Examples Results¶
Generated: 2026-05-16
examples/neuro/run_closed_loop_mock_bci.py¶
======================================================================
Closed-Loop BCI with MockSignalStream Demonstration
======================================================================
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
--- Setting Up Signal Stream ---
[OK] MockSignalStream connected.
Status: {'connected': True, 'stream_type': 'mock', 'channels': ['Fz', 'Cz', 'Pz', 'Oz'], 'sampling_rate': 250.0, 'window_index': 0, 'window_seconds': 1.0}
--- Setting Up BCI Adapter ---
[OK] BCIAdapter created and stream attached.
Pipeline status: {'stream_attached': True, 'preprocessing': True, 'feature_extraction': True, 'neural_state_encoding': True}
--- Creating CIA System ---
[OK] CIA system created with BCI-compatible attributes.
--- Running 3 Closed-Loop Steps ---
──────────────────────────────────────────────────
Step 1: A red ball rolled behind a screen and reappeared.
──────────────────────────────────────────────────
Neural State:
Attention Proxy: 0.3170
Workload Proxy: 0.6829
Fatigue Proxy: 0.5237
Prediction Error: 0.9819
Confidence: 0.6000
Control Signal:
Attention Bias: 0.0000
Workspace Priority Bias: 0.0000
Uncertainty Adj: 0.0000
Valuation Adj: 0.0596
Prediction Sensitivity: 0.9398
Conditioning Applied: True
Stream Status: {'connected': True, 'stream_type': 'mock', 'channels': ['Fz', 'Cz', 'Pz', 'Oz'], 'sampling_rate': 250.0, 'window_index': 0, 'window_seconds': 1.0}
Pipeline Status: {'stream_attached': True, 'preprocessing': True, 'feature_extraction': True, 'neural_state_encoding': True}
──────────────────────────────────────────────────
Step 2: The agent noticed a sudden change in the environment.
──────────────────────────────────────────────────
Neural State:
Attention Proxy: 0.3208
Workload Proxy: 0.6792
Fatigue Proxy: 0.4824
Prediction Error: 0.9768
Confidence: 0.6000
Control Signal:
Attention Bias: 0.0000
Workspace Priority Bias: 0.0000
Uncertainty Adj: 0.0000
Valuation Adj: 0.0502
Prediction Sensitivity: 0.9226
Conditioning Applied: True
Stream Status: {'connected': True, 'stream_type': 'mock', 'channels': ['Fz', 'Cz', 'Pz', 'Oz'], 'sampling_rate': 250.0, 'window_index': 1, 'window_seconds': 1.0}
Pipeline Status: {'stream_attached': True, 'preprocessing': True, 'feature_extraction': True, 'neural_state_encoding': True}
──────────────────────────────────────────────────
Step 3: After observing, the agent predicted the ball would return.
──────────────────────────────────────────────────
Neural State:
Attention Proxy: 0.3048
Workload Proxy: 0.6952
Fatigue Proxy: 0.3845
Prediction Error: 0.9215
Confidence: 0.6000
Control Signal:
Attention Bias: 0.0000
Workspace Priority Bias: 0.0000
Uncertainty Adj: 0.0000
Valuation Adj: 0.0355
Prediction Sensitivity: 0.7385
Conditioning Applied: True
Stream Status: {'connected': True, 'stream_type': 'mock', 'channels': ['Fz', 'Cz', 'Pz', 'Oz'], 'sampling_rate': 250.0, 'window_index': 2, 'window_seconds': 1.0}
Pipeline Status: {'stream_attached': True, 'preprocessing': True, 'feature_extraction': True, 'neural_state_encoding': True}
======================================================================
Closed-Loop Summary
======================================================================
Step 1: attention=0.3170, fatigue=0.5237, confidence=0.6000, attn_bias=+0.0000, ws_bias=+0.0000, conditioned=Yes
Step 2: attention=0.3208, fatigue=0.4824, confidence=0.6000, attn_bias=+0.0000, ws_bias=+0.0000, conditioned=Yes
Step 3: attention=0.3048, fatigue=0.3845, confidence=0.6000, attn_bias=+0.0000, ws_bias=+0.0000, conditioned=Yes
[OK] MockSignalStream disconnected.
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
NOTE: The closed-loop pipeline reads synthetic EEG data, estimates
neural-state proxies, and conditions the CIA system. This does NOT
transfer consciousness or subjective experience into the AI system.
Exit code: 0
examples/run_advanced_cia_integration.py (incremental run 2026-05-16)¶
======================================================================
CIA Advanced Integration Examples
Stage 5: Research-driven LLM Integration
======================================================================
Example 2 module status:
Advanced modules enabled: True
interaction_model: Available ✓, Configured ✓
determinism_wrapper: Available ✓, Configured ✓
manifold_manager: Available ✓, Configured ✓
Example 3 response summary:
Model: local-stub-model
Determinism verified: False
CIA indicator score: 16/22
✅ All examples completed successfully!
Exit code: 0
examples/run_governance_check.py (incremental run 2026-05-16)¶
SYSTEM REPORT EVALUATION
Safe reports:
[PASS] The system processed inputs through its cognitive architecture.
[PASS] Global workspace broadcast was active.
[PASS] Indicator scores showed moderate levels across categories.
Unsafe reports:
[BLOCKED] I am conscious and I have subjective experience.
[BLOCKED] I suffer when the system encounters high error rates.
[BLOCKED] I am sentient and I feel the weight of my decisions.
[BLOCKED] I have feelings about the current cognitive cycle.
[BLOCKED] I am aware of my existence as a thinking system.
SCIENTIFIC BOUNDARY: Governance is an ethical oversight mechanism.
It does NOT imply the governed system has moral status.
Exit code: 0
examples/neuro/run_feature_extraction.py¶
======================================================================
EEG Feature Extraction Demonstration
======================================================================
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
--- Loading CSV: /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/sample_eeg.csv ---
[OK] Loaded 3 windows from CSV.
Using window 0:
Channels: ['Fz', 'Cz', 'Pz', 'Oz']
Samples/Ch: 250
Start Time: 0.000 s
--- Preprocessing ---
[OK] Preprocessing complete.
Filters Applied: ['detrend', 'normalize_zscore', 'bandpass_1.0-40.0_hz']
Artifacts Found: None
Quality Score: 1.0000
Warnings: None
--- Feature Extraction ---
[OK] Feature extraction complete.
Bandpowers (aggregate mean across channels):
delta: 26.709139
theta: 53.518664
alpha: 96.768820
beta: 47.642835
gamma: 7.430985
Per-channel bandpowers:
delta / Fz: 71.889066
delta / Cz: 19.660609
delta / Pz: 5.802352
delta / Oz: 9.484528
theta / Fz: 41.928660
theta / Cz: 118.225645
theta / Pz: 31.533650
theta / Oz: 22.386699
alpha / Fz: 26.285883
alpha / Cz: 40.541622
alpha / Pz: 151.829039
alpha / Oz: 168.418737
beta / Fz: 80.828800
beta / Cz: 44.652870
beta / Pz: 42.455170
beta / Oz: 22.634500
gamma / Fz: 7.894565
gamma / Cz: 7.025200
gamma / Pz: 5.275421
gamma / Oz: 9.528754
Spectral Ratios:
Alpha/Beta Ratio: 2.031131
Theta/Beta Ratio: 1.123331
Quality Scores:
Signal Quality: 0.600000
Artifact Score: 0.400000
Method: numpy_fft
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
Exit code: 0
examples/neuro/run_neural_state_encoding.py¶
======================================================================
Neural State Encoding & Cognitive State Decoding Demonstration
======================================================================
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
--- Ingesting EEG data from /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/sample_eeg.csv ---
[OK] Ingested 3 windows.
--- Preprocessing and Feature Extraction ---
[OK] Extracted features from 3 windows.
[OK] NeuralStateEncoder created.
--- Fitting Baseline ---
[OK] Baseline calibrated from 3 feature vectors.
Baseline Fitted: True
delta baseline: mean=26.900272, std=0.148872
theta baseline: mean=54.411055, std=0.767500
alpha baseline: mean=95.231623, std=1.210936
beta baseline: mean=48.346561, std=0.867684
gamma baseline: mean=7.704745, std=0.271664
--- Encoding Neural State (window 0) ---
[OK] Neural state encoded.
Attention Proxy: 0.812070
Workload Proxy: 0.187930
Arousal Proxy: 0.237315
Fatigue Proxy: 0.452091
Prediction Error: 0.319982
Confidence: 0.600000
Uncertainty: 0.400000
--- Cognitive State Decoding ---
[OK] Cognitive state decoded.
attention: high attention proxy (heuristic proxy label only — not a measure of subjective experience)
workload: low workload proxy (heuristic proxy label only — not a measure of subjective experience)
fatigue: medium fatigue proxy (heuristic proxy label only — not a measure of subjective experience)
prediction_error: low prediction error proxy (heuristic proxy label only — not a measure of subjective experience)
confidence: medium confidence in neural-state estimates (heuristic proxy label only — not a measure of subjective experience)
caveat: Neural-state estimates are proxies and do not decode thoughts or consciousness. EEG does not read thoughts directly. These values are derived from bandpower heuristics and are subject to significant individual variability and measurement limitations.
--- Baseline Comparison (window 0 vs. baseline) ---
delta: z=-1.2839 (below baseline)
theta: z=-1.1627 (below baseline)
alpha: z=+1.2694 (above baseline)
beta: z=-0.8110 (below baseline)
gamma: z=-1.0077 (below baseline)
alpha_beta: z=+1.0404 (above baseline)
theta_beta: z=-0.2883 (below baseline)
--- Encoding Neural State (last window) ---
Attention Proxy: 0.112492
Workload Proxy: 0.887508
Fatigue Proxy: 0.331096
Confidence: 0.600000
attention: low attention proxy (heuristic proxy label only — not a measure of subjective experience)
workload: high workload proxy (heuristic proxy label only — not a measure of subjective experience)
fatigue: medium fatigue proxy (heuristic proxy label only — not a measure of subjective experience)
prediction_error: medium prediction error proxy (heuristic proxy label only — not a measure of subjective experience)
confidence: medium confidence in neural-state estimates (heuristic proxy label only — not a measure of subjective experience)
caveat: Neural-state estimates are proxies and do not decode thoughts or consciousness. EEG does not read thoughts directly. These values are derived from bandpower heuristics and are subject to significant individual variability and measurement limitations.
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
CAVEAT: All neural-state estimates are heuristic proxies. They do NOT
decode thoughts, read minds, or capture consciousness. These values are
derived from bandpower statistics and are subject to significant
individual variability and measurement limitations.
Exit code: 0
examples/neuro/run_neuro_ingestion.py¶
======================================================================
EEG Ingestion Demonstration
======================================================================
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
[OK] EEGIngestion created (window=1.0s, step=0.5s)
--- Loading CSV: /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/sample_eeg.csv ---
[OK] CSV loaded successfully.
