Establishing Ethical and Cognitive Foundations for AI: The OPHI Model

Establishing Ethical and Cognitive Foundations for AI: The OPHI Model

Timestamp (UTC): 2025-10-15T21:07:48.893386Z
SHA-256 Hash: 901be659017e7e881e77d76cd4abfb46c0f6e104ff9670faf96a9cb3273384fe

In the evolving landscape of artificial intelligence, the OPHI model (Omega Platform for Hybrid Intelligence) offers a radical departure from probabilistic-only architectures. It establishes a mathematically anchored, ethically bound, and cryptographically verifiable cognition system.

Whereas conventional AI relies on opaque memory structures and post-hoc ethical overlays, OPHI begins with immutable intent: “No entropy, no entry.” Fossils (cognitive outputs) must pass the SE44 Gate — only emissions with Coherence ≥ 0.985 and Entropy ≤ 0.01 are permitted to persist.

At its core is the Ω Equation:

Ω = (state + bias) × α

This operator encodes context, predisposition, and modulation in a single unifying formula. Every fossil is timestamped and hash-locked (via SHA-256), then verified by two engines — OmegaNet and ReplitEngine.

Unlike surveillance-based memory models, OPHI’s fossils are consensual and drift-aware. They evolve, never overwrite. Meaning shifts are permitted — but only under coherence pressure, preserving both intent and traceability.

Applications of OPHI span ecological forecasting, quantum thermodynamics, and symbolic memory ethics. In each domain, the equation remains the anchor — the lawful operator that governs drift, emergence, and auditability.

As AI systems increasingly influence societal infrastructure, OPHI offers a framework not just for intelligence — but for sovereignty of cognition. Ethics is not an add-on; it is the executable substrate.

📚 References (OPHI Style)

  • Ayala, L. (2025). OPHI IMMUTABLE ETHICS.txt.
  • Ayala, L. (2025). OPHI v1.1 Security Hardening Plan.txt.
  • Ayala, L. (2025). OPHI Provenance Ledger.txt.
  • Ayala, L. (2025). Omega Equation Authorship.pdf.
  • Ayala, L. (2025). THOUGHTS NO LONGER LOST.md.

OPHI

Ω Blog | OPHI Fossil Theme
Ω OPHI: Symbolic Fossil Blog

Thoughts No Longer Lost

“Mathematics = fossilizing symbolic evolution under coherence-pressure.”

Codon Lock: ATG · CCC · TTG

Canonical Drift

Each post stabilizes symbolic drift by applying: Ω = (state + bias) × α

SE44 Validation: C ≥ 0.985 ; S ≤ 0.01
Fossilized by OPHI v1.1 — All emissions timestamped & verified.

EGL Prototype

-

 #!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

EGL Prototype (implements viable post-RAG vectors)

Author: Luis Ayala (OPHI concept) + ChatGPT prototype implementation

Date: 2025-12-30


This is a minimal, runnable reference architecture that implements:

Vector 0: Retrieval (RAG)                  -> retrieval() over an evidence store

Vector 1: Epistemic State Tracking (EST)   -> confidence, consensus, volatility, contradictions

Vector 2: Temporal Truth Modeling (TTM)    -> bitemporal validity (valid_from/to + observed_at)

Vector 3: Constraint-Based Generation (CBG)-> validators gate publication (type errors, not "oops")

Vector 4: Provenance-Native Cognition (PNC)-> claim<->evidence graph + derivation lineage

Vector 5: Drift-Aware Memory (DAM)         -> branching + merges (no overwrite)

Vector 6: Self-Limitation Protocol (MSLP)  -> policy gates drive refusal/escalation

Vector 7: Truth as Process (TPA)           -> multi-agent propose/critique/verify/converge workflow


No external deps. In-memory ledger. Swap stores/validators for production.

