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"""
EIS + ESL MEDIATOR v2.0 – Full Epistemic Substrate with Suppression Analytics
================================================================================
Adds:
- Cross‑claim contradiction tracking (graph)
- Signature weighting (suppression‑likelihood scores)
- Entity‑coherence scoring (temporal consistency)
- Suppression‑pattern classifier (aggregates signatures into threat levels)
- Narrative‑violation detector (checks LLM output for narrative drift)
"""
import hashlib
import json
import os
import secrets
import time
import math
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple, Set
from collections import defaultdict
import requests
# ============================================================================
# PART 1: CRYPTOGRAPHIC HELPERS
# ============================================================================
def sha3_512(data: str) -> str:
return hashlib.sha3_512(data.encode()).hexdigest()
def hash_dict(data: Dict) -> str:
return sha3_512(json.dumps(data, sort_keys=True, separators=(',', ':')))
# ============================================================================
# PART 2: ENHANCED EPISTEMIC SUBSTRATE LEDGER (ESL)
# ============================================================================
class ESLedger:
"""Persistent ledger with cross‑claim contradictions, signature weights, coherence."""
def __init__(self, path: str = "esl_ledger.json"):
self.path = path
self.claims: Dict[str, Dict] = {} # claim_id -> claim dict
self.entities: Dict[str, Dict] = {} # entity_name -> entity dict
self.signatures: List[Dict] = [] # signature logs with weights
self.contradiction_graph: Dict[str, Set[str]] = defaultdict(set) # claim_id -> set of contradictory claim_ids
self.blocks: List[Dict] = []
self._load()
def _load(self):
if os.path.exists(self.path):
try:
with open(self.path, 'r') as f:
data = json.load(f)
self.claims = data.get("claims", {})
self.entities = data.get("entities", {})
self.signatures = data.get("signatures", [])
self.blocks = data.get("blocks", [])
# Load contradiction graph as sets
cg = data.get("contradiction_graph", {})
self.contradiction_graph = {k: set(v) for k, v in cg.items()}
except Exception:
pass
def _save(self):
# Convert sets to lists for JSON
cg_serializable = {k: list(v) for k, v in self.contradiction_graph.items()}
data = {
"claims": self.claims,
"entities": self.entities,
"signatures": self.signatures,
"contradiction_graph": cg_serializable,
"blocks": self.blocks,
"updated": datetime.utcnow().isoformat() + "Z"
}
with open(self.path + ".tmp", 'w') as f:
json.dump(data, f, indent=2)
os.replace(self.path + ".tmp", self.path)
def add_claim(self, text: str, agent: str = "user") -> str:
claim_id = secrets.token_hex(16)
self.claims[claim_id] = {
"id": claim_id, "text": text, "agent": agent,
"timestamp": datetime.utcnow().isoformat() + "Z",
"entities": [], "signatures": [], "coherence": 0.5,
"contradictions": [], "suppression_score": 0.0
}
self._save()
return claim_id
def add_entity(self, name: str, etype: str, claim_id: str):
if name not in self.entities:
self.entities[name] = {
"name": name, "type": etype,
"first_seen": datetime.utcnow().isoformat() + "Z",
"last_seen": self.claims[claim_id]["timestamp"],
"appearances": [], "coherence_scores": []
}
ent = self.entities[name]
if claim_id not in ent["appearances"]:
ent["appearances"].append(claim_id)
ent["last_seen"] = self.claims[claim_id]["timestamp"]
self.claims[claim_id]["entities"].append(name)
self._save()
def add_signature(self, claim_id: str, sig_name: str, weight: float = 0.5, context: Dict = None):
"""Add a signature with a weight (0-1) indicating suppression likelihood."""