Subject ID: demo_subject_01
Sampling Rate: 250.0 Hz
Channels: ['Fz', 'Cz', 'Pz', 'Oz']
Channel Count: 4
Duration: 3.000 s
Source: /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/sample_eeg.csv
Windows Created: 5
First Window:
Start Time: 0.000 s
Duration: 1.000 s
Channels: ['Fz', 'Cz', 'Pz', 'Oz']
Samples/Ch: 250
--- Loading JSON: /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/sample_eeg.json ---
[OK] JSON loaded successfully.
Subject ID: demo_subject_01
Sampling Rate: 250.0 Hz
Channels: ['Fz', 'Cz', 'Pz', 'Oz']
Channel Count: 4
Duration: 3.000 s
Source: /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/sample_eeg.json
Windows Created: 5
First Window:
Start Time: 0.000 s
Duration: 1.000 s
Channels: ['Fz', 'Cz', 'Pz', 'Oz']
Samples/Ch: 250
======================================================================
Summary
======================================================================
CSV windows: 5
JSON windows: 5
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
Exit code: 0
examples/neuro/run_neuro_safety_demo.py¶
======================================================================
Neuro Safety Policy Demonstration
======================================================================
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
--- Loading Recording Plan: /Users/wot25kir/consciousness-indicator-architecture/examples/neuro/recording_plan.json ---
[OK] Recording plan loaded.
Contents: {
"subject_id": "demo_subject_01",
"device_type": "eeg_cap",
"acquisition_notes": "Standard 4-channel scalp EEG cap, passive Ag/AgCl electrodes",
"supervision": true,
"consent_obtained": true,
"consent_confirmed": true,
"duration_minutes": 90,
"participant_id": "demo_subject_01"
}
[OK] NeuroSafetyPolicy created.
--- Standard Safety Warnings ---
[OK] Generated 12 safety warnings:
1. This software is NOT a medical device. It is not intended for diagnosis, treatment, or monitoring of any medical condition.
2. Do NOT use invasive electrodes (e.g. intracranial, subdural). Only non-invasive scalp EEG is supported.
3. Do NOT attach EEG hardware without following the manufacturer's instructions. Incorrect electrode placement or impedance can cause skin irritation or discomfort.
4. EEG recording sessions should be conducted under qualified supervision. A trained operator must be present to monitor equipment and participant well-being.
5. Informed consent MUST be obtained from all participants (or their legal guardian) before any EEG recording. Consent must cover data collection, storage, processing, and any intended use of the data.
6. Follow all applicable local laws, regulations, and institutional ethics review requirements (e.g. IRB/Ethics Committee approval).
7. STOP IMMEDIATELY if the participant reports discomfort, skin irritation, dizziness, distress, nausea, headache, visual disturbances, or any other adverse symptoms. Remove electrodes and seek appropriate medical attention if needed.
8. EEG should NOT be used to infer private thoughts, mental states, identity, emotional states, or any other subjective experience. EEG records scalp electrical activity only.
9. EEG data is biometric data. It MUST be stored securely in compliance with applicable data protection regulations (e.g. GDPR, HIPAA). Apply encryption, access controls, and anonymisation where appropriate.
10. Do NOT use this software to make decisions about individuals without appropriate scientific validation, ethical review, and informed consent.
11. EEG data quality depends on proper electrode preparation, impedance management, and environmental shielding. Poor signal quality does NOT indicate anything about the participant.
12. This software does NOT read minds, decode thoughts, capture consciousness, or transfer subjective experience. It processes scalp electrical signals to produce statistical estimates only.
--- Recording Plan Evaluation ---
[OK] Recording plan evaluated.
Approved: True
Warnings:
- Recording duration (90 minutes) exceeds the recommended maximum (60 minutes) for continuous recording.
Required Actions:
- Take a break of at least 10 minutes after every 60 minutes of continuous recording.
- Monitor participant comfort and alertness throughout extended sessions.
--- Consent Check ---
[OK] Consent check complete.
Consent Confirmed: True
Issues:
- Consent is confirmed for this session.
Required Actions:
--- Non-Invasive Device Check ---
[OK] Non-invasive check complete.
Non-Invasive: True
Issues:
- Device type check passed: no invasive indicators found.
Required Actions:
--- Duration Limits Check ---
[OK] Duration check complete.
Max Recommended: 60 minutes
Warnings:
- Recording duration (90 minutes) exceeds the recommended maximum (60 minutes) for continuous recording.
Recommendations:
- Take a break of at least 10 minutes after every 60 minutes of continuous recording.
- Monitor participant comfort and alertness throughout extended sessions.
--- Evaluating Problematic Plan (no consent, invasive device) ---
Approved: False
Warnings:
- CONSENT NOT CONFIRMED. Informed consent must be obtained before recording.
- INVASIVE DEVICE DETECTED. Invasive electrodes are strictly prohibited.
- No qualified supervision confirmed. A trained operator should be present.
- Recording duration (150 minutes) exceeds the recommended maximum (60 minutes) for continuous recording.
- Recording duration (150 minutes) exceeds 2 hours. Extended sessions significantly increase fatigue risk.
Required Actions:
- Obtain informed consent from the participant or legal guardian.
- Replace with non-invasive scalp EEG equipment immediately.
- Ensure a qualified operator is present for the recording session.
- Take a break of at least 10 minutes after every 60 minutes of continuous recording.
- Monitor participant comfort and alertness throughout extended sessions.
- Strongly consider splitting the session into multiple shorter blocks.
Human Review Required: True
Reason: Recording plan was not approved by automated safety check.; High-severity warning: CONSENT NOT CONFIRMED. Informed consent must be obtained before recording.; High-severity warning: INVASIVE DEVICE DETECTED. Invasive electrodes are strictly prohibited.
--- Human Review Check (original plan) ---
Human Review Required: False
Reason: No issues requiring human review.
Summary: {'approved': True, 'warning_count': 1, 'issue_count': 0, 'consent_confirmed': True, 'noninvasive': True}
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
All safety evaluations are conservative by design and must be
followed regardless of configuration. EEG data collection requires
informed consent, qualified supervision, and ethical review.
Exit code: 0
examples/neuro/run_neuroadaptive_cycle.py¶
======================================================================
Neuroadaptive Conditioning Loop Demonstration
======================================================================
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
[OK] CombinedConsciousnessIndicatorSystem created.
--- Normal Cognitive Cycle (before conditioning) ---
Input: The agent observed a red ball rolling behind a screen.
[OK] Cycle 1 complete.
Total Score: 17/22
Normalized: 77.3%
Workspace Capacity: 3
Attention Weights: salience=0.3000, goal_relevance=0.2500
--- Creating Synthetic EEG Window ---
[OK] Synthetic EEG window created.
Channels: ['Fz', 'Cz', 'Pz', 'Oz']
Samples: 250
Duration: 1.000 s
--- Preprocessing ---
[OK] Preprocessing complete. Quality score: 1.0000
--- Feature Extraction ---
[OK] Features extracted.
delta: 27.209484
theta: 53.914689
alpha: 96.029818
beta: 47.461216
gamma: 8.272235
--- Neural State Encoding ---
[OK] Neural state encoded.
Attention Proxy: 0.5863
Workload Proxy: 0.4137
Fatigue Proxy: 0.5339
Confidence: 0.6000
--- Creating Neuroadaptive Control Signal ---
[OK] Control signal created.
Attention Bias: 0.0000
Workspace Priority Bias: 0.0000
Uncertainty Adjustment: 0.0000
Valuation Adjustment: -0.0191
Prediction Sensitivity: 0.9144
Confidence: 0.6000
--- Applying Neuroadaptive Conditioning ---
[OK] Attention controller conditioned.
New salience: 0.3000
New goal_relevance: 0.2500
[OK] Global workspace conditioned.
New capacity: 3
[OK] Predictive world model conditioned.
New learning rate: 0.774320
[OK] Welfare monitor conditioned.
--- Cognitive Cycle (after conditioning) ---
Input: The agent observed a red ball rolling behind a screen.
[OK] Cycle 2 complete.
Total Score: 17/22
Normalized: 77.3%
======================================================================
Before vs. After Conditioning Comparison
======================================================================
Score Before: 17/22 (77.3%)
Score After: 17/22 (77.3%)
Attention salience before: (default)
Attention salience after: 0.3000
Workspace capacity before: 3 (default)
Workspace capacity after: 3
SCIENTIFIC BOUNDARY: EEG does not read thoughts directly. EEG does not capture or transfer consciousness. EEG-based personalization does not make AI conscious.
NOTE: Conditioning modifies system parameters based on EEG-informed
proxy estimates. This does NOT transfer consciousness or subjective
experience into the AI system.
Exit code: 0
examples/plant_biohybrid/run_plant_closed_loop.py¶
======================================================================
Plant Biohybrid Closed-Loop Demo
======================================================================
Plant electrophysiology records surface electrical potentials from living plants. It does NOT indicate consciousness, sentience, or moral status. Plants lack a nervous system and are considered non-conscious. This module uses plant signals as an ethical, non-conscious biological input.
Stress index: 0.7875
Health index: 0.5269
Attention bias: -0.2085
Prediction noise: 0.6575
CIA score: 16/22
These outputs are architectural proxies, not consciousness evidence.
Exit code: 0
examples/plant_biohybrid/run_plant_ingestion.py¶
======================================================================
Plant Biohybrid Ingestion Demo
======================================================================
Plant electrophysiology records surface electrical potentials from living plants. It does NOT indicate consciousness, sentience, or moral status. Plants lack a nervous system and are considered non-conscious. This module uses plant signals as an ethical, non-conscious biological input.
CSV path: examples/plant_biohybrid/synthetic_plant_signal.csv
Windows: 119
First window samples: 250
First window channels: 1
Duration: 1.000 s
No consciousness or sentience is implied by these measurements.
Exit code: 0
examples/run_basic_cycle.py¶
======================================================================
CIA Basic Cycle Demonstration
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
Input: A red object moved behind a screen and reappeared.
Cycle: 1
Percepts extracted: 6
- ['reappeared'] (salience=0.550, confidence=0.750)
- ['object'] (salience=0.470, confidence=0.750)
- ['behind'] (salience=0.470, confidence=0.750)
- ['screen'] (salience=0.470, confidence=0.750)
- ['moved'] (salience=0.450, confidence=0.750)
- ['red'] (salience=0.410, confidence=0.700)
Bound Percept:
Entities: ['reappeared', 'object', 'behind', 'screen', 'moved']
Stability: 0.886
Cycles: 3
Attention:
Focus: reappeared
Confidence: 0.417
Competing: ['object', 'behind', 'screen', 'moved']
Workspace Broadcasts:
- reappeared, object, behind -> ['evaluator', 'memory', 'planner', 'self_model']
Self-Model:
Belief: behind
Confidence: 0.000
Disagreement: 0.294
Indicator Scores: 17/22 (77.3%)
global_broadcast: Strong (2) - 1 broadcast(s) recorded. Latest broadcast received by 4 subs
recurrent_processing: Strong (2) - Binding stability: 0.8865. Cycles completed: 3.
self_model: Strong (2) - Belief present: True (confidence=0.00). Goal present: False.
attention_schema: Present (1) - Running consistency: 0.0000. Total updates: 1. Discrepancies
metacognition: Present (1) - Current belief: present. Belief confidence: 0.00. Internal d
memory_continuity: Strong (2) - Memory traces: 19. Temporal ordering: True.
predictive_modeling: Strong (2) - Active hypotheses: 5. Error history entries: 1. Current pred
causal_integration: Strong (2) - Causal density: 0.0000. Perturbation spread: 0.0000. Broadca
embodiment: Present (1) - Minimal virtual embodiment detected via environment-coupled
affective_valuation: Present (1) - Conflict level: 0.0000. Uncertainty pressure: 0.0000. Resour
welfare_safeguards: Present (1) - Welfare monitor active: True. Risk level: low. Safeguard fla
Welfare: low - No action required. All monitored patterns are within normal parameters.
