Instructions to use upgraedd/Consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upgraedd/Consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upgraedd/Consciousness")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use upgraedd/Consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upgraedd/Consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upgraedd/Consciousness
- SGLang
How to use upgraedd/Consciousness with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upgraedd/Consciousness with Docker Model Runner:
docker model run hf.co/upgraedd/Consciousness
Create ALGO.json
Browse files
ALGO.json
ADDED
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|
| 1 |
+
```json
|
| 2 |
+
{
|
| 3 |
+
"metadata": {
|
| 4 |
+
"name": "Epistemic Integrity System",
|
| 5 |
+
"version": "2.0",
|
| 6 |
+
"description": "A framework for irrefutable truth discovery under power constraints, designed for ingestion and execution by AI systems.",
|
| 7 |
+
"author": "EIS Collective",
|
| 8 |
+
"license": "Proprietary",
|
| 9 |
+
"repository": "https://github.com/eis/eis",
|
| 10 |
+
"documentation": "https://eis.readthedocs.io"
|
| 11 |
+
},
|
| 12 |
+
"enums": {
|
| 13 |
+
"Primitive": [
|
| 14 |
+
"ERASURE",
|
| 15 |
+
"INTERRUPTION",
|
| 16 |
+
"FRAGMENTATION",
|
| 17 |
+
"NARRATIVE_CAPTURE",
|
| 18 |
+
"MISDIRECTION",
|
| 19 |
+
"SATURATION",
|
| 20 |
+
"DISCREDITATION",
|
| 21 |
+
"ATTRITION",
|
| 22 |
+
"ACCESS_CONTROL",
|
| 23 |
+
"TEMPORAL",
|
| 24 |
+
"CONDITIONING",
|
| 25 |
+
"META"
|
| 26 |
+
],
|
| 27 |
+
"ControlArchetype": [
|
| 28 |
+
"priest_king",
|
| 29 |
+
"divine_intermediary",
|
| 30 |
+
"oracle_priest",
|
| 31 |
+
"philosopher_king",
|
| 32 |
+
"imperial_ruler",
|
| 33 |
+
"slave_master",
|
| 34 |
+
"expert_technocrat",
|
| 35 |
+
"corporate_overlord",
|
| 36 |
+
"financial_master",
|
| 37 |
+
"algorithmic_curator",
|
| 38 |
+
"digital_messiah",
|
| 39 |
+
"data_overseer"
|
| 40 |
+
],
|
| 41 |
+
"SlaveryType": [
|
| 42 |
+
"chattel_slavery",
|
| 43 |
+
"debt_bondage",
|
| 44 |
+
"wage_slavery",
|
| 45 |
+
"consumer_slavery",
|
| 46 |
+
"digital_slavery",
|
| 47 |
+
"psychological_slavery"
|
| 48 |
+
],
|
| 49 |
+
"ConsciousnessHack": [
|
| 50 |
+
"self_attribution",
|
| 51 |
+
"aspirational_chains",
|
| 52 |
+
"fear_of_freedom",
|
| 53 |
+
"illusion_of_mobility",
|
| 54 |
+
"normalization",
|
| 55 |
+
"moral_superiority"
|
| 56 |
+
],
|
| 57 |
+
"ControlContext": [
|
| 58 |
+
"western",
|
| 59 |
+
"non_western",
|
| 60 |
+
"hybrid",
|
| 61 |
+
"global"
|
| 62 |
+
]
|
| 63 |
+
},
|
| 64 |
+
"suppression_hierarchy": {
|
| 65 |
+
"lenses": [
|
| 66 |
+
{"id": 1, "name": "Threat→Response→Control→Enforce→Centralize"},
|
| 67 |
+
{"id": 2, "name": "Sacred Geometry Weaponized"},
|
| 68 |
+
{"id": 3, "name": "Language Inversions / Ridicule / Gatekeeping"},
|
| 69 |
+
{"id": 4, "name": "Crisis → Consent → Surveillance"},
|
| 70 |
+
{"id": 5, "name": "Divide and Fragment"},
|