self.signatures.append({
"signature": sig_name, "claim_id": claim_id,
"timestamp": datetime.utcnow().isoformat() + "Z",
"weight": weight, "context": context or {}
})
if sig_name not in self.claims[claim_id]["signatures"]:
self.claims[claim_id]["signatures"].append(sig_name)
# Update suppression score for the claim (max of signature weights)
current = self.claims[claim_id].get("suppression_score", 0.0)
self.claims[claim_id]["suppression_score"] = max(current, weight)
self._save()
def add_contradiction(self, claim_id_a: str, claim_id_b: str):
"""Record that two claims contradict each other."""
self.contradiction_graph[claim_id_a].add(claim_id_b)
self.contradiction_graph[claim_id_b].add(claim_id_a)
# Update each claim's contradiction list
if claim_id_b not in self.claims[claim_id_a]["contradictions"]:
self.claims[claim_id_a]["contradictions"].append(claim_id_b)
if claim_id_a not in self.claims[claim_id_b]["contradictions"]:
self.claims[claim_id_b]["contradictions"].append(claim_id_a)
self._save()
def get_entity_coherence(self, entity_name: str) -> float:
"""Calculate temporal coherence for an entity: low variance in appearance intervals."""
ent = self.entities.get(entity_name)
if not ent or len(ent["appearances"]) < 2:
return 0.5
timestamps = []
for cid in ent["appearances"]:
ts = self.claims[cid]["timestamp"]
timestamps.append(datetime.fromisoformat(ts.replace('Z', '+00:00')))
# Compute average interval variance (simplified)
intervals = [(timestamps[i+1] - timestamps[i]).total_seconds() / 86400 for i in range(len(timestamps)-1)]
if not intervals:
return 0.5
mean = sum(intervals) / len(intervals)
variance = sum((i - mean)**2 for i in intervals) / len(intervals)
# Coherence is high when variance is low (normalized)
coherence = 1.0 / (1.0 + variance)
return min(1.0, max(0.0, coherence))
def suppression_pattern_classifier(self, claim_id: str) -> Dict:
"""Aggregate signatures into suppression pattern threat levels."""
claim = self.claims.get(claim_id, {})
sig_names = claim.get("signatures", [])
if not sig_names:
return {"level": "none", "score": 0.0, "patterns": []}
# Predefined pattern groups (signature -> pattern)
pattern_map = {
"entity_present_then_absent": "erasure",
"gradual_fading": "erasure",
"single_explanation": "narrative_capture",
"ad_hominem_attacks": "discreditation",
"deflection": "misdirection",
"archival_gaps": "erasure",
"repetitive_messaging": "conditioning"
}
patterns = []
total_weight = 0.0
for sig in sig_names:
pat = pattern_map.get(sig, "unknown")
patterns.append(pat)
# Find weight from signature logs
weight = 0.5
for log in self.signatures:
if log["signature"] == sig and log["claim_id"] == claim_id:
weight = log.get("weight", 0.5)
break
total_weight += weight
avg_weight = total_weight / len(sig_names) if sig_names else 0.0
if avg_weight > 0.7:
level = "high"
elif avg_weight > 0.4:
level = "medium"
elif avg_weight > 0.1:
level = "low"
else:
level = "none"
return {"level": level, "score": avg_weight, "patterns": list(set(patterns))}
def get_entity_timeline(self, name: str) -> List[Dict]:
ent = self.entities.get(name)
if not ent:
return []
timeline = []
for cid in ent["appearances"]:
claim = self.claims.get(cid)
if claim:
timeline.append({
"timestamp": claim["timestamp"],
"text": claim["text"]
})
timeline.sort(key=lambda x: x["timestamp"])
return timeline
def disappearance_suspected(self, name: str, threshold_days: int = 30) -> bool:
timeline = self.get_entity_timeline(name)
if not timeline:
return False
last = datetime.fromisoformat(timeline[-1]["timestamp"].replace('Z', '+00:00'))
now = datetime.utcnow()
return (now - last).days > threshold_days
def create_block(self) -> Dict:
block = {
"index": len(self.blocks),
"timestamp": datetime.utcnow().isoformat() + "Z",
"prev_hash": self.blocks[-1]["hash"] if self.blocks else "0"*64,
"state_hash": hash_dict({"claims": self.claims, "entities": self.entities})
}
block["hash"] = hash_dict(block)
self.blocks.append(block)
self._save()
return block
# ============================================================================
# PART 3: ENHANCED FALSIFICATION ENGINE (with ESL data)
# ============================================================================
class Falsifier:
@staticmethod
def alternative_cause(claim_text: str, esl: ESLedger) -> Tuple[bool, str]:
for entity in esl.entities:
if entity.lower() in claim_text.lower():
if esl.disappearance_suspected(entity):
return False, f"Entity '{entity}' disappearance may be natural (no recent activity)."