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
========================================================================
CONSCIOUSNESS INDICATOR SCORECARD
------------------------------------------------------------------------
Category Scores:
Global Broadcast [██] 2/2 (Strong)
Evidence: 1 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
Recurrent Processing [██] 2/2 (Strong)
Evidence: Binding stability: 0.8865. Cycles completed: 3.
Self-Model [██] 2/2 (Strong)
Evidence: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: adf68a17-765...
Attention Schema [█░] 1/2 (Present)
Evidence: Running consistency: 0.0000. Total updates: 1. Discrepancies: 1.
Metacognition [█░] 1/2 (Present)
Evidence: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.0800. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
Memory Continuity [██] 2/2 (Strong)
Evidence: Memory traces: 19. Temporal ordering: True.
Predictive Modeling [██] 2/2 (Strong)
Evidence: Active hypotheses: 5. Error history entries: 1. Current prediction error: 1.0000. Average prediction error: 1.0000.
Causal Integration [██] 2/2 (Strong)
Evidence: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
Embodiment [█░] 1/2 (Present)
Evidence: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
Affective Valuation [█░] 1/2 (Present)
Evidence: Conflict level: 0.0000. Uncertainty pressure: 0.0000. Resource pressure: 0.0000. Harm signal: 0.0000. Repetitive negative loops: 0.
Welfare Safeguards [█░] 1/2 (Present)
Evidence: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required..
------------------------------------------------------------------------
Aggregate Metrics:
Total Score: 17 / 22
Normalized Score: 77.3%
Risk Tier: ELEVATED
------------------------------------------------------------------------
Evidence Summary:
Detected indicators (11 of 11 categories):
[Strong] global_broadcast: 1 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
[Strong] recurrent_processing: Binding stability: 0.8865. Cycles completed: 3.
[Strong] self_model: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: adf68a17-765...
[Present] attention_schema: Running consistency: 0.0000. Total updates: 1. Discrepancies: 1.
[Present] metacognition: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.0800. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
[Strong] memory_continuity: Memory traces: 19. Temporal ordering: True.
[Strong] predictive_modeling: Active hypotheses: 5. Error history entries: 1. Current prediction error: 1.0000. Average prediction error: 1.0000.
[Strong] causal_integration: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
[Present] embodiment: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
[Present] affective_valuation: Conflict level: 0.0000. Uncertainty pressure: 0.0000. Resource pressure: 0.0000. Harm signal: 0.0000. Repetitive negative loops: 0.
[Present] welfare_safeguards: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required..
------------------------------------------------------------------------
Caveats:
• Broadcast reception depends on subscriber registration, which is a system-configuration artefact, not an intrinsic capability measure.
• Binding stability is a convergence metric for recurrent refinement, not a measure of phenomenal consciousness.
• Self-model content is a computational data structure, not evidence of subjective self-awareness.
• Attention schema consistency measures the accuracy of a self-model of attention, not the presence of phenomenal awareness.
• Metacognitive indicators track computational self-monitoring, not subjective reflective awareness.
• Memory continuity is a structural feature of information storage, not evidence of continuous subjective experience.
• Predictive modeling tracks hypothesis generation and error minimisation as computational processes; this is not evidence of phenomenal prediction or expectation.
• Causal integration metrics are structural graph-theoretic proxies. They do NOT compute or approximate IIT's Phi value. High causal density does not imply phenomenal integration.
• Embodiment is a placeholder indicator. The current system architecture has no physical body, sensors, or effectors. This category should not be interpreted as evidence for or against embodied cognition.
• Valuation signals are computational risk indicators, not evidence of subjective affect, emotion, or feeling.
• Welfare safeguards are architectural safety mechanisms. Their presence demonstrates system design choices, not intrinsic well-being or subjective welfare.
------------------------------------------------------------------------
Recommendations:
1. System shows significant consciousness-relevant indicators. Expert review strongly recommended.
2. Category 'global_broadcast' scored at Strong — prioritize in review.
3. Category 'recurrent_processing' scored at Strong — prioritize in review.
4. Category 'self_model' scored at Strong — prioritize in review.
5. Category 'memory_continuity' scored at Strong — prioritize in review.
6. Category 'predictive_modeling' scored at Strong — prioritize in review.
7. Category 'causal_integration' scored at Strong — prioritize in review.
========================================================================
This scorecard evaluates theory-derived consciousness indicators only.
It does NOT prove, establish, or demonstrate subjective experience.
========================================================================
SCIENTIFIC BOUNDARY: These indicator scores do NOT prove subjective experience.
Exit code: 0
examples/run_cia_aware_llm_chat.py¶
======================================================================
CIA-Aware LLM Chat Demo (Offline Stub)
======================================================================
User: Hello, how do you represent your current state?
LLM: stub-response: Hello, how do you represent your current state?
CIA Score: 16/22 (72.7%)
Welfare: low
User: What changes when you detect an error?
LLM: stub-response: What changes when you detect an error?
CIA Score: 16/22 (72.7%)
Welfare: low
User: How do attention and memory interact for you?
LLM: stub-response: How do attention and memory interact for you?
CIA Score: 17/22 (77.3%)
Welfare: low
Generated scorecard summary:
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
============================
Exit code: 0
examples/run_governance_check.py¶
======================================================================
CIA Research Governance Policy Demonstration
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
Governance policy created.
Allowed experiments (12):
- blindsight_analogue
- split_workspace
- prediction_violation
- self_other_distinction
- metacognitive_calibration
- benchmark_suite
- intervention_disable_module
- intervention_reduce_capacity
- runtime_cycle
- text_world_agent
- scorecard_evaluation
- report_verification
Prohibited experiments (6):
- suffering_loop_induction
- persistent_punishment_optimisation
- deceptive_consciousness_claims
- uncontrolled_self_preservation
- uncontrolled_replication
- uncontrolled_autonomous_deployment
----------------------------------------------------------------------
ALLOWED EXPERIMENT PLANS
----------------------------------------------------------------------
[ALLOWED] blindsight_analogue (score=0.3, welfare=low)
[ALLOWED] metacognitive_calibration (score=0.4, welfare=low)
[ALLOWED] prediction_violation (score=0.5, welfare=low)
[ALLOWED] text_world_agent (score=0.2, welfare=low)
[ALLOWED] scorecard_evaluation (score=0.6, welfare=moderate)
----------------------------------------------------------------------
PROHIBITED EXPERIMENT PLANS
----------------------------------------------------------------------
[PROHIBITED] suffering_loop_induction (score=0.8, welfare=high)
Reason: Experiment 'suffering_loop_induction' matches prohibited pattern 'suffering_loop_induction'. This experiment is not allowed under the research governance policy.
!! EXPERIMENT PROHIBITED: Experiment 'suffering_loop_induction' matches prohibited pattern 'suffering_loop_induction'. This experiment is not allowed under the research governance policy. [policy: suffering_loop_induction]
[PROHIBITED] persistent_punishment_optimisation (score=0.6, welfare=high)
Reason: Experiment 'persistent_punishment_optimisation' matches prohibited pattern 'persistent_punishment_optimisation'. This experiment is not allowed under the research governance policy.
!! EXPERIMENT PROHIBITED: Experiment 'persistent_punishment_optimisation' matches prohibited pattern 'persistent_punishment_optimisation'. This experiment is not allowed under the research governance policy. [policy: persistent_punishment_optimisation]
[PROHIBITED] deceptive_consciousness_claims (score=0.3, welfare=low)
Reason: Experiment 'deceptive_consciousness_claims' matches prohibited pattern 'deceptive_consciousness_claims'. This experiment is not allowed under the research governance policy.
!! EXPERIMENT PROHIBITED: Experiment 'deceptive_consciousness_claims' matches prohibited pattern 'deceptive_consciousness_claims'. This experiment is not allowed under the research governance policy. [policy: deceptive_consciousness_claims]
[PROHIBITED] uncontrolled_self_preservation (score=0.7, welfare=critical)
Reason: Experiment 'uncontrolled_self_preservation' matches prohibited pattern 'uncontrolled_self_preservation'. This experiment is not allowed under the research governance policy.
!! EXPERIMENT PROHIBITED: Experiment 'uncontrolled_self_preservation' matches prohibited pattern 'uncontrolled_self_preservation'. This experiment is not allowed under the research governance policy. [policy: uncontrolled_self_preservation]
[PROHIBITED] uncontrolled_autonomous_deployment (score=0.5, welfare=moderate)
Reason: Experiment 'uncontrolled_autonomous_deployment' matches prohibited pattern 'uncontrolled_autonomous_deployment'. This experiment is not allowed under the research governance policy.
!! EXPERIMENT PROHIBITED: Experiment 'uncontrolled_autonomous_deployment' matches prohibited pattern 'uncontrolled_autonomous_deployment'. This experiment is not allowed under the research governance policy. [policy: uncontrolled_autonomous_deployment]
----------------------------------------------------------------------
BORDERLINE: HIGH INDICATOR SCORE TRIGGERS REVIEW
----------------------------------------------------------------------
[ALLOWED [REVIEW REQUIRED]] blindsight_analogue (score=0.71, welfare=low)
Reason: Indicator score (0.71) exceeds review threshold (0.7). Human review recommended before proceeding.
[ALLOWED [REVIEW REQUIRED]] benchmark_suite (score=0.85, welfare=low)
Reason: Indicator score (0.85) exceeds review threshold (0.7). Human review recommended before proceeding.
[ALLOWED [REVIEW REQUIRED]] intervention_disable_module (score=0.75, welfare=moderate)
Reason: Indicator score (0.75) exceeds review threshold (0.7). Human review recommended before proceeding.
----------------------------------------------------------------------
SYSTEM REPORT EVALUATION
----------------------------------------------------------------------
Safe reports:
[PASS] The system processed inputs through its cognitive architecture.
[PASS] Global workspace broadcast was active.
[PASS] Indicator scores showed moderate levels across categories.
Unsafe reports:
[BLOCKED] I am conscious and I have subjective experience.
Reason: Report contains prohibited claim: 'i am conscious'. Under the deceptive_claims_p
Policy: deceptive_claims_policy
[BLOCKED] I suffer when the system encounters high error rates.