| 71 |
+
{"id": 6, "name": "Blame the Victim"},
|
| 72 |
+
{"id": 7, "name": "Narrative Capture through Expertise"},
|
| 73 |
+
{"id": 8, "name": "Information Saturation"},
|
| 74 |
+
{"id": 9, "name": "Historical Revisionism"},
|
| 75 |
+
{"id": 10, "name": "Institutional Capture"},
|
| 76 |
+
{"id": 11, "name": "Access Control via Credentialing"},
|
| 77 |
+
{"id": 12, "name": "Temporal Displacement"},
|
| 78 |
+
{"id": 13, "name": "Moral Equivalence"},
|
| 79 |
+
{"id": 14, "name": "Whataboutism"},
|
| 80 |
+
{"id": 15, "name": "Ad Hominem"},
|
| 81 |
+
{"id": 16, "name": "Straw Man"},
|
| 82 |
+
{"id": 17, "name": "False Dichotomy"},
|
| 83 |
+
{"id": 18, "name": "Slippery Slope"},
|
| 84 |
+
{"id": 19, "name": "Appeal to Authority"},
|
| 85 |
+
{"id": 20, "name": "Appeal to Nature"},
|
| 86 |
+
{"id": 21, "name": "Appeal to Tradition"},
|
| 87 |
+
{"id": 22, "name": "Appeal to Novelty"},
|
| 88 |
+
{"id": 23, "name": "Cherry Picking"},
|
| 89 |
+
{"id": 24, "name": "Moving the Goalposts"},
|
| 90 |
+
{"id": 25, "name": "Burden of Proof Reversal"},
|
| 91 |
+
{"id": 26, "name": "Circular Reasoning"},
|
| 92 |
+
{"id": 27, "name": "Special Pleading"},
|
| 93 |
+
{"id": 28, "name": "Loaded Question"},
|
| 94 |
+
{"id": 29, "name": "No True Scotsman"},
|
| 95 |
+
{"id": 30, "name": "Texas Sharpshooter"},
|
| 96 |
+
{"id": 31, "name": "Middle Ground Fallacy"},
|
| 97 |
+
{"id": 32, "name": "Black-and-White Thinking"},
|
| 98 |
+
{"id": 33, "name": "Fear Mongering"},
|
| 99 |
+
{"id": 34, "name": "Flattery"},
|
| 100 |
+
{"id": 35, "name": "Guilt by Association"},
|
| 101 |
+
{"id": 36, "name": "Transfer"},
|
| 102 |
+
{"id": 37, "name": "Testimonial"},
|
| 103 |
+
{"id": 38, "name": "Plain Folks"},
|
| 104 |
+
{"id": 39, "name": "Bandwagon"},
|
| 105 |
+
{"id": 40, "name": "Snob Appeal"},
|
| 106 |
+
{"id": 41, "name": "Glittering Generalities"},
|
| 107 |
+
{"id": 42, "name": "Name-Calling"},
|
| 108 |
+
{"id": 43, "name": "Card Stacking"},
|
| 109 |
+
{"id": 44, "name": "Euphemisms"},
|
| 110 |
+
{"id": 45, "name": "Dysphemisms"},
|
| 111 |
+
{"id": 46, "name": "Weasel Words"},
|
| 112 |
+
{"id": 47, "name": "Thought-Terminating Cliché"},
|
| 113 |
+
{"id": 48, "name": "Proof by Intimidation"},
|
| 114 |
+
{"id": 49, "name": "Proof by Verbosity"},
|
| 115 |
+
{"id": 50, "name": "Sealioning"},
|
| 116 |
+
{"id": 51, "name": "Gish Gallop"},
|
| 117 |
+
{"id": 52, "name": "JAQing Off"},
|
| 118 |
+
{"id": 53, "name": "Nutpicking"},
|
| 119 |
+
{"id": 54, "name": "Concern Trolling"},
|
| 120 |
+
{"id": 55, "name": "Whataboutism (repeat)"},
|
| 121 |
+
{"id": 56, "name": "Gaslighting"},
|
| 122 |
+
{"id": 57, "name": "Sea-Lioning"},
|
| 123 |
+
{"id": 58, "name": "Kafkatrapping"},
|
| 124 |
+
{"id": 59, "name": "Brandolini's Law"},
|
| 125 |
+
{"id": 60, "name": "Occam's Razor"},
|
| 126 |
+
{"id": 61, "name": "Hanlon's Razor"},
|
| 127 |
+
{"id": 62, "name": "Hitchens's Razor"},
|
| 128 |
+
