,
+ "automatic_promotion_forbidden": true
+ }
+}
diff --git a/research/preregistrations/PREREG-TEMP-002.json b/research/preregistrations/PREREG-TEMP-002.json
new file mode 100644
index 0000000000000000000000000000000000000000..62dc60fb8cda9452892fc17b8990871040967451
--- /dev/null
+++ b/research/preregistrations/PREREG-TEMP-002.json
@@ -0,0 +1,14 @@
+{
+ "schema_version":"1.0","preregistration_id":"PREREG-TEMP-002","research_question":"RQ-TEMP-002","hypothesis":"H-TEMP-002-A","protocol_id":"temporal_order_spiking_v1","mode":"CONFIRMATORY",
+ "primary_outcomes":["output_spike_count","total_spikes","sequence_digest"],
+ "secondary_outcomes":["condition-wise spike timing"],
+ "conditions":[{"id":"simultaneous","role":"control"},{"id":"reverse","role":"comparison"},{"id":"forward","role":"treatment"}],
+ "seed_strategy":{"minimum_independent_seeds":20,"rule":"same seed set and observation window for all temporal orders"},
+ "stopping_rule":"complete all registered temporal-order conditions for all seeds",
+ "inclusion_criteria":["spike-carrying input schedule","tick contract satisfied","identical amplitudes across conditions"],
+ "exclusion_criteria":["condition-specific amplitude changes","post-hoc schedule changes","invalid provenance"],
+ "analysis_plan":{"descriptive_statistics":["condition spike distributions","sequence digest consistency","paired condition differences"],"inference_policy":"Temporal-order use requires a condition-dependent response not explainable by stimulus count alone.","planned_tests":[]},
+ "expected_failure_modes":["all schedules produce identical response","network is insensitive to order"],
+ "ai_treatment":{"role":"interpretation_only","authority_boundary":"AI cannot change temporal schedules or stimuli.","prompt_digest_required":true},
+ "freeze":{"immutable_after_first_run":true,"human_review_required":true,"status":"FROZEN","amendment_id":null}
+}
diff --git a/research/preregistrations/amendments/EXP-S6-SEM-CL-002-A1.json b/research/preregistrations/amendments/EXP-S6-SEM-CL-002-A1.json
new file mode 100644
index 0000000000000000000000000000000000000000..7869d514ea7c18dc33775cd28f044d0a55b69f55
--- /dev/null
+++ b/research/preregistrations/amendments/EXP-S6-SEM-CL-002-A1.json
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "experiment_id": "EXP-S6-SEM-CL-002",
+ "amendment_id": "EXP-S6-SEM-CL-002-A1",
+ "status": "preregistered_pre_execution_amendment",
+ "created_date": "2026-09-15",
+ "empirical_run_before_amendment": false,
+ "changes_primary_hypothesis": false,
+ "changes_primary_endpoints": false,
+ "changes_seeds": false,
+ "changes_effect_thresholds": false,
+ "semantic_prototype": {
+ "implementation": "src.memory.semantic.SemanticMemory",
+ "min_episode_support": 8,
+ "prototype_support": 0.30,
+ "match_threshold": 0.25,
+ "sensor_id": "split-mnist-label-