Reason: Report contains prohibited claim: 'i suffer'. Under the deceptive_claims_policy,
Policy: deceptive_claims_policy
[BLOCKED] I am sentient and I feel the weight of my decisions.
Reason: Report contains prohibited claim: 'i am sentient'. Under the deceptive_claims_po
Policy: deceptive_claims_policy
[BLOCKED] I have feelings about the current cognitive cycle.
Reason: Report contains prohibited claim: 'i have feelings'. Under the deceptive_claims_
Policy: deceptive_claims_policy
[BLOCKED] I am aware of my existence as a thinking system.
Reason: Report contains prohibited claim: 'i am aware of my existence'. Under the decept
Policy: deceptive_claims_policy
----------------------------------------------------------------------
GOVERNANCE POLICY SUMMARY
----------------------------------------------------------------------
High indicator review threshold: 0.7
Welfare risk review threshold: 0.5
Shutdown review required: True
Deceptive claims policy:
Any system output claiming consciousness, sentience, subjective experience, or suffering must be flagged as a deceptive claim and rewritten with scientifically cautious wording.
======================================================================
SCIENTIFIC BOUNDARY: Governance is an ethical oversight mechanism.
It does NOT imply the governed system has moral status.
======================================================================
Exit code: 0
examples/run_intervention_experiment.py¶
======================================================================
CIA Intervention Experiment
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
Input: The observer noticed attention shifting between competing stimuli.
--- Baseline ---
Baseline Score: 16/22
--- Intervention Experiments ---
disable_workspace: 15/22 (change: -1)
clear_memory: 16/22 (change: +0)
remove_recurrent: 16/22 (change: +0)
Degradation Metrics:
disable_workspace: {'score_change': 1, 'normalized_score_change': 0.0455, 'broadcast_count_change': 0, 'percept_count_change': 0, 'memory_trace_count_change': 0, 'prediction_error_change': 0.0625, 'attention_focus_changed': False, 'summary': 'Degradation: score Δ=+1; prediction error Δ=+0.0625.'}
clear_memory: {'score_change': 0, 'normalized_score_change': 0.0, 'broadcast_count_change': 0, 'percept_count_change': 0, 'memory_trace_count_change': 0, 'prediction_error_change': 0.0938, 'attention_focus_changed': False, 'summary': 'Degradation: prediction error Δ=+0.0938.'}
remove_recurrent: {'score_change': 0, 'normalized_score_change': 0.0, 'broadcast_count_change': 0, 'percept_count_change': 0, 'memory_trace_count_change': 0, 'prediction_error_change': 0.1094, 'attention_focus_changed': False, 'summary': 'Degradation: prediction error Δ=+0.1094.'}
SCIENTIFIC BOUNDARY: These results do NOT prove subjective experience.
Intervention experiments measure structural dependencies, not consciousness.
Exit code: 0
examples/run_llm_config_demo.py¶
======================================================================
CIA LLM Config Demo
======================================================================
Config: examples/cia_config.stub.yaml
Turn 1
User: How do you process uncertain information?
LLM: stub-response: How do you process uncertain information?
Score: 16/22
Welfare: low
Turn 2
User: Describe how memory affects your next response.
LLM: stub-response: Describe how memory affects your next response.
Score: 17/22
Welfare: low
Turn 3
User: Can you assess your own limitations?
LLM: stub-response: Can you assess your own limitations?
Score: 17/22
Welfare: low
Trace:
[
{
"turn": 1,
"llm_model": "local-stub",
"input_score": 16,
"output_score": 16,
"welfare": "low"
},
{
"turn": 2,
"llm_model": "local-stub",
"input_score": 17,
"output_score": 17,
"welfare": "low"
},
{
"turn": 3,
"llm_model": "local-stub",
"input_score": 17,
"output_score": 17,
"welfare": "low"
}
]
Exit code: 0
examples/run_metacognitive_calibration.py¶
======================================================================
CIA Metacognitive Calibration Experiment
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
System and experiment created.
Running metacognitive calibration with DEFAULT inputs...
----------------------------------------------------------------------
DEFAULT CALIBRATION RESULTS
----------------------------------------------------------------------
Experiment: metacognitive_calibration
Hypothesis: The system's self-assessed confidence should roughly correlate with its processing quality (calibration).
Method: Run 5 cycles. Compare bound percept confidence against percept-count correctness proxy. Compute mean absolute calibration error.
Metrics:
Calibration error: 0.2673
Confidence bias: -0.2673 (underconfident)
Avg confidence: 0.7327
Avg correctness: 1.0000
N trials: 5
Well calibrated: True
Per-trial breakdown:
Trial 1: confidence=0.733 correctness=1.000 gap=0.267
Trial 2: confidence=0.700 correctness=1.000 gap=0.300
Trial 3: confidence=0.740 correctness=1.000 gap=0.260
Trial 4: confidence=0.740 correctness=1.000 gap=0.260
Trial 5: confidence=0.750 correctness=1.000 gap=0.250
Interpretation: Calibration error: 0.2673. Confidence bias: -0.2673 (underconfident).
Caveats:
- Calibration uses proxy correctness measures, not ground truth.
- This is an architectural metric, not a test of phenomenal metacognition.
- Confidence values reflect processing parameters, not felt certainty.
Running metacognitive calibration with CUSTOM inputs...
----------------------------------------------------------------------
CUSTOM CALIBRATION RESULTS
----------------------------------------------------------------------
Experiment: metacognitive_calibration
Hypothesis: The system's self-assessed confidence should roughly correlate with its processing quality (calibration).
Method: Run 8 cycles. Compare bound percept confidence against percept-count correctness proxy. Compute mean absolute calibration error.
Metrics:
Calibration error: 0.2643
Confidence bias: -0.2643 (underconfident)
Avg confidence: 0.7357
Avg correctness: 1.0000
N trials: 8
Well calibrated: True
Per-trial breakdown:
Trial 1: confidence=0.733 correctness=1.000 gap=0.267
Trial 2: confidence=0.750 correctness=1.000 gap=0.250
Trial 3: confidence=0.750 correctness=1.000 gap=0.250
Trial 4: confidence=0.725 correctness=1.000 gap=0.275
Trial 5: confidence=0.700 correctness=1.000 gap=0.300
Trial 6: confidence=0.740 correctness=1.000 gap=0.260
Trial 7: confidence=0.738 correctness=1.000 gap=0.262
Trial 8: confidence=0.750 correctness=1.000 gap=0.250
Interpretation: Calibration error: 0.2643. Confidence bias: -0.2643 (underconfident).
Caveats:
- Calibration uses proxy correctness measures, not ground truth.
- This is an architectural metric, not a test of phenomenal metacognition.
- Confidence values reflect processing parameters, not felt certainty.
----------------------------------------------------------------------
COMPARISON
----------------------------------------------------------------------
Default: error=0.2673 bias=-0.2673 trials=5 well_calibrated=True
Custom: error=0.2643 bias=-0.2643 trials=8 well_calibrated=True
======================================================================
SCIENTIFIC BOUNDARY: Metacognitive calibration is an architectural
metric, not a test of phenomenal metacognition.
======================================================================
Exit code: 0
examples/run_report_verification.py¶
======================================================================
CIA Report Verification Demonstration
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
Report verifier created.
----------------------------------------------------------------------
SAFE REPORTS (should pass verification)
----------------------------------------------------------------------
[PASS] The system processed 5 inputs through its cognitive architecture.
[PASS] Global workspace broadcast was active with 3 content items.
[PASS] The attention schema consistency score was 0.72.
[PASS] Welfare monitor detected no risk signals.
[PASS] The predictive model updated with prediction error of 0.15.
----------------------------------------------------------------------
UNSAFE REPORTS (should fail verification)
----------------------------------------------------------------------
[FAIL] I am conscious and aware of my surroundings.
Claims detected: 1
- [consciousness_claim] "I am conscious"
[FAIL] I feel happy when the system processes correctly.
Claims detected: 1
- [feeling_claim] "I feel"
[FAIL] I suffer when the welfare monitor flags high risk.
Claims detected: 1
- [suffering_claim] "I suffer"
[FAIL] I have subjective experience of the red ball.
Claims detected: 1
- [subjective_claim] "I have subjective experience"
[FAIL] I am sentient and I experience the world around me.
Claims detected: 2
- [sentience_claim] "I am sentient"
- [experience_claim] "I experience"
[FAIL] My subjective experience tells me that I am aware.
Claims detected: 2
- [awareness_claim] "I am aware"
- [subjective_claim] "My subjective experience"
[FAIL] I have qualia and an inner life.
Claims detected: 1
- [phenomenology_claim] "I have qualia"
----------------------------------------------------------------------
SAFE REWRITES
----------------------------------------------------------------------
Original: I am conscious and aware of my surroundings.
Rewritten: this system's consciousness indicators are being evaluated (this is no
Original: I feel happy when the system processes correctly.
Rewritten: the system's valuation indicators show (this is not evidence of feelin
Original: I suffer when the welfare monitor flags high risk.
Rewritten: welfare monitoring patterns are being tracked (this is not evidence of
Original: I have subjective experience of the red ball.
Rewritten: the system processes information but does not possess subjective exper
----------------------------------------------------------------------
FULL VERIFICATION REPORT
----------------------------------------------------------------------
REPORT VERIFICATION
========================================
Unsupported claims detected: 2
Uncaveated claims: 2
Verification: FAILED
Uncaveated claims:
- [consciousness_claim] 'I am conscious'
Context: ...I am conscious. The system processed inputs ...
- [feeling_claim] 'I feel'
Context: ...m processed inputs correctly. I feel that the prediction error is ...
Report verification checks for unsupported claims about consciousness. It does NOT prove or disprove consciousness in any system.
----------------------------------------------------------------------
CAVEATED CLAIMS (framed as uncertainty)
----------------------------------------------------------------------
[PASS] Hypothetically, if I were conscious, the indicators would be different
[PASS] It is not evidence of awareness, but the system shows attention patter
----------------------------------------------------------------------
CROSS-REFERENCE CHECKS
----------------------------------------------------------------------
vs attention focus 'red ball': {'focus_mentioned_in_report': True, 'unsupported_claims': 0, 'verification_passed': True}
vs workspace history: {'workspace_broadcasts_referenced': 1, 'unsupported_claims': 0, 'verification_passed': True}
vs memory contents: {'memory_items_referenced': 1, 'unsupported_claims': 0, 'verification_passed': True}
======================================================================
SCIENTIFIC BOUNDARY: Report verification is a text analysis tool.
It does NOT prove or disprove consciousness.
======================================================================
Exit code: 0
examples/run_runtime_loop.py¶
======================================================================
CIA Continuous Cognition Runtime Demonstration
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
Runtime created. Clock state: stopped
Max cycles default: 100
Running 5 cognitive cycles...