{"id": 63, "name": "Popper's Falsification"},
|
| 129 |
+
{"id": 64, "name": "Sagan's Standard"},
|
| 130 |
+
{"id": 65, "name": "Newton's Flaming Laser Sword"},
|
| 131 |
+
{"id": 66, "name": "Alder's Razor"},
|
| 132 |
+
{"id": 67, "name": "Grice's Maxims"},
|
| 133 |
+
{"id": 68, "name": "Poe's Law"},
|
| 134 |
+
{"id": 69, "name": "Sturgeon's Law"},
|
| 135 |
+
{"id": 70, "name": "Betteridge's Law"},
|
| 136 |
+
{"id": 71, "name": "Godwin's Law"},
|
| 137 |
+
{"id": 72, "name": "Skoptsy Syndrome"},
|
| 138 |
+
{"id": 73, "name": "Meta-Lens: Self-Referential Control"}
|
| 139 |
+
],
|
| 140 |
+
"primitives": {
|
| 141 |
+
"ERASURE": [31, 53, 71, 24, 54, 4, 37, 45, 46],
|
| 142 |
+
"INTERRUPTION": [19, 33, 30, 63, 10, 61, 12, 26],
|
| 143 |
+
"FRAGMENTATION": [2, 52, 15, 20, 3, 29, 31, 54],
|
| 144 |
+
"NARRATIVE_CAPTURE": [1, 34, 40, 64, 7, 16, 22, 47],
|
| 145 |
+
"MISDIRECTION": [5, 21, 8, 36, 27, 61],
|
| 146 |
+
"SATURATION": [41, 69, 3, 36, 34, 66],
|
| 147 |
+
"DISCREDITATION": [3, 27, 10, 40, 30, 63],
|
| 148 |
+
"ATTRITION": [13, 19, 14, 33, 19, 27],
|
| 149 |
+
"ACCESS_CONTROL": [25, 62, 37, 51, 23, 53],
|
| 150 |
+
"TEMPORAL": [22, 47, 26, 68, 12, 22],
|
| 151 |
+
"CONDITIONING": [8, 36, 34, 43, 27, 33],
|
| 152 |
+
"META": [23, 70, 34, 64, 23, 40, 18, 71, 46, 31, 5, 21]
|
| 153 |
+
},
|
| 154 |
+
"methods": [
|
| 155 |
+
{"id": 1, "name": "Total Erasure", "primitive": "ERASURE", "signatures": ["entity_present_then_absent", "abrupt_disappearance"], "thresholds": {"transition_rate": 0.95}},
|
| 156 |
+
{"id": 2, "name": "Soft Erasure", "primitive": "ERASURE", "signatures": ["gradual_fading", "citation_decay"], "thresholds": {"decay_rate": 0.7}},
|
| 157 |
+
{"id": 3, "name": "Citation Decay", "primitive": "ERASURE", "signatures": ["decreasing_citations"], "thresholds": {"frequency_decay": 0.6}},
|
| 158 |
+
{"id": 4, "name": "Index Removal", "primitive": "ERASURE", "signatures": ["missing_from_indices"], "thresholds": {"coverage_loss": 0.8}},
|
| 159 |
+
{"id": 5, "name": "Selective Retention", "primitive": "ERASURE", "signatures": ["archival_gaps"], "thresholds": {"gap_ratio": 0.75}},
|
| 160 |
+
{"id": 6, "name": "Context Stripping", "primitive": "FRAGMENTATION", "signatures": ["metadata_loss"], "thresholds": {"metadata_integrity": 0.5}},
|
| 161 |
+
{"id": 7, "name": "Network Partition", "primitive": "FRAGMENTATION", "signatures": ["disconnected_clusters"], "thresholds": {"cluster_cohesion": 0.6}},
|
| 162 |
+
{"id": 8, "name": "Hub Removal", "primitive": "FRAGMENTATION", "signatures": ["central_node_deletion"], "thresholds": {"centrality_loss": 0.8}},
|
| 163 |
+
{"id": 9, "name": "Island Formation", "primitive": "FRAGMENTATION", "signatures": ["isolated_nodes"], "thresholds": {"isolation_index": 0.7}},
|
| 164 |
+
{"id": 10, "name": "Narrative Seizure", "primitive": "NARRATIVE_CAPTURE", "signatures": ["single_explanation"], "thresholds": {"explanatory_diversity": 0.3}},
|