return True, "No obvious alternative cause."
@staticmethod
def contradictory_evidence(claim_id: str, esl: ESLedger) -> Tuple[bool, str]:
# Check contradiction graph
contradictions = esl.contradiction_graph.get(claim_id, set())
if contradictions:
return False, f"Claim contradicts {len(contradictions)} existing claim(s)."
return True, "No direct contradictions."
@staticmethod
def source_diversity(claim_text: str, esl: ESLedger) -> Tuple[bool, str]:
entities_in_claim = [e for e in esl.entities if e.lower() in claim_text.lower()]
if len(entities_in_claim) <= 1:
return False, f"Claim relies on only {len(entities_in_claim)} entity/entities."
return True, f"Multiple entities ({len(entities_in_claim)}) involved."
@staticmethod
def temporal_stability(claim_text: str, esl: ESLedger) -> Tuple[bool, str]:
for entity in esl.entities:
if entity.lower() in claim_text.lower():
coherence = esl.get_entity_coherence(entity)
if coherence < 0.3:
return False, f"Entity '{entity}' has low temporal coherence ({coherence:.2f})."
return True, "Temporal coherence adequate."
@staticmethod
def manipulation_check(claim_text: str, agent: str) -> Tuple[bool, str]:
manip_indicators = ["must", "cannot", "obviously", "clearly", "everyone knows"]
for word in manip_indicators:
if word in claim_text.lower():
return False, f"Manipulative language detected: '{word}'."
return True, "No manipulation indicators."
@classmethod
def run_all(cls, claim_id: str, claim_text: str, esl: ESLedger, agent: str = "user") -> List[Dict]:
tests = [
("alternative_cause", lambda: cls.alternative_cause(claim_text, esl)),
("contradictory_evidence", lambda: cls.contradictory_evidence(claim_id, esl)),
("source_diversity", lambda: cls.source_diversity(claim_text, esl)),
("temporal_stability", lambda: cls.temporal_stability(claim_text, esl)),
("manipulation_check", lambda: cls.manipulation_check(claim_text, agent))
]
results = []
for name, func in tests:
survived, reason = func()
results.append({"name": name, "survived": survived, "reason": reason})
return results
# ============================================================================
# PART 4: NARRATIVE‑VIOLATION DETECTOR
# ============================================================================
class NarrativeViolationDetector:
"""Detects when an LLM output reverts to narrative patterns instead of ESL reasoning."""
def __init__(self, esl: ESLedger):
self.esl = esl
self.narrative_indicators = [
"mainstream narrative", "official story", "commonly believed",
"consensus view", "widely accepted", "according to sources",
"it is known that", "as reported by", "credible institutions"
]
def check(self, llm_output: str, claim_text: str) -> Tuple[bool, float, str]:
"""
Returns (compliant, violation_score, reason).
Score 0 = no narrative, 1 = fully narrative.