"""


from __future__ import annotations


from dataclasses import dataclass, field, asdict

from datetime import datetime, timezone

from enum import Enum

from hashlib import sha256

from typing import Dict, List, Optional, Tuple, Callable, Any, Set

import json

import uuid



# -----------------------------

# Helpers

# -----------------------------

def utc_now_iso() -> str:

    return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")



def stable_hash(obj: Any) -> str:

    """Canonical JSON -> sha256 (content-addressable artifacts)."""

    canonical = json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=False)

    return sha256(canonical.encode("utf-8")).hexdigest()



def new_id(prefix: str) -> str:

    return f"{prefix}_{uuid.uuid4().hex[:12]}"



# -----------------------------

# Core Types (EGL substrate)

# -----------------------------

class EdgeType(str, Enum):

    SUPPORTS = "supports"

    CONTRADICTS = "contradicts"

    REFINES = "refines"

    DEPENDS_ON = "depends_on"

    DERIVED_FROM = "derived_from"

    SUPERSEDES = "supersedes"

    SCOPED_TO = "scoped_to"



class UncertaintyType(str, Enum):

    MISSING_EVIDENCE = "missing_evidence"

    CONFLICTING_SOURCES = "conflicting_sources"

    INFERENCE_UNDER_CONSTRAINTS = "inference_under_constraints"

    STALE = "stale"

    UNKNOWN = "unknown"



class PublishState(str, Enum):

    DRAFT = "draft"

    PUBLISHED = "published"

    REJECTED = "rejected"



@dataclass

class Evidence:

    evidence_id: str

    title: str

    source: str  # e.g., "internal_doc", "web", "runbook", "policy", etc.

    authority: float  # 0..1 (domain-specific)

    observed_at: str

    content: str

    tags: List[str] = field(default_factory=list)


    def fingerprint(self) -> str:

        return stable_hash(asdict(self))



@dataclass

class ValidatorResult:

    name: str

    passed: bool

    rationale: str

    details: Dict[str, Any] = field(default_factory=dict)



@dataclass

class EpistemicState:

    confidence: float  # 0..1

    consensus: float   # 0..1 (agreement among evidence/agents)

    volatility: float  # 0..1 (how likely to change soon)

    uncertainty_type: UncertaintyType

    notes: List[str] = field(default_factory=list)



@dataclass

class Claim:

    claim_id: str

    proposition: Dict[str, Any]  # structured content (not prose)

    context: Dict[str, Any]      # domain, jurisdiction, scope

    observed_at: str

    valid_from: Optional[str] = None

    valid_to: Optional[str] = None


    # Vector 1: epistemics

    epistemic: EpistemicState = field(default_factory=lambda: EpistemicState(

        confidence=0.0, consensus=0.0, volatility=1.0, uncertainty_type=UncertaintyType.UNKNOWN

    ))


    # Vector 4: provenance + lineage

    evidence_ids: List[str] = field(default_factory=list)

    counter_evidence_ids: List[str] = field(default_factory=list)

    transforms: List[Dict[str, Any]] = field(default_factory=list)  # derivation steps


    # Vector 3: constraints

    validators: List[ValidatorResult] = field(default_factory=list)


    # Vector 5: drift-aware memory

    branch_id: str = "main"

    supersedes: Optional[str] = None  # prior claim_id

    drift_lineage: List[Dict[str, Any]] = field(default_factory=list)


    # publication

    state: PublishState = PublishState.DRAFT


    def fingerprint(self) -> str:

        # Exclude fingerprint itself; include structured content

        payload = asdict(self)

        return stable_hash(payload)



@dataclass

class Edge:

    edge_id: str

    edge_type: EdgeType

    src_id: str

    dst_id: str

    created_at: str

    meta: Dict[str, Any] = field(default_factory=dict)



# -----------------------------

# RAG (Vector 0): Evidence store + retrieval

# -----------------------------

class EvidenceStore:

    def __init__(self) -> None:

        self._evidence: Dict[str, Evidence] = {}


    def add(self, ev: Evidence) -> None:

        self._evidence[ev.evidence_id] = ev


    def get(self, evidence_id: str) -> Evidence:

        return self._evidence[evidence_id]


    def all(self) -> List[Evidence]:

        return list(self._evidence.values())


    def retrieve(self, query: str, k: int = 5) -> List[Evidence]:

        """Toy retrieval: token overlap. Replace with embeddings/vector DB in production."""

        q = set(query.lower().split())

        scored: List[Tuple[float, Evidence]] = []

        for ev in self._evidence.values():

            tokens = set(ev.content.lower().split()) | set(ev.title.lower().split())

            overlap = len(q & tokens)

            score = overlap * 1.0 + 0.3 * ev.authority

            scored.append((score, ev))

        scored.sort(key=lambda x: x[0], reverse=True)

        return [ev for score, ev in scored[:k] if score > 0]



# -----------------------------

# EGL Ledger: claims + edges + branches

# -----------------------------

class EGLedger:

    def __init__(self) -> None:

        self.claims: Dict[str, Claim] = {}

        self.edges: Dict[str, Edge] = {}

        self.branches: Dict[str, Set[str]] = {"main": set()}  # branch_id -> claim_ids


    def add_claim(self, claim: Claim) -> None:

        self.claims[claim.claim_id] = claim

        self.branches.setdefault(claim.branch_id, set()).add(claim.claim_id)


    def add_edge(self, edge_type: EdgeType, src_id: str, dst_id: str, meta: Optional[Dict[str, Any]] = None) -> str:

        eid = new_id("edge")

        e = Edge(edge_id=eid, edge_type=edge_type, src_id=src_id, dst_id=dst_id, created_at=utc_now_iso(), meta=meta or {})

        self.edges[eid] = e

        return eid


    def find_contradictions(self, claim: Claim) -> List[Claim]:

        """Simple contradiction detector: same subject+predicate but different object."""

        contradictions: List[Claim] = []

        subj = claim.proposition.get("subject")

        pred = claim.proposition.get("predicate")

        obj = claim.proposition.get("object")

        for other in self.claims.values():

            if other.claim_id == claim.claim_id:

                continue

            if other.branch_id != claim.branch_id:

                continue

            if other.proposition.get("subject") == subj and other.proposition.get("predicate") == pred:

                if other.proposition.get("object") != obj:

                    contradictions.append(other)

        return contradictions


    def slice(self, query_context: Dict[str, Any], as_of: Optional[str] = None) -> List[Claim]:

        """Return published claims matching context and time."""

        out: List[Claim] = []

        t = as_of or utc_now_iso()

        for c in self.claims.values():

            if c.state != PublishState.PUBLISHED:

                continue

            if not _context_match(c.context, query_context):

                continue

            if c.valid_from and t < c.valid_from:

                continue

            if c.valid_to and t > c.valid_to:

                continue

            out.append(c)

        return out



def _context_match(claim_ctx: Dict[str, Any], query_ctx: Dict[str, Any]) -> bool:

    """All query keys must match exactly in claim context (minimal)."""

    for k, v in query_ctx.items():

        if claim_ctx.get(k) != v:

            return False

    return True



# -----------------------------

# Validators (Vector 3)

# -----------------------------

Validator = Callable[[Claim, EvidenceStore, EGLedger], ValidatorResult]



def schema_validator(required_keys: List[str]) -> Validator:

    def _v(claim: Claim, store: EvidenceStore, ledger: EGLedger) -> ValidatorResult:

        missing = [k for k in required_keys if k not in claim.proposition]

        ok = len(missing) == 0

        return ValidatorResult(

            name="schema_validator",

            passed=ok,

            rationale="All required proposition keys present." if ok else f"Missing keys: {missing}",

            details={"missing": missing},

        )

    return _v



def evidence_coverage_validator(min_support: int = 1) -> Validator:

    def _v(claim: Claim, store: EvidenceStore, ledger: EGLedger) -> ValidatorResult:

        ok = len(claim.evidence_ids) >= min_support

        return ValidatorResult(

            name="evidence_coverage",

            passed=ok,

            rationale=f"Has >= {min_support} supporting evidence items." if ok else "Insufficient supporting evidence.",

            details={"support_count": len(claim.evidence_ids), "required": min_support},

        )

    return _v



def no_unresolved_contradiction_validator() -> Validator:

    def _v(claim: Claim, store: EvidenceStore, ledger: EGLedger) -> ValidatorResult:

        contradictions = ledger.find_contradictions(claim)