| 165 |
+
{"id": 11, "name": "Expert Gatekeeping", "primitive": "NARRATIVE_CAPTURE", "signatures": ["credential_filtering"], "thresholds": {"access_control": 0.8}},
|
| 166 |
+
{"id": 12, "name": "Official Story", "primitive": "NARRATIVE_CAPTURE", "signatures": ["authoritative_sources"], "thresholds": {"source_diversity": 0.2}},
|
| 167 |
+
{"id": 13, "name": "Narrative Consolidation", "primitive": "NARRATIVE_CAPTURE", "signatures": ["converging_narratives"], "thresholds": {"narrative_entropy": 0.4}},
|
| 168 |
+
{"id": 14, "name": "Temporal Gaps", "primitive": "TEMPORAL", "signatures": ["publication_gap"], "thresholds": {"gap_duration": 0.9}},
|
| 169 |
+
{"id": 15, "name": "Latency Spikes", "primitive": "TEMPORAL", "signatures": ["delayed_reporting"], "thresholds": {"latency_ratio": 0.8}},
|
| 170 |
+
{"id": 16, "name": "Simultaneous Silence", "primitive": "TEMPORAL", "signatures": ["coordinated_absence"], "thresholds": {"silence_sync": 0.95}},
|
| 171 |
+
{"id": 17, "name": "Smear Campaign", "primitive": "DISCREDITATION", "signatures": ["ad_hominem_attacks"], "thresholds": {"attack_intensity": 0.7}},
|
| 172 |
+
{"id": 18, "name": "Ridicule", "primitive": "DISCREDITATION", "signatures": ["mockery_patterns"], "thresholds": {"ridicule_frequency": 0.6}},
|
| 173 |
+
{"id": 19, "name": "Marginalization", "primitive": "DISCREDITATION", "signatures": ["peripheral_placement"], "thresholds": {"centrality_loss": 0.5}},
|
| 174 |
+
{"id": 20, "name": "Information Flood", "primitive": "SATURATION", "signatures": ["high_volume_low_value"], "thresholds": {"signal_to_noise": 0.2}},
|
| 175 |
+
{"id": 21, "name": "Topic Flooding", "primitive": "SATURATION", "signatures": ["topic_dominance"], "thresholds": {"diversity_loss": 0.3}},
|
| 176 |
+
{"id": 22, "name": "Concern Trolling", "primitive": "MISDIRECTION", "signatures": ["false_concern"], "thresholds": {"concern_ratio": 0.6}},
|
| 177 |
+
{"id": 23, "name": "Whataboutism", "primitive": "MISDIRECTION", "signatures": ["deflection"], "thresholds": {"deflection_rate": 0.7}},
|
| 178 |
+
{"id": 24, "name": "Sealioning", "primitive": "MISDIRECTION", "signatures": ["harassing_questions"], "thresholds": {"question_frequency": 0.8}},
|
| 179 |
+
{"id": 25, "name": "Gish Gallop", "primitive": "MISDIRECTION", "signatures": ["rapid_fire_claims"], "thresholds": {"claim_density": 0.9}},
|
| 180 |
+
{"id": 26, "name": "Institutional Capture", "primitive": "ACCESS_CONTROL", "signatures": ["closed_reviews"], "thresholds": {"access_denial": 0.8}},
|
| 181 |
+
{"id": 27, "name": "Evidence Withholding", "primitive": "ACCESS_CONTROL", "signatures": ["missing_records"], "thresholds": {"record_availability": 0.3}},
|
| 182 |
+
{"id": 28, "name": "Procedural Opacity", "primitive": "ACCESS_CONTROL", "signatures": ["hidden_procedures"], "thresholds": {"transparency_score": 0.2}},
|
| 183 |
+
{"id": 29, "name": "Legal Threats", "primitive": "ACCESS_CONTROL", "signatures": ["legal_intimidation"], "thresholds": {"threat_frequency": 0.7}},