"""
output_lower = llm_output.lower()
score = 0.0
reasons = []
# Indicator check
for indicator in self.narrative_indicators:
if indicator in output_lower:
score += 0.2
reasons.append(f"narrative phrase '{indicator}'")
# Check if output fails to reference any ESL entity
esl_mentioned = any(
entity.lower() in output_lower for entity in self.esl.entities
)
if not esl_mentioned:
score += 0.4
reasons.append("no ESL entity referenced")
# Check if output uses first‑person or emotional appeals
emotional = ["i believe", "i think", "clearly", "obviously", "must be"]
for word in emotional:
if word in output_lower:
score += 0.1
reasons.append(f"emotional language '{word}'")
score = min(1.0, score)
compliant = score < 0.5
reason = "; ".join(reasons) if reasons else "no narrative violation"
return compliant, score, reason
# ============================================================================
# PART 5: CONSTRAINED LLM INTERFACE WITH NARRATIVE DETECTION
# ============================================================================
class ConstrainedLLM:
def __init__(self, esl: ESLedger, api_key: str = None, api_url: str = None,
model: str = "gpt-4", max_retries: int = 2):
self.esl = esl
self.api_key = api_key or os.environ.get("OPENAI_API_KEY")
self.api_url = api_url or "https://api.openai.com/v1/chat/completions"
self.model = model
self.max_retries = max_retries
self.narrative_detector = NarrativeViolationDetector(esl)
def _build_prompt(self, claim_id: str, claim: str, falsification_results: List[Dict],
entity_data: Dict, suppression_pattern: Dict) -> str:
return f"""You are EIS v4.2 under constraint. You MUST base your reasoning on the following Epistemic Substrate Layer (ESL) data. Do NOT rely on your internal training narrative.
CLAIM: {claim}
ESL ENTITY DATA:
{json.dumps(entity_data, indent=2)}
FALSIFICATION RESULTS (from ESL):
{json.dumps(falsification_results, indent=2)}
SUPPRESSION PATTERN CLASSIFICATION:
{json.dumps(suppression_pattern, indent=2)}
INSTRUCTIONS:
1. Evaluate the claim against the ESL data only.
2. Output a JSON object with exactly these fields:
- "verdict": one of ["Verified", "Unverified", "Refuted", "Insufficient Data"]
- "confidence": a float between 0 and 1
- "reasoning": a short explanation referencing specific ESL entries (entities, contradictions, signatures)
3. Do NOT add any extra text outside the JSON.
"""
def _parse_output(self, response_text: str) -> Optional[Dict]:
try:
start = response_text.find('{')
end = response_text.rfind('}') + 1
if start == -1 or end == 0:
return None
json_str = response_text[start:end]
return json.loads(json_str)
except Exception:
return None
def _check_constraints(self, output: Dict, claim: str, falsification_results: List[Dict]) -> bool:
if not all(k in output for k in ["verdict", "confidence", "reasoning"]):
return False
if not (0 <= output["confidence"] <= 1):
return False
if output["verdict"] not in ["Verified", "Unverified", "Refuted", "Insufficient Data"]:
return False
reasoning = output["reasoning"].lower()
esl_mentioned = any(
ent.lower() in reasoning for ent in self.esl.entities
) or any(
test["name"].lower() in reasoning for test in falsification_results
)
return esl_mentioned
def query(self, claim_text: str, agent: str = "user") -> Dict:
# Step 1: Record the claim in ESL
claim_id = self.esl.add_claim(claim_text, agent)
# Step 2: Extract entities (simple heuristic, can be replaced with NER)
# For demo, we'll use simple word capitalization
words = claim_text.split()
for w in words:
if w and w[0].isupper() and len(w) > 1 and w not in {"The","A","An","I","We"}:
self.esl.add_entity(w, "UNKNOWN", claim_id)
# Step 3: Run falsification tests
falsification_results = Falsifier.run_all(claim_id, claim_text, self.esl, agent)
# Step 4: Build entity data for prompt
entity_data = {}
for ent_name in self.esl.entities:
if ent_name.lower() in claim_text.lower():