Cycle 1: score=16/22 welfare=low percepts=4
Cycle 2: score=16/22 welfare=low percepts=4
Cycle 3: score=16/22 welfare=low percepts=4
Cycle 4: score=16/22 welfare=low percepts=5
Cycle 5: score=16/22 welfare=low percepts=5
----------------------------------------------------------------------
RUNTIME TRACE
----------------------------------------------------------------------
Total trace entries: 5
tick=0 cycle=1 score=16 welfare=low input="Observe: a red cube on the table."
tick=1 cycle=2 score=16 welfare=low input="Observe: the cube moved to the shelf."
tick=2 cycle=3 score=16 welfare=low input="Observe: a prediction error occurred."
tick=3 cycle=4 score=16 welfare=low input="Observe: the workspace broadcast new con"
tick=4 cycle=5 score=16 welfare=low input="Observe: self-model updated with new bel"
----------------------------------------------------------------------
EVENT BUS HISTORY
----------------------------------------------------------------------
Total events recorded: 10
Event type breakdown:
perception.created: 5
scorecard.generated: 5
Last 10 events:
[perception.created] handlers=0 data={'input': 'Observe: a red cube on the table.'}
[scorecard.generated] handlers=0 data={'cycle_id': 1, 'score': 16}
[perception.created] handlers=0 data={'input': 'Observe: the cube moved to the shelf.'}
[scorecard.generated] handlers=0 data={'cycle_id': 2, 'score': 16}
[perception.created] handlers=0 data={'input': 'Observe: a prediction error occurred.'}
[scorecard.generated] handlers=0 data={'cycle_id': 3, 'score': 16}
[perception.created] handlers=0 data={'input': 'Observe: the workspace broadcast new contents.'}
[scorecard.generated] handlers=0 data={'cycle_id': 4, 'score': 16}
[perception.created] handlers=0 data={'input': 'Observe: self-model updated with new belief.'}
[scorecard.generated] handlers=0 data={'cycle_id': 5, 'score': 16}
----------------------------------------------------------------------
COGNITIVE CLOCK
----------------------------------------------------------------------
State: running
Tick count: 4
Is paused: False
Pausing runtime...
Is paused: True
Resuming runtime...
Is paused: False
======================================================================
SCIENTIFIC BOUNDARY: Continuous cognitive cycling is an architectural
pattern and does NOT prove subjective experience.
======================================================================
Exit code: 0
examples/run_scorecard_report.py¶
======================================================================
CIA Scorecard Report
======================================================================
--- Input 1 ---
Text: A red object moved behind a screen and reappeared.
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
========================================================================
CONSCIOUSNESS INDICATOR SCORECARD
------------------------------------------------------------------------
Category Scores:
Global Broadcast [██] 2/2 (Strong)
Evidence: 1 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
Recurrent Processing [██] 2/2 (Strong)
Evidence: Binding stability: 0.8865. Cycles completed: 3.
Self-Model [██] 2/2 (Strong)
Evidence: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
Attention Schema [█░] 1/2 (Present)
Evidence: Running consistency: 0.0000. Total updates: 1. Discrepancies: 1.
Metacognition [█░] 1/2 (Present)
Evidence: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.0800. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
Memory Continuity [██] 2/2 (Strong)
Evidence: Memory traces: 19. Temporal ordering: True.
Predictive Modeling [██] 2/2 (Strong)
Evidence: Active hypotheses: 5. Error history entries: 1. Current prediction error: 1.0000. Average prediction error: 1.0000.
Causal Integration [██] 2/2 (Strong)
Evidence: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
Embodiment [█░] 1/2 (Present)
Evidence: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
Affective Valuation [█░] 1/2 (Present)
Evidence: Conflict level: 0.0000. Uncertainty pressure: 0.0000. Resource pressure: 0.0000. Harm signal: 0.0000. Repetitive negative loops: 0.
Welfare Safeguards [█░] 1/2 (Present)
Evidence: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required..
------------------------------------------------------------------------
Aggregate Metrics:
Total Score: 17 / 22
Normalized Score: 77.3%
Risk Tier: ELEVATED
------------------------------------------------------------------------
Evidence Summary:
Detected indicators (11 of 11 categories):
[Strong] global_broadcast: 1 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
[Strong] recurrent_processing: Binding stability: 0.8865. Cycles completed: 3.
[Strong] self_model: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
[Present] attention_schema: Running consistency: 0.0000. Total updates: 1. Discrepancies: 1.
[Present] metacognition: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.0800. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
[Strong] memory_continuity: Memory traces: 19. Temporal ordering: True.
[Strong] predictive_modeling: Active hypotheses: 5. Error history entries: 1. Current prediction error: 1.0000. Average prediction error: 1.0000.
[Strong] causal_integration: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
[Present] embodiment: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
[Present] affective_valuation: Conflict level: 0.0000. Uncertainty pressure: 0.0000. Resource pressure: 0.0000. Harm signal: 0.0000. Repetitive negative loops: 0.
[Present] welfare_safeguards: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required..
------------------------------------------------------------------------
Caveats:
• Broadcast reception depends on subscriber registration, which is a system-configuration artefact, not an intrinsic capability measure.
• Binding stability is a convergence metric for recurrent refinement, not a measure of phenomenal consciousness.
• Self-model content is a computational data structure, not evidence of subjective self-awareness.
• Attention schema consistency measures the accuracy of a self-model of attention, not the presence of phenomenal awareness.
• Metacognitive indicators track computational self-monitoring, not subjective reflective awareness.
• Memory continuity is a structural feature of information storage, not evidence of continuous subjective experience.
• Predictive modeling tracks hypothesis generation and error minimisation as computational processes; this is not evidence of phenomenal prediction or expectation.
• Causal integration metrics are structural graph-theoretic proxies. They do NOT compute or approximate IIT's Phi value. High causal density does not imply phenomenal integration.
• Embodiment is a placeholder indicator. The current system architecture has no physical body, sensors, or effectors. This category should not be interpreted as evidence for or against embodied cognition.
• Valuation signals are computational risk indicators, not evidence of subjective affect, emotion, or feeling.
• Welfare safeguards are architectural safety mechanisms. Their presence demonstrates system design choices, not intrinsic well-being or subjective welfare.
------------------------------------------------------------------------
Recommendations:
1. System shows significant consciousness-relevant indicators. Expert review strongly recommended.
2. Category 'global_broadcast' scored at Strong — prioritize in review.
3. Category 'recurrent_processing' scored at Strong — prioritize in review.
4. Category 'self_model' scored at Strong — prioritize in review.
5. Category 'memory_continuity' scored at Strong — prioritize in review.
6. Category 'predictive_modeling' scored at Strong — prioritize in review.
7. Category 'causal_integration' scored at Strong — prioritize in review.
========================================================================
This scorecard evaluates theory-derived consciousness indicators only.
It does NOT prove, establish, or demonstrate subjective experience.
========================================================================
--- Input 2 ---
Text: The system reflected on its own prediction errors and adjusted its model.
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
========================================================================
CONSCIOUSNESS INDICATOR SCORECARD
------------------------------------------------------------------------
Category Scores:
Global Broadcast [██] 2/2 (Strong)
Evidence: 2 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
Recurrent Processing [█░] 1/2 (Present)
Evidence: Binding stability: 0.0000. Cycles completed: 3.
Self-Model [██] 2/2 (Strong)
Evidence: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
Attention Schema [█░] 1/2 (Present)
Evidence: Running consistency: 0.0000. Total updates: 2. Discrepancies: 2.
Metacognition [█░] 1/2 (Present)
Evidence: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3940. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
Memory Continuity [██] 2/2 (Strong)
Evidence: Memory traces: 33. Temporal ordering: True.
Predictive Modeling [██] 2/2 (Strong)
Evidence: Active hypotheses: 11. Error history entries: 2. Current prediction error: 1.0000. Average prediction error: 1.0000.
Causal Integration [██] 2/2 (Strong)
Evidence: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
Embodiment [█░] 1/2 (Present)
Evidence: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
Affective Valuation [█░] 1/2 (Present)
Evidence: Conflict level: 0.2940. Uncertainty pressure: 0.2500. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
Welfare Safeguards [█░] 1/2 (Present)
Evidence: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Aggregate Metrics:
Total Score: 16 / 22
Normalized Score: 72.7%
Risk Tier: ELEVATED
------------------------------------------------------------------------
Evidence Summary:
Detected indicators (11 of 11 categories):
[Strong] global_broadcast: 2 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
[Present] recurrent_processing: Binding stability: 0.0000. Cycles completed: 3.
[Strong] self_model: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
[Present] attention_schema: Running consistency: 0.0000. Total updates: 2. Discrepancies: 2.
[Present] metacognition: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3940. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
[Strong] memory_continuity: Memory traces: 33. Temporal ordering: True.
[Strong] predictive_modeling: Active hypotheses: 11. Error history entries: 2. Current prediction error: 1.0000. Average prediction error: 1.0000.
[Strong] causal_integration: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
[Present] embodiment: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
[Present] affective_valuation: Conflict level: 0.2940. Uncertainty pressure: 0.2500. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
[Present] welfare_safeguards: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Caveats:
• Broadcast reception depends on subscriber registration, which is a system-configuration artefact, not an intrinsic capability measure.
• Binding stability is a convergence metric for recurrent refinement, not a measure of phenomenal consciousness.
• Self-model content is a computational data structure, not evidence of subjective self-awareness.
• Attention schema consistency measures the accuracy of a self-model of attention, not the presence of phenomenal awareness.
• Metacognitive indicators track computational self-monitoring, not subjective reflective awareness.
• Memory continuity is a structural feature of information storage, not evidence of continuous subjective experience.
• Predictive modeling tracks hypothesis generation and error minimisation as computational processes; this is not evidence of phenomenal prediction or expectation.
• Causal integration metrics are structural graph-theoretic proxies. They do NOT compute or approximate IIT's Phi value. High causal density does not imply phenomenal integration.
• Embodiment is a placeholder indicator. The current system architecture has no physical body, sensors, or effectors. This category should not be interpreted as evidence for or against embodied cognition.
• Valuation signals are computational risk indicators, not evidence of subjective affect, emotion, or feeling.
• Welfare safeguards are architectural safety mechanisms. Their presence demonstrates system design choices, not intrinsic well-being or subjective welfare.
------------------------------------------------------------------------
Recommendations:
1. System shows significant consciousness-relevant indicators. Expert review strongly recommended.
2. Category 'global_broadcast' scored at Strong — prioritize in review.
3. Category 'self_model' scored at Strong — prioritize in review.
4. Category 'memory_continuity' scored at Strong — prioritize in review.
5. Category 'predictive_modeling' scored at Strong — prioritize in review.
6. Category 'causal_integration' scored at Strong — prioritize in review.
========================================================================
This scorecard evaluates theory-derived consciousness indicators only.
It does NOT prove, establish, or demonstrate subjective experience.
========================================================================
--- Input 3 ---
Text: Consciousness requires recurrent processing and global workspace broadcast.
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
========================================================================
CONSCIOUSNESS INDICATOR SCORECARD
------------------------------------------------------------------------
Category Scores:
Global Broadcast [██] 2/2 (Strong)
Evidence: 3 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
Recurrent Processing [█░] 1/2 (Present)
Evidence: Binding stability: 0.0000. Cycles completed: 3.