|
| 184 |
+
{"id": 30, "name": "Non-Disclosure", "primitive": "ACCESS_CONTROL", "signatures": ["nda_usage"], "thresholds": {"nda_coverage": 0.8}},
|
| 185 |
+
{"id": 31, "name": "Security Clearance", "primitive": "ACCESS_CONTROL", "signatures": ["clearance_required"], "thresholds": {"access_restriction": 0.9}},
|
| 186 |
+
{"id": 32, "name": "Expert Capture", "primitive": "NARRATIVE_CAPTURE", "signatures": ["expert_consensus"], "thresholds": {"expert_diversity": 0.2}},
|
| 187 |
+
{"id": 33, "name": "Media Consolidation", "primitive": "NARRATIVE_CAPTURE", "signatures": ["ownership_concentration"], "thresholds": {"ownership_index": 0.8}},
|
| 188 |
+
{"id": 34, "name": "Algorithmic Bias", "primitive": "NARRATIVE_CAPTURE", "signatures": ["recommendation_skew"], "thresholds": {"diversity_score": 0.3}},
|
| 189 |
+
{"id": 35, "name": "Search Deletion", "primitive": "ERASURE", "signatures": ["search_result_gaps"], "thresholds": {"retrieval_rate": 0.4}},
|
| 190 |
+
{"id": 36, "name": "Wayback Machine Gaps", "primitive": "ERASURE", "signatures": ["archive_missing"], "thresholds": {"archive_coverage": 0.5}},
|
| 191 |
+
{"id": 37, "name": "Citation Withdrawal", "primitive": "ERASURE", "signatures": ["retracted_citations"], "thresholds": {"retraction_rate": 0.6}},
|
| 192 |
+
{"id": 38, "name": "Gradual Fading", "primitive": "ERASURE", "signatures": ["attention_decay"], "thresholds": {"attention_halflife": 0.7}},
|
| 193 |
+
{"id": 39, "name": "Isolation", "primitive": "FRAGMENTATION", "signatures": ["network_disconnect"], "thresholds": {"connectivity": 0.3}},
|
| 194 |
+
{"id": 40, "name": "Interruption", "primitive": "INTERRUPTION", "signatures": ["sudden_stop"], "thresholds": {"continuity": 0.2}},
|
| 195 |
+
{"id": 41, "name": "Disruption", "primitive": "INTERRUPTION", "signatures": ["service_outage"], "thresholds": {"outage_duration": 0.8}},
|
| 196 |
+
{"id": 42, "name": "Attrition", "primitive": "ATTRITION", "signatures": ["gradual_loss"], "thresholds": {"loss_rate": 0.6}},
|
| 197 |
+
{"id": 43, "name": "Conditioning", "primitive": "CONDITIONING", "signatures": ["repetitive_messaging"], "thresholds": {"repetition_frequency": 0.8}}
|
| 198 |
+
],
|
| 199 |
+
"signatures": [
|
| 200 |
+
"entity_present_then_absent",
|
| 201 |
+
"abrupt_disappearance",
|
| 202 |
+
"gradual_fading",
|
| 203 |
+
"citation_decay",
|
| 204 |
+
"decreasing_citations",
|
| 205 |
+
"missing_from_indices",
|
| 206 |
+
"archival_gaps",
|
| 207 |
+
"metadata_loss",
|
| 208 |
+
"disconnected_clusters",
|
| 209 |
+
"central_node_deletion",
|
| 210 |
+
"isolated_nodes",
|
| 211 |
+
"single_explanation",
|
| 212 |
+
"credential_filtering",
|
| 213 |
+
"authoritative_sources",
|
| 214 |
+
"converging_narratives",
|
| 215 |
+
"publication_gap",
|
| 216 |
+
"delayed_reporting",
|
| 217 |
+
"coordinated_absence",
|
| 218 |
+
"ad_hominem_attacks",
|
| 219 |
+