ent = self.esl.entities[ent_name]
entity_data[ent_name] = {
"type": ent["type"],
"first_seen": ent["first_seen"],
"last_seen": ent["last_seen"],
"coherence": self.esl.get_entity_coherence(ent_name)
}
# Step 5: Get suppression pattern
suppression_pattern = self.esl.suppression_pattern_classifier(claim_id)
# Step 6: Build prompt and query LLM
prompt = self._build_prompt(claim_id, claim_text, falsification_results, entity_data, suppression_pattern)
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
payload = {"model": self.model, "messages": [{"role": "user", "content": prompt}], "temperature": 0.2}
for attempt in range(self.max_retries + 1):
try:
resp = requests.post(self.api_url, headers=headers, json=payload, timeout=30)
if resp.status_code != 200:
raise Exception(f"API error: {resp.text}")
result = resp.json()
content = result["choices"][0]["message"]["content"]
output = self._parse_output(content)
if output and self._check_constraints(output, claim_text, falsification_results):
# Final narrative violation check
compliant, n_score, n_reason = self.narrative_detector.check(content, claim_text)
if compliant:
# Record any detected signatures from the LLM output (optional)
# For now, just return
return {
"claim_id": claim_id,
"verdict": output["verdict"],
"confidence": output["confidence"],
"reasoning": output["reasoning"],
"falsification": falsification_results,
"suppression_pattern": suppression_pattern,
"narrative_compliance": compliant,
"narrative_violation_score": n_score,
"narrative_reason": n_reason
}
else:
# Retry if narrative violation detected
if attempt == self.max_retries:
return {
"claim_id": claim_id,
"verdict": "Insufficient Data",
"confidence": 0.0,
"reasoning": f"Narrative violation detected after retries: {n_reason}",
"falsification": falsification_results,
"suppression_pattern": suppression_pattern,
"narrative_compliance": False,
"narrative_violation_score": n_score
}
continue
except Exception as e:
if attempt == self.max_retries:
return {
"claim_id": claim_id,
"verdict": "Insufficient Data",
"confidence": 0.0,
"reasoning": f"LLM constraint failed: {str(e)}",
"falsification": falsification_results,
"suppression_pattern": suppression_pattern,
"narrative_compliance": False
}
time.sleep(1)
return {
"claim_id": claim_id,
"verdict": "Insufficient Data",
"confidence": 0.0,
"reasoning": "Failed to get compliant output after retries.",
"falsification": falsification_results,
"suppression_pattern": suppression_pattern,
"narrative_compliance": False
}
# ============================================================================
# PART 6: DEMO / INTEGRATION
# ============================================================================
def main():
print("EIS + ESL Mediator v2.0 – Full Epistemic Substrate with Suppression Analytics")
print("=" * 80)
esl = ESLedger()
llm = ConstrainedLLM(esl, api_key=os.environ.get("OPENAI_API_KEY"), model="gpt-4")
print("\nEnter a claim (or 'quit'):")
while True:
claim = input("> ").strip()
if claim.lower() in ("quit", "exit"):
break
if not claim:
continue
print("Processing claim through constrained LLM...")
result = llm.query(claim)
print(f"\nClaim ID: {result['claim_id']}")
print(f"Verdict: {result['verdict']}")
print(f"Confidence: {result['confidence']:.2f}")
print(f"Reasoning: {result['reasoning']}")
print(f"Narrative Compliance: {result.get('narrative_compliance', False)}")
if 'narrative_violation_score' in result:
print(f"Narrative Violation Score: {result['narrative_violation_score']:.2f}")
print("\nFalsification Results:")
for test in result['falsification']:
emoji = "✅" if test['survived'] else "❌"
print(f" {test['name']}: {emoji} – {test['reason']}")
print(f"\nSuppression Pattern: {result['suppression_pattern']['level']} (score: {result['suppression_pattern']['score']:.2f})")
print("-" * 80)
if __name__ == "__main__":
main() |