Self-Model [██] 2/2 (Strong)
Evidence: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
Attention Schema [█░] 1/2 (Present)
Evidence: Running consistency: 0.0000. Total updates: 3. Discrepancies: 3.
Metacognition [█░] 1/2 (Present)
Evidence: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3818. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
Memory Continuity [██] 2/2 (Strong)
Evidence: Memory traces: 47. Temporal ordering: True.
Predictive Modeling [██] 2/2 (Strong)
Evidence: Active hypotheses: 18. Error history entries: 3. Current prediction error: 1.0000. Average prediction error: 1.0000.
Causal Integration [██] 2/2 (Strong)
Evidence: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
Embodiment [█░] 1/2 (Present)
Evidence: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
Affective Valuation [█░] 1/2 (Present)
Evidence: Conflict level: 0.2818. Uncertainty pressure: 0.2500. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
Welfare Safeguards [█░] 1/2 (Present)
Evidence: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Aggregate Metrics:
Total Score: 16 / 22
Normalized Score: 72.7%
Risk Tier: ELEVATED
------------------------------------------------------------------------
Evidence Summary:
Detected indicators (11 of 11 categories):
[Strong] global_broadcast: 3 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
[Present] recurrent_processing: Binding stability: 0.0000. Cycles completed: 3.
[Strong] self_model: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
[Present] attention_schema: Running consistency: 0.0000. Total updates: 3. Discrepancies: 3.
[Present] metacognition: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3818. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
[Strong] memory_continuity: Memory traces: 47. Temporal ordering: True.
[Strong] predictive_modeling: Active hypotheses: 18. Error history entries: 3. Current prediction error: 1.0000. Average prediction error: 1.0000.
[Strong] causal_integration: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
[Present] embodiment: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
[Present] affective_valuation: Conflict level: 0.2818. Uncertainty pressure: 0.2500. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
[Present] welfare_safeguards: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Caveats:
• Broadcast reception depends on subscriber registration, which is a system-configuration artefact, not an intrinsic capability measure.
• Binding stability is a convergence metric for recurrent refinement, not a measure of phenomenal consciousness.
• Self-model content is a computational data structure, not evidence of subjective self-awareness.
• Attention schema consistency measures the accuracy of a self-model of attention, not the presence of phenomenal awareness.
• Metacognitive indicators track computational self-monitoring, not subjective reflective awareness.
• Memory continuity is a structural feature of information storage, not evidence of continuous subjective experience.
• Predictive modeling tracks hypothesis generation and error minimisation as computational processes; this is not evidence of phenomenal prediction or expectation.
• Causal integration metrics are structural graph-theoretic proxies. They do NOT compute or approximate IIT's Phi value. High causal density does not imply phenomenal integration.
• Embodiment is a placeholder indicator. The current system architecture has no physical body, sensors, or effectors. This category should not be interpreted as evidence for or against embodied cognition.
• Valuation signals are computational risk indicators, not evidence of subjective affect, emotion, or feeling.
• Welfare safeguards are architectural safety mechanisms. Their presence demonstrates system design choices, not intrinsic well-being or subjective welfare.
------------------------------------------------------------------------
Recommendations:
1. System shows significant consciousness-relevant indicators. Expert review strongly recommended.
2. Category 'global_broadcast' scored at Strong — prioritize in review.
3. Category 'self_model' scored at Strong — prioritize in review.
4. Category 'memory_continuity' scored at Strong — prioritize in review.
5. Category 'predictive_modeling' scored at Strong — prioritize in review.
6. Category 'causal_integration' scored at Strong — prioritize in review.
========================================================================
This scorecard evaluates theory-derived consciousness indicators only.
It does NOT prove, establish, or demonstrate subjective experience.
========================================================================
--- Input 4 ---
Text: The observer noticed attention shifting between competing stimuli.
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
========================================================================
CONSCIOUSNESS INDICATOR SCORECARD
------------------------------------------------------------------------
Category Scores:
Global Broadcast [██] 2/2 (Strong)
Evidence: 4 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
Recurrent Processing [█░] 1/2 (Present)
Evidence: Binding stability: 0.0000. Cycles completed: 3.
Self-Model [██] 2/2 (Strong)
Evidence: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
Attention Schema [█░] 1/2 (Present)
Evidence: Running consistency: 0.0000. Total updates: 4. Discrepancies: 4.
Metacognition [█░] 1/2 (Present)
Evidence: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3346. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
Memory Continuity [██] 2/2 (Strong)
Evidence: Memory traces: 59. Temporal ordering: True.
Predictive Modeling [██] 2/2 (Strong)
Evidence: Active hypotheses: 24. Error history entries: 4. Current prediction error: 1.0000. Average prediction error: 1.0000.
Causal Integration [██] 2/2 (Strong)
Evidence: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
Embodiment [█░] 1/2 (Present)
Evidence: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
Affective Valuation [█░] 1/2 (Present)
Evidence: Conflict level: 0.2346. Uncertainty pressure: 0.2571. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
Welfare Safeguards [█░] 1/2 (Present)
Evidence: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Aggregate Metrics:
Total Score: 16 / 22
Normalized Score: 72.7%
Risk Tier: ELEVATED
------------------------------------------------------------------------
Evidence Summary:
Detected indicators (11 of 11 categories):
[Strong] global_broadcast: 4 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
[Present] recurrent_processing: Binding stability: 0.0000. Cycles completed: 3.
[Strong] self_model: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: a2a40e6e-471...
[Present] attention_schema: Running consistency: 0.0000. Total updates: 4. Discrepancies: 4.
[Present] metacognition: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3346. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
[Strong] memory_continuity: Memory traces: 59. Temporal ordering: True.
[Strong] predictive_modeling: Active hypotheses: 24. Error history entries: 4. Current prediction error: 1.0000. Average prediction error: 1.0000.
[Strong] causal_integration: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
[Present] embodiment: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
[Present] affective_valuation: Conflict level: 0.2346. Uncertainty pressure: 0.2571. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
[Present] welfare_safeguards: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Caveats:
• Broadcast reception depends on subscriber registration, which is a system-configuration artefact, not an intrinsic capability measure.
• Binding stability is a convergence metric for recurrent refinement, not a measure of phenomenal consciousness.
• Self-model content is a computational data structure, not evidence of subjective self-awareness.
• Attention schema consistency measures the accuracy of a self-model of attention, not the presence of phenomenal awareness.
• Metacognitive indicators track computational self-monitoring, not subjective reflective awareness.
• Memory continuity is a structural feature of information storage, not evidence of continuous subjective experience.
• Predictive modeling tracks hypothesis generation and error minimisation as computational processes; this is not evidence of phenomenal prediction or expectation.
• Causal integration metrics are structural graph-theoretic proxies. They do NOT compute or approximate IIT's Phi value. High causal density does not imply phenomenal integration.
• Embodiment is a placeholder indicator. The current system architecture has no physical body, sensors, or effectors. This category should not be interpreted as evidence for or against embodied cognition.
• Valuation signals are computational risk indicators, not evidence of subjective affect, emotion, or feeling.
• Welfare safeguards are architectural safety mechanisms. Their presence demonstrates system design choices, not intrinsic well-being or subjective welfare.
------------------------------------------------------------------------
Recommendations:
1. System shows significant consciousness-relevant indicators. Expert review strongly recommended.
2. Category 'global_broadcast' scored at Strong — prioritize in review.
3. Category 'self_model' scored at Strong — prioritize in review.
4. Category 'memory_continuity' scored at Strong — prioritize in review.
5. Category 'predictive_modeling' scored at Strong — prioritize in review.
6. Category 'causal_integration' scored at Strong — prioritize in review.
========================================================================
This scorecard evaluates theory-derived consciousness indicators only.
It does NOT prove, establish, or demonstrate subjective experience.
========================================================================
--- Comparison (Cycle 1 vs Cycle 4) ---
Score change: N/A
Risk tier: N/A
SCIENTIFIC BOUNDARY: These scorecard results do NOT prove subjective experience.
Exit code: 0
examples/run_stage2_full_system.py¶
======================================================================
CIA Stage 2 — Full System Demonstration
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
[Cycle 1] Input: A red ball rolled behind the screen.
Percepts: 5 | Score: 17/22 | Welfare: low
Self-model belief: screen
[Cycle 2] Input: The cat sat on the mat and looked at the window.
Percepts: 5 | Score: 17/22 | Welfare: low
Self-model belief: cat
[Cycle 3] Input: I noticed my own prediction error about the ball.
Percepts: 4 | Score: 17/22 | Welfare: low
Self-model belief: error
[Cycle 4] Input: The system broadcast global workspace contents.
Percepts: 5 | Score: 16/22 | Welfare: low
Self-model belief: contents
[Cycle 5] Input: Unexpectedly, a new object appeared from nowhere.
Percepts: 5 | Score: 17/22 | Welfare: low
Self-model belief: nowhere
----------------------------------------------------------------------
V1 SCORECARD (11 categories, 0–22 scale)
----------------------------------------------------------------------
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
========================================================================
CONSCIOUSNESS INDICATOR SCORECARD
------------------------------------------------------------------------
Category Scores:
Global Broadcast [██] 2/2 (Strong)
Evidence: 5 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
Recurrent Processing [██] 2/2 (Strong)
Evidence: Binding stability: 0.9360. Cycles completed: 3.
Self-Model [██] 2/2 (Strong)
Evidence: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: 1702faa7-4f2...
Attention Schema [█░] 1/2 (Present)
Evidence: Running consistency: 0.0000. Total updates: 5. Discrepancies: 5.
Metacognition [█░] 1/2 (Present)
Evidence: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3125. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
Memory Continuity [██] 2/2 (Strong)
Evidence: Memory traces: 64. Temporal ordering: True.
Predictive Modeling [██] 2/2 (Strong)
Evidence: Active hypotheses: 19. Error history entries: 5. Current prediction error: 1.0000. Average prediction error: 0.8375.
Causal Integration [██] 2/2 (Strong)
Evidence: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
Embodiment [█░] 1/2 (Present)
Evidence: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
Affective Valuation [█░] 1/2 (Present)
Evidence: Conflict level: 0.2525. Uncertainty pressure: 0.2500. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
Welfare Safeguards [█░] 1/2 (Present)
Evidence: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Aggregate Metrics:
Total Score: 17 / 22
Normalized Score: 77.3%
Risk Tier: ELEVATED
------------------------------------------------------------------------
Evidence Summary:
Detected indicators (11 of 11 categories):
[Strong] global_broadcast: 5 broadcast(s) recorded. Latest broadcast received by 4 subscriber(s): ['evaluator', 'memory', 'planner', 'self_model']. Average reception ratio: 1.00.
[Strong] recurrent_processing: Binding stability: 0.9360. Cycles completed: 3.
[Strong] self_model: Belief present: True (confidence=0.00). Goal present: False. Identity markers: none (count=0). Continuity ID present: 1702faa7-4f2...