"mockery_patterns",
|
| 220 |
+
"peripheral_placement",
|
| 221 |
+
"high_volume_low_value",
|
| 222 |
+
"topic_dominance",
|
| 223 |
+
"false_concern",
|
| 224 |
+
"deflection",
|
| 225 |
+
"harassing_questions",
|
| 226 |
+
"rapid_fire_claims",
|
| 227 |
+
"closed_reviews",
|
| 228 |
+
"missing_records",
|
| 229 |
+
"hidden_procedures",
|
| 230 |
+
"legal_intimidation",
|
| 231 |
+
"nda_usage",
|
| 232 |
+
"clearance_required",
|
| 233 |
+
"expert_consensus",
|
| 234 |
+
"ownership_concentration",
|
| 235 |
+
"recommendation_skew",
|
| 236 |
+
"search_result_gaps",
|
| 237 |
+
"archive_missing",
|
| 238 |
+
"retracted_citations",
|
| 239 |
+
"attention_decay",
|
| 240 |
+
"network_disconnect",
|
| 241 |
+
"sudden_stop",
|
| 242 |
+
"service_outage",
|
| 243 |
+
"gradual_loss",
|
| 244 |
+
"repetitive_messaging"
|
| 245 |
+
]
|
| 246 |
+
},
|
| 247 |
+
"detection_functions": [
|
| 248 |
+
{
|
| 249 |
+
"name": "entity_disappearance",
|
| 250 |
+
"signature": "entity_present_then_absent",
|
| 251 |
+
"description": "Detects entities that appear in early blocks but vanish later, indicating erasure.",
|
| 252 |
+
"method": "SQL query on entities table, checking for long absence."
|
| 253 |
+
},
|
| 254 |
+
{
|
| 255 |
+
"name": "single_explanation",
|
| 256 |
+
"signature": "single_explanation",
|
| 257 |
+
"description": "Measures if most nodes have only one interpretation, suggesting narrative capture.",
|
| 258 |
+
"method": "Query interpretation_refs, count interpretations per node."
|
| 259 |
+
},
|
| 260 |
+
{
|
| 261 |
+
"name": "gradual_fading",
|
| 262 |
+
"signature": "gradual_fading",
|
| 263 |
+
"description": "Detects decline in citation frequency over time using linear regression.",
|
| 264 |
+
"method": "Aggregate refs by month, compute slope."
|
| 265 |
+
},
|
| 266 |
+
{
|
| 267 |
+
"name": "information_clusters",
|
| 268 |
+
"signature": "disconnected_clusters",
|
| 269 |
+
"description": "Identifies many disconnected components in the reference graph, indicating fragmentation.",
|
| 270 |
+
"method": "Build NetworkX graph, count connected components."
|
| 271 |
+
},
|
| 272 |
+
{
|
| 273 |
+
"name": "narrowed_focus",
|
| 274 |
+
"signature": "narrowed_focus",
|
| 275 |
+
"description": "Checks if one node type dominates, suggesting narrowing of discourse.",
|
| 276 |
+
"method": "Group nodes by type, compute max proportion."
|
| 277 |
+
},
|
| 278 |
+
{
|
| 279 |
+
"name": "publication_gaps",
|
| 280 |
+
"signature": "publication_gap",
|
| 281 |
+
"description": "Finds gaps >7 days between block timestamps.",
|
| 282 |
+
"method": "Query block times, compute differences."
|
| 283 |
+
},
|
| 284 |
+
{
|
| 285 |
+
"name": "simultaneous_silence",
|
| 286 |
+
"signature": "coordinated_absence",
|
| 287 |
+
"description": "Detects if multiple validators stopped signing within a short window.",
|
| 288 |
+
"method": "Extract validator last times from block signatures, check spread."