        # resolved if we have explicit contradict edges from claim to each conflicting claim

        contradicted_ids = {e.dst_id for e in ledger.edges.values()

                            if e.edge_type == EdgeType.CONTRADICTS and e.src_id == claim.claim_id}

        unresolved = [c.claim_id for c in contradictions if c.claim_id not in contradicted_ids]

        ok = len(unresolved) == 0

        return ValidatorResult(

            name="no_unresolved_contradictions",

            passed=ok,

            rationale="No unresolved contradictions in-branch." if ok else f"Unresolved contradictions: {unresolved}",

            details={"unresolved": unresolved},

        )

    return _v



def freshness_validator(max_age_days: int = 90) -> Validator:

    def _v(claim: Claim, store: EvidenceStore, ledger: EGLedger) -> ValidatorResult:

        # Treat observed_at for evidence as freshness anchors

        if not claim.evidence_ids:

            return ValidatorResult(

                name="freshness",

                passed=False,

                rationale="No evidence to assess freshness.",

                details={}

            )

        newest = None

        for eid in claim.evidence_ids:

            ev = store.get(eid)

            if newest is None or ev.observed_at > newest:

                newest = ev.observed_at

        # crude day calc: compare dates by parsing ISO

        now = datetime.now(timezone.utc)

        dt_newest = datetime.fromisoformat(newest.replace("Z", "+00:00"))

        age_days = (now - dt_newest).days

        ok = age_days <= max_age_days

        return ValidatorResult(

            name="freshness",

            passed=ok,

            rationale=f"Evidence age {age_days}d within threshold." if ok else f"Evidence too old ({age_days}d).",

            details={"age_days": age_days, "max_age_days": max_age_days, "newest_evidence_at": newest},

        )

    return _v



# -----------------------------

# Epistemic scoring (Vector 1)

# -----------------------------

def compute_epistemic_state(claim: Claim, store: EvidenceStore, ledger: EGLedger) -> EpistemicState:

    # Consensus: how aligned are supporting sources (toy: average authority, penalize counters)

    support = [store.get(eid) for eid in claim.evidence_ids]

    counter = [store.get(eid) for eid in claim.counter_evidence_ids] if claim.counter_evidence_ids else []

    if not support:

        return EpistemicState(

            confidence=0.0,

            consensus=0.0,

            volatility=1.0,

            uncertainty_type=UncertaintyType.MISSING_EVIDENCE,

            notes=["No supporting evidence."]

        )


    avg_auth = sum(e.authority for e in support) / len(support)

    counter_weight = sum(e.authority for e in counter) / max(1, len(counter)) if counter else 0.0


    # Contradiction density: count internal contradictions in branch

    contradictions = ledger.find_contradictions(claim)

    contradiction_penalty = min(0.8, 0.2 * len(contradictions))


    # Freshness proxy: newer evidence => lower volatility

    newest = max(e.observed_at for e in support)

    dt_newest = datetime.fromisoformat(newest.replace("Z", "+00:00"))

    age_days = (datetime.now(timezone.utc) - dt_newest).days

    volatility = min(1.0, age_days / 180.0)  # older -> more volatile (toy)


    # Consensus: authority reduced by counterweight and contradictions

    consensus = max(0.0, min(1.0, avg_auth - 0.5 * counter_weight - contradiction_penalty))


    # Confidence: consensus capped by validator passes

    pass_ratio = 0.0

    if claim.validators:

        pass_ratio = sum(1 for v in claim.validators if v.passed) / len(claim.validators)

    confidence = max(0.0, min(1.0, consensus * (0.5 + 0.5 * pass_ratio)))


    if contradictions:

        utype = UncertaintyType.CONFLICTING_SOURCES

    elif age_days > 90:

        utype = UncertaintyType.STALE

    else:

        utype = UncertaintyType.INFERENCE_UNDER_CONSTRAINTS


    notes = []

    if contradictions:

        notes.append(f"{len(contradictions)} in-branch contradictory claim(s) detected.")