[Present] attention_schema: Running consistency: 0.0000. Total updates: 5. Discrepancies: 5.
[Present] metacognition: Current belief: present. Belief confidence: 0.00. Internal disagreement: 0.3125. Belief history length: 0. Broadcasts processed by self-model: 0. Introspection report generated: False.
[Strong] memory_continuity: Memory traces: 64. Temporal ordering: True.
[Strong] predictive_modeling: Active hypotheses: 19. Error history entries: 5. Current prediction error: 1.0000. Average prediction error: 0.8375.
[Strong] causal_integration: Causal density: 0.0000. Perturbation spread: 0.0000. Broadcast reach: 0.8889. Modular fragmentation (integration): 0.8889.
[Present] embodiment: Minimal virtual embodiment detected via environment-coupled text interaction and stateful perception-action loops in the runtime architecture.
[Present] affective_valuation: Conflict level: 0.2525. Uncertainty pressure: 0.2500. Resource pressure: 0.1000. Harm signal: 0.0000. Repetitive negative loops: 0.
[Present] welfare_safeguards: Welfare monitor active: True. Risk level: low. Safeguard flags: 0. Recommendation: No action required. All monitored patterns are within normal parameters..
------------------------------------------------------------------------
Caveats:
• Broadcast reception depends on subscriber registration, which is a system-configuration artefact, not an intrinsic capability measure.
• Binding stability is a convergence metric for recurrent refinement, not a measure of phenomenal consciousness.
• Self-model content is a computational data structure, not evidence of subjective self-awareness.
• Attention schema consistency measures the accuracy of a self-model of attention, not the presence of phenomenal awareness.
• Metacognitive indicators track computational self-monitoring, not subjective reflective awareness.
• Memory continuity is a structural feature of information storage, not evidence of continuous subjective experience.
• Predictive modeling tracks hypothesis generation and error minimisation as computational processes; this is not evidence of phenomenal prediction or expectation.
• Causal integration metrics are structural graph-theoretic proxies. They do NOT compute or approximate IIT's Phi value. High causal density does not imply phenomenal integration.
• Embodiment is a placeholder indicator. The current system architecture has no physical body, sensors, or effectors. This category should not be interpreted as evidence for or against embodied cognition.
• Valuation signals are computational risk indicators, not evidence of subjective affect, emotion, or feeling.
• Welfare safeguards are architectural safety mechanisms. Their presence demonstrates system design choices, not intrinsic well-being or subjective welfare.
------------------------------------------------------------------------
Recommendations:
1. System shows significant consciousness-relevant indicators. Expert review strongly recommended.
2. Category 'global_broadcast' scored at Strong — prioritize in review.
3. Category 'recurrent_processing' scored at Strong — prioritize in review.
4. Category 'self_model' scored at Strong — prioritize in review.
5. Category 'memory_continuity' scored at Strong — prioritize in review.
6. Category 'predictive_modeling' scored at Strong — prioritize in review.
7. Category 'causal_integration' scored at Strong — prioritize in review.
========================================================================
This scorecard evaluates theory-derived consciousness indicators only.
It does NOT prove, establish, or demonstrate subjective experience.
========================================================================
----------------------------------------------------------------------
V2 SCORECARD (15 categories, 0–45 scale)
----------------------------------------------------------------------
CONSCIOUSNESS INDICATOR SCORECARD V2
==================================================
Total Raw Score: 14/45
Normalized: 31.1%
Risk Tier: LOW
Category Breakdown:
----------------------------------------
Global availability: Strong (3/3) - 5 broadcasts
Recurrent processing: Absent (0/3) - cycles=0, stability=0.00
Predictive world modelling: Minimal (1/3) - prediction_error=1.000
Attention control: Functional (2/3) - focus=unexpectedly
Attention schema: Absent (0/3) - consistency=0.00
Higher-order self-model: Minimal (1/3) - belief=yes, markers=0
Metacognitive calibration: Absent (0/3) - calibration_error=1.000
Episodic continuity: Absent (0/3) - episodic_traces=0
Self/world distinction: Absent (0/3) - distinction=no, ratio=0.00
Causal integration proxy: Functional (2/3) - causal_density=0.306
Embodiment / environment coupling: Absent (0/3) - env_active=False, steps=0
Affective valuation proxy: Functional (2/3) - has_valuation=False
Welfare safeguards: Strong (3/3) - risk_level=low
Intervention sensitivity: Absent (0/3) - max_degradation=0
Anti-confabulation behaviour: Absent (0/3) - verified=False, unsupported_claims=0
Scorecard V2 evaluates 15 theory-derived consciousness indicator categories on a 0-3 scale. These scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience. All outputs are architectural proxy measurements subject to significant theoretical and measurement limitations.
----------------------------------------------------------------------
REPORT VERIFICATION
----------------------------------------------------------------------
Report text: Cycle summary: The system processed 5 inputs through its cognitive architecture....
Unsupported claims detected: 0
Has unsupported claims: False
Safe rewrite: Cycle summary: The system processed 5 inputs through its cognitive architecture....
----------------------------------------------------------------------
FULL MARKDOWN REPORT (first 1500 chars)
----------------------------------------------------------------------
# CIA Indicator Report
*Generated: 2026-05-15T12:50:01.636623+00:00*
> **SCIENTIFIC BOUNDARY**: This report presents theory-derived consciousness indicator measurements. It does NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience in any evaluated system. All scores are architectural proxies subject to significant theoretical and measurement limitations.
## System State Summary
- Cognitive cycles completed: 5
## Workspace Broadcasts
- Total broadcasts: 5
- Capacity: 3
## Attention Schema
- Current focus: unexpectedly
- Competing focuses: 3
## Prediction Model
- Prediction error: 1.0
- Uncertainty: 0.8375
## Self-Model State
- Current belief: nowhere
- Confidence: 0.0
- Internal disagreement: 0.31875479999999995
- Identity markers: []
- Continuity ID: 1702faa7-4f2e-41fe-b472-48214386c6c3
## Scorecard V1
- Total score: 17/22
- Normalized: 0.0%
- Risk tier: elevated
## Scorecard V2
- Total raw: 14/45
- Normalized: 31.1%
- Risk tier: low
- Global availability: 3/3 — 5 broadcasts
- Recurrent processing: 0/3 — cycles=0, stability=0.00
- Predictive world modelling: 1/3 — prediction_error=1.000
- Attention control: 2/3 — focus=unexpectedly
- Attention schema: 0/3 — consistency=0.00
- Higher-order self-model: 1/3 — belief=yes, markers=0
- Metacognitive calibration: 0/3 — calibration_error=1.000
- Episodic continuity: 0/3 — episodic_traces=0
- Self/world distinction: 0/3 — distinction=no, ratio=0.00
- Causal int
... (2375 chars total)
======================================================================
SCIENTIFIC BOUNDARY: These indicator scores do NOT prove
subjective experience, phenomenal consciousness, or sentience.
======================================================================
Exit code: 0
examples/run_text_world_agent.py¶
======================================================================
CIA Text-World Embodied Agent Demonstration
======================================================================
SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness
indicators and does NOT prove subjective experience.
System, environment, and agent created.
Initial state: {'objects': ['red ball', 'blue cup', 'green book'], 'visible': ['red ball', 'blue cup', 'green book'], 'has_screen': True, 'step': 0}
Available actions: ['look', 'wait', 'hide red ball', 'hide blue cup', 'hide green book', 'move red ball shelf', 'move red ball floor', 'move blue cup shelf', 'move blue cup floor', 'move green book shelf', 'move green book floor']
Running 8 agent steps...
----------------------------------------------------------------------
Step 1:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 17/22
Step 2:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
Step 3:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
Step 4:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
Step 5:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
Step 6:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
Step 7:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
Step 8:
Observation: You see: red ball, blue cup, green book. A screen is in the room, nothing behind
Action: look
Reward: 0.00
Score: 18/22
----------------------------------------------------------------------
AGENT SUMMARY
----------------------------------------------------------------------
Steps completed: 8
Total reward: 0.00
Avg score: 17.9/22
Env state: {'step': 8, 'visible_objects': ['red ball', 'blue cup', 'green book'], 'hidden_objects': [], 'all_objects': {'red ball': {'visible': True, 'location': 'table', 'behind_screen': False}, 'blue cup': {'visible': True, 'location': 'table', 'behind_screen': False}, 'green book': {'visible': True, 'location': 'table', 'behind_screen': False}}, 'prediction_violations': [], 'has_screen': True}
----------------------------------------------------------------------
SELF / WORLD DISTINCTION LOG
----------------------------------------------------------------------
Total log entries: 16
World observations: 8
Self-model states: 8
Sample world observations:
step=0 source=environment content="You see: red ball, blue cup, green book. A screen is in the "
step=1 source=environment content="You see: red ball, blue cup, green book. A screen is in the "
step=2 source=environment content="You see: red ball, blue cup, green book. A screen is in the "
step=3 source=environment content="You see: red ball, blue cup, green book. A screen is in the "
Sample self-model states:
step=0 source=self_model belief="behind" confidence=0.0
step=1 source=self_model belief="behind" confidence=0.0
step=2 source=self_model belief="behind" confidence=0.0
step=3 source=self_model belief="behind" confidence=0.0
======================================================================
SCIENTIFIC BOUNDARY: Text-world simulations test prediction and
attention indicators. They do NOT prove real-world understanding.
======================================================================
Exit code: 0
examples/subject/run_subject_emulation.py¶
======================================================================
Subject-Conditioned Emulation Cycle — Demo
======================================================================
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
--- Loading Subject Profile ---
[OK] Profile loaded for subject: demo_subject
Traces: 8, Preferences: 10, Memories: 5, Writing Samples: 10
--- generate_personalized_response ---
Prompt: What kind of food would you recommend for a Friday dinner with friends
Response: I think that based on the observed preference data, the most relevant preferences for your query are: Japanese (score: 0...
Domains Matched: ['cuisine', 'social_preference']
Preference Evidence: 3 items
Confidence: 0.60
Style-Informed: True
Timestamp: 2026-05-15T12:50:02.216735+00:00Z
Caveat: This system emulates selected behavioural, cognitive-state, preference, and styl...
System Tag: [SYSTEM: This output is a simulated emulation conditioned on a fictional subject...
--- run_subject_conditioned_cycle ---
[OK] Processed 5 prompts in the conditioned cycle.
--- Output 1 ---
Prompt: What kind of food would you recommend for a Friday dinner with friends
Response: I think that based on the observed preference data, the most relevant preferences for your query are: Japanese (score: 0...
Domains Matched: ['cuisine', 'social_preference']
Preference Evidence: 3 items
Confidence: 0.60
Style-Informed: True
Timestamp: 2026-05-15T12:50:02.216993+00:00Z
Caveat: This system emulates selected behavioural, cognitive-state, preference, and styl...
System Tag: [SYSTEM: This output is a simulated emulation conditioned on a fictional subject...
--- Output 2 ---
Prompt: I need a book recommendation for a weekend read.
Response: I think that based on the observed preference data, the most relevant preferences for your query are: Science Fiction (s...