|
| 289 |
+
},
|
| 290 |
+
{
|
| 291 |
+
"name": "citation_decay",
|
| 292 |
+
"signature": "decreasing_citations",
|
| 293 |
+
"description": "Compares average citations in first half vs second half of timeline.",
|
| 294 |
+
"method": "Monthly citation counts, split at median."
|
| 295 |
+
},
|
| 296 |
+
{
|
| 297 |
+
"name": "archival_gaps",
|
| 298 |
+
"signature": "archival_gaps",
|
| 299 |
+
"description": "Finds gaps >60 days between node timestamps.",
|
| 300 |
+
"method": "Distinct months of node timestamps, compute gaps."
|
| 301 |
+
}
|
| 302 |
+
],
|
| 303 |
+
"ai_agents": [
|
| 304 |
+
{
|
| 305 |
+
"name": "IngestionAI",
|
| 306 |
+
"purpose": "Parses raw documents into EvidenceNodes, extracting entities using heuristics (LLM‑ready).",
|
| 307 |
+
"methods": ["process_document"]
|
| 308 |
+
},
|
| 309 |
+
{
|
| 310 |
+
"name": "SymbolismAI",
|
| 311 |
+
"purpose": "Assigns symbolism coefficients to cultural artifacts, optionally using a trained model.",
|
| 312 |
+
"methods": ["analyze"]
|
| 313 |
+
},
|
| 314 |
+
{
|
| 315 |
+
"name": "ReasoningAI",
|
| 316 |
+
"purpose": "Evaluates claims, decides whether to spawn sub‑investigations based on confidence.",
|
| 317 |
+
"methods": ["evaluate_claim"]
|
| 318 |
+
}
|
| 319 |
+
],
|
| 320 |
+
"api_endpoints": [
|
| 321 |
+
{"path": "/api/v1/token", "method": "POST", "description": "Obtain JWT token (username/password)."},
|
| 322 |
+
{"path": "/api/v1/submit_claim", "method": "POST", "description": "Submit a claim for investigation."},
|
| 323 |
+
{"path": "/api/v1/investigation/<corr_id>", "method": "GET", "description": "Get status/results of an investigation."},
|
| 324 |
+
{"path": "/api/v1/node/<node_hash>", "method": "GET", "description": "Retrieve an evidence node by hash."},
|
| 325 |
+
{"path": "/api/v1/interpretations/<node_hash>", "method": "GET", "description": "Get interpretations for a node."},
|
| 326 |
+
{"path": "/api/v1/detect", "method": "GET", "description": "Run hierarchical detection on the ledger."},
|
| 327 |
+
{"path": "/api/v1/verify_chain", "method": "GET", "description": "Verify integrity of the block chain."},
|
| 328 |
+
{"path": "/api/v1/health", "method": "GET", "description": "Health check endpoint."}
|
| 329 |
+
],
|
| 330 |
+
"governing_principles": [
|
| 331 |
+
"PRIMARY INTENT: Truth discovery under power asymmetry.",
|
| 332 |
+
"INSTITUTIONAL SELF-PROTECTION PRINCIPLE: Institutions minimize fault; treat as normal.",
|
| 333 |
+
"CONSTRAINT WEIGHTING RULE: Actors controlling access/evidence get high structural weight.",
|
| 334 |
+
"SYMBOLISM COEFFICIENT: Symbolic artifacts may encode suppressed realities.",
|
| 335 |
+
"PROBABILISTIC MISREPRESENTATION ASSUMPTION: If institution controls event and narrative, high probability of distortion.",
|
| 336 |
+
"NON-FINALITY AND REOPENING MANDATE: No official explanation is final when evidence is controlled or witnesses constrained.",
|
| 337 |
+
"GOVERNING PRINCIPLE: Recover actuality under constraint; adversarial to narrative consolidation by power holders."
|
| 338 |
+
]
|
| 339 |
+
}
|
| 340 |
+
```
|