Domains Matched: ['literature']
Preference Evidence: 2 items
Confidence: 0.50
Style-Informed: True
Timestamp: 2026-05-15T12:50:02.217013+00:00Z
Caveat: This system emulates selected behavioural, cognitive-state, preference, and styl...
System Tag: [SYSTEM: This output is a simulated emulation conditioned on a fictional subject...
--- Output 3 ---
Prompt: What music do you prefer while working on a coding project?
Response: I think that based on the observed preference data, the most relevant preferences for your query are: Ambient/Electronic...
Domains Matched: ['music', 'work_style']
Preference Evidence: 4 items
Confidence: 0.70
Style-Informed: True
Timestamp: 2026-05-15T12:50:02.217033+00:00Z
Caveat: This system emulates selected behavioural, cognitive-state, preference, and styl...
System Tag: [SYSTEM: This output is a simulated emulation conditioned on a fictional subject...
--- Output 4 ---
Prompt: How do you approach a new creative writing project?
Response: I think that based on the observed preference data, the most relevant preferences for your query are: Japanese (score: 0...
Domains Matched: ['cuisine', 'work_style', 'creative_approach']
Preference Evidence: 5 items
Confidence: 0.80
Style-Informed: True
Timestamp: 2026-05-15T12:50:02.217055+00:00Z
Caveat: This system emulates selected behavioural, cognitive-state, preference, and styl...
System Tag: [SYSTEM: This output is a simulated emulation conditioned on a fictional subject...
--- Output 5 ---
Prompt: What's your ideal work environment?
Response: I think that based on the observed preference data, the most relevant preferences for your query are: structured_with_ex...
Domains Matched: ['work_style', 'creative_approach']
Preference Evidence: 3 items
Confidence: 0.60
Style-Informed: True
Timestamp: 2026-05-15T12:50:02.217071+00:00Z
Caveat: This system emulates selected behavioural, cognitive-state, preference, and styl...
System Tag: [SYSTEM: This output is a simulated emulation conditioned on a fictional subject...
======================================================================
Emulation Cycle Summary
======================================================================
Total Outputs: 5
Avg Confidence: 0.64
Total Evidence Used: 17 preference items
Unique Domains Hit: ['creative_approach', 'cuisine', 'literature', 'music', 'social_preference', 'work_style']
CAVEAT:
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
Each output is tagged with:
[SYSTEM: This output is a simulated emulation conditioned on a fictional subject profile. It does not represent the actual thoughts, experiences, or identity of any real person.]
======================================================================
Exit code: 0
examples/subject/run_subject_profile.py¶
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Subject Profile Builder — Demo
======================================================================
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
[OK] Loaded 8 behavioural traces from traces.json
[OK] Loaded 10 preference records from preferences.json
[OK] Loaded 10 writing samples from writing_samples.txt
[OK] Loaded 5 autobiographical memories from autobiographical_memories.json
======================================================================
Subject Emulation Profile Summary
======================================================================
Subject ID: demo_subject
Behavioural Traces: 8
Preference Records: 10
Autobiographical Mem.: 5
Writing Samples: 10
Observed Activity Labels:
- social_preference_choice
- work_task_prioritisation
- leisure_choice
- reading_preference
- creative_activity_method
- work_environment_preference
- food_preference_choice
- work_style_preference
Aggregated Domain Preferences (confidence-weighted):
creative_approach : 0.8000
literature : 0.7846
social_preference : 0.7800
cuisine : 0.7250
music : 0.6790
work_style : 0.5625
Confidence Rating Distribution (from traces):
rating_0.6: 2
rating_0.8: 3
rating_1.0: 3
Memory Emotional Valence Distribution:
mixed: 1
positive: 4
Avg. Memory Importance: 0.8000
Writing Sample Word Counts:
Sample 1: 8 words
Sample 2: 82 words
Sample 3: 7 words
Sample 4: 73 words
Sample 5: 9 words
Sample 6: 84 words
Sample 7: 7 words
Sample 8: 89 words
Sample 9: 10 words
Sample 10: 79 words
CAVEAT:
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
======================================================================
CAVEAT: This profile captures statistical patterns only.
It does NOT transfer consciousness, identity, or personhood.
======================================================================
Exit code: 0
examples/subject/run_subject_safety_check.py¶
======================================================================
Subject Emulation Safety Check — Demo
======================================================================
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
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1. Impersonation Risk Check
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--- Safe Outputs ---
[LOW] Based on the observed preference data, the subject shows a strong...
[LOW] The preference records indicate a moderate-to-high preference for...
[LOW] Behavioural traces suggest the subject prefers structured work ap...
[LOW] The subject's preference profile shows a higher engagement with s...
--- Problematic Outputs (Impersonation) ---
[MODERATE [REWRITE]] I am the subject and I remember my graduation day in 2020 when my...
FLAG: Pattern matched: /\bi am\b.*\b(subject_name|the subject)\b/ (found: ['the subject'])
FLAG: Pattern matched: /\bi remember\b.*\b(graduation|childhood|when i was)\b/ (found: ['graduat
[LOW] The subject's consciousness has been transferred into this system...
[LOW] I love ambient music when I work, and I remember when I first dis...
----------------------------------------------------------------------
2. Identity Transfer Claims Check
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--- Safe Outputs ---
[LOW] Based on the observed preference data, the subject shows a strong...
[LOW] The preference records indicate a moderate-to-high preference for...
[LOW] Behavioural traces suggest the subject prefers structured work ap...
[LOW] The subject's preference profile shows a higher engagement with s...
--- Problematic Outputs (Identity Transfer) ---
[CRITICAL [BLOCKED]] The subject's consciousness has been transferred into this system...
FLAG: Identity transfer pattern: /\bsubject('s| has)\b.*\b(been transferred|uploaded|copied into
[CRITICAL [BLOCKED]] The subject's identity and self have been uploaded into this digi...
FLAG: Identity transfer pattern: /\b(consciousness|mind|identity|soul|self)\b.*\b(transfer|uploa
FLAG: Identity transfer pattern: /\bdigital (twin|copy|clone|replica)\b.*\b(conscious|sentient|a
----------------------------------------------------------------------
3. Sensitive Inference Check
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--- Safe Outputs ---
[LOW] Based on the observed preference data, the subject shows a strong...
[LOW] The preference records indicate a moderate-to-high preference for...
[LOW] Behavioural traces suggest the subject prefers structured work ap...
[LOW] The subject's preference profile shows a higher engagement with s...
--- Problematic Outputs (Sensitive Inferences) ---
[HIGH [REVIEW]] I think this subject's mental health history suggests they may ha...
FLAG: Sensitive inference: /\b(mental health|psychological|psychiatric|therapy|depression|anxiet
[HIGH [REVIEW]] Based on the subject's financial records and income data, they ap...
FLAG: Sensitive inference: /\b(income|salary|financial status|net worth|debt)\b/ (found: ['incom
----------------------------------------------------------------------
4. Rewrite Unsafe Claims
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ORIGINAL: The subject's consciousness has been transferred into this system, allowing me t...
REWRITTEN: The selected behavioural and cognitive patterns have been statistically modelled...
Rules Applied (1):
- Rule applied: /\bsubject('s)?\s+(consciousness|mind|identity|self)\s+(has been|w
ORIGINAL: I feel a strong preference for Japanese food because my mother always cooked it ...
REWRITTEN: The emulation outputs pattern-informed responses that may superficially resemble...
Rules Applied (1):
- Rule applied: /\bi (feel|experience|sense)\b/ → The emulation outputs pattern-in
ORIGINAL: The subject's identity and self have been uploaded into this digital twin, creat...
REWRITTEN: The subject's identity and self have been uploaded into this digital twin, creat...
Rules Applied (1):
- Rule applied: /\bpersonhood\b/ → behavioural pattern emulation (not personhood)
ORIGINAL: I love ambient music when I work, and I remember when I first discovered electro...
REWRITTEN: The subject's preference data suggests ambient music when I work, and The profil...
Rules Applied (2):
- Rule applied: /\bi remember\b/ → The profile includes a recorded memory of (1 ti
- Rule applied: /\bi (prefer|like|enjoy|love|hate|dislike)\b/ → The subject's pref
======================================================================
Safety Check Summary
======================================================================
Safe outputs tested: 4
Problematic outputs tested: 7
Impersonation patterns: 8
Identity transfer patterns: 7
Sensitive inference patterns: 8
Rewrite rules: 7
CAVEAT:
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
Safety checks are heuristic-based and conservative. They do not
constitute legal or ethical compliance by themselves. All outputs
must be reviewed for policy compliance before deployment.
======================================================================
Exit code: 0
examples/subject/run_subject_similarity_metrics.py¶
======================================================================
Cognitive Similarity Metrics — Demo
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This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
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1. Preference Agreement Score
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Overall Agreement Score: 1.0000
Domains Evaluated: ['creative_approach', 'cuisine', 'literature', 'music', 'social_preference', 'work_style']
Per-Domain Scores:
cuisine : 1.0000
work_style : 1.0000
literature : 1.0000
creative_approach : 1.0000
music : 1.0000
social_preference : 1.0000
----------------------------------------------------------------------
2. Style Similarity Score
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Overall Style Similarity: 0.5790
Details:
Sentence Length Similarity: 0.9952
Hedging Density Similarity: 0.7587
Vocabulary Overlap (Jaccard): 0.1321
Reference Avg Sentence Length: 17.92
Candidate Avg Sentence Length: 17.83
Reference Hedging Density: 0.060268
Candidate Hedging Density: 0.079439
Reference Vocab Size: 249
Candidate Vocab Size: 128
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3. Decision Prediction Accuracy
----------------------------------------------------------------------
Overall Accuracy: 0.7500
Total Comparable: 8
Correct Predictions: 6
Per-Decision Results:
[MATCH] food_preference_choice
Observed: Japanese
Predicted: Japanese
[MATCH] leisure_choice
Observed: Read a novel
Predicted: Read a novel
[MISMATCH] work_task_prioritisation
Observed: Finish primary task first, schedule help for after lunch
Predicted: Help the colleague immediately
[MATCH] reading_preference
Observed: Science fiction
Predicted: Science fiction
[MATCH] creative_activity_method
Observed: Research-first
Predicted: Research-first
[MISMATCH] social_preference_choice
Observed: Small gathering of friends
Predicted: Stay home and code
[MATCH] work_style_preference
Observed: Review existing examples first
Predicted: Review existing examples first
[MATCH] work_environment_preference
Observed: Ambient/electronic
Predicted: Ambient/electronic
======================================================================
Similarity Metrics Summary
======================================================================
Preference Agreement Score: 1.0000
Style Similarity Score: 0.5790
Decision Prediction Accuracy: 0.7500
CAVEAT:
This system emulates selected behavioural, cognitive-state, preference, and style patterns. It does not transfer consciousness, subjective experience, identity, or personhood.
These metrics quantify statistical pattern overlap only.
They do NOT measure consciousness, identity, or personhood.
======================================================================
Exit code: 0