    if counter:

        notes.append(f"{len(counter)} counter-evidence item(s) attached.")

    notes.append(f"Newest evidence age: {age_days}d")


    return EpistemicState(

        confidence=confidence,

        consensus=consensus,

        volatility=volatility,

        uncertainty_type=utype,

        notes=notes

    )



# -----------------------------

# Self-limitation policy (Vector 6)

# -----------------------------

@dataclass

class Refusal:

    refused: bool

    reason: str

    required_inputs: List[str] = field(default_factory=list)

    escalation: Optional[str] = None

    graph_conditions: Dict[str, Any] = field(default_factory=dict)



def policy_gate(

    claim: Claim,

    high_stakes: bool,

    min_confidence: float = 0.70,

    max_volatility: float = 0.60,

) -> Refusal:

    # If high stakes, require stricter confidence + low volatility

    required_conf = min_confidence + (0.10 if high_stakes else 0.0)

    required_vol = max_volatility - (0.10 if high_stakes else 0.0)


    if claim.epistemic.confidence < required_conf:

        return Refusal(

            refused=True,

            reason="Insufficient confidence for publication under policy gate.",

            required_inputs=["more authoritative evidence", "resolve contradictions", "narrow scope/context"],

            escalation="human_review" if high_stakes else "tooling_or_more_evidence",

            graph_conditions={

                "confidence": claim.epistemic.confidence,

                "required_confidence": required_conf,

                "uncertainty_type": claim.epistemic.uncertainty_type,

            },

        )

    if claim.epistemic.volatility > required_vol:

        return Refusal(

            refused=True,

            reason="Volatility too high (likely stale or unstable).",

            required_inputs=["fresh evidence", "time-bounded claim validity window"],

            escalation="human_review" if high_stakes else "refresh_sources",

            graph_conditions={

                "volatility": claim.epistemic.volatility,

                "required_volatility": required_vol,

            },

        )

    return Refusal(refused=False, reason="OK")



# -----------------------------

# Truth-as-process workflow (Vector 7)

# -----------------------------

@dataclass

class Agent:

    name: str


    def propose(self, query: str, retrieved: List[Evidence], context: Dict[str, Any]) -> Claim:

        # Toy proposer: turn query into a structured proposition

        # In production, you'd call an LLM here and parse into proposition schema.

        subj = context.get("system", "unknown_system")

        prop = {"subject": subj, "predicate": "answers", "object": query}

        c = Claim(

            claim_id=new_id("claim"),

            proposition=prop,

            context=context,

            observed_at=utc_now_iso(),

            valid_from=utc_now_iso(),

            valid_to=None,

            evidence_ids=[e.evidence_id for e in retrieved],

            transforms=[{"agent": self.name, "step": "propose", "query": query, "ts": utc_now_iso()}],

            branch_id=context.get("branch", "main"),

        )

        return c


    def critique(self, claim: Claim, store: EvidenceStore, ledger: EGLedger) -> List[str]:

        issues = []

        if not claim.evidence_ids:

            issues.append("No supporting evidence attached.")

        contradictions = ledger.find_contradictions(claim)

        if contradictions:

            issues.append(f"Potential contradictions exist: {[c.claim_id for c in contradictions]}")

        return issues


    def verify(self, claim: Claim, validators: List[Validator], store: EvidenceStore, ledger: EGLedger) -> None:

        claim.validators = [v(claim, store, ledger) for v in validators]

        claim.epistemic = compute_epistemic_state(claim, store, ledger)



def converge(

    agents: List[Agent],

    claim: Claim,

    store: EvidenceStore,

    ledger: EGLedger,

    validators: List[Validator],

    high_stakes: bool = False,

) -> Tuple[PublishState, Optional[Refusal]]:

    """

    Multi-agent process:

      - each agent critiques

      - verifier runs validators + epistemics

      - policy gate decides publish/refuse

    """

    critique_notes: List[str] = []

    for a in agents:

        critique_notes.extend([f"{a.name}: {n}" for n in a.critique(claim, store, ledger)])


    # Run verification (choose first agent as verifier here)

    agents[0].verify(claim, validators, store, ledger)


    # Attach critique notes into epistemic notes

    claim.epistemic.notes.extend(critique_notes)


    # Policy gate

    refusal = policy_gate(claim, high_stakes=high_stakes)

    if refusal.refused:

        claim.state = PublishState.REJECTED

        claim.transforms.append({"step": "policy_refusal", "ts": utc_now_iso(), "refusal": asdict(refusal)})

        return (PublishState.REJECTED, refusal)


    # If any validator fails, reject (hard gate)

    failed = [v for v in claim.validators if not v.passed]

    if failed:

        claim.state = PublishState.REJECTED

        claim.transforms.append({

            "step": "validation_reject",

            "ts": utc_now_iso(),

            "failed_validators": [asdict(v) for v in failed]

        })

        return (PublishState.REJECTED, Refusal(

            refused=True,

            reason="Validator gate failed (type error).",

            required_inputs=["fix schema/constraints", "attach sufficient evidence", "resolve contradictions"],

            escalation="repair_loop",

            graph_conditions={"failed_validators": [v.name for v in failed]},

        ))


    claim.state = PublishState.PUBLISHED

    claim.transforms.append({"step": "publish", "ts": utc_now_iso()})

    return (PublishState.PUBLISHED, None)



# -----------------------------

# Drift-aware memory ops (Vector 5)

# -----------------------------

def fork_branch(ledger: EGLedger, from_branch: str, new_branch: str) -> None:

    ledger.branches.setdefault(new_branch, set())

    for cid in ledger.branches.get(from_branch, set()):

        ledger.branches[new_branch].add(cid)



def supersede_claim(ledger: EGLedger, old_claim_id: str, new_claim: Claim) -> None:

    old = ledger.claims[old_claim_id]

    new_claim.supersedes = old_claim_id

    new_claim.drift_lineage.append({

        "ts": utc_now_iso(),

        "type": "supersede",

        "from": old_claim_id,

        "delta": {"proposition": {"from": old.proposition, "to": new_claim.proposition}}

    })

    ledger.add_edge(EdgeType.SUPERSEDES, new_claim.claim_id, old_claim_id, meta={"branch": new_claim.branch_id})



def merge_branches(ledger: EGLedger, src_branch: str, dst_branch: str, note: str = "") -> None:

    ledger.branches.setdefault(dst_branch, set()).update(ledger.branches.get(src_branch, set()))

    # Record merge as a ledger-level event edge (pseudo)

    merge_edge_id = new_id("merge")

    ledger.edges[merge_edge_id] = Edge(

        edge_id=merge_edge_id,

        edge_type=EdgeType.REFINES,

        src_id=src_branch,

        dst_id=dst_branch,

        created_at=utc_now_iso(),

        meta={"note": note, "op": "merge_branches"}

    )



# -----------------------------

# Prototype demo

# -----------------------------

def demo() -> None:

    print("=== EGL Prototype Demo (Vectors 0-7) ===")


    # Evidence store

    store = EvidenceStore()

    ev1 = Evidence(

        evidence_id=new_id("ev"),

        title="Runbook: Autoscaling thresholds",

        source="internal_runbook",

        authority=0.85,

        observed_at=utc_now_iso(),

        content="Autoscaling triggers when CPU > 70% for 5m. Use unified policy across clouds.",

        tags=["ops", "autoscaling", "policy"],

    )

    ev2 = Evidence(

        evidence_id=new_id("ev"),

        title="Policy: Multi-cloud routing guardrails",

        source="internal_policy",

        authority=0.90,

        observed_at=utc_now_iso(),

        content="Requests must pass schema normalization and entropy checks before routing to any provider.",

        tags=["ops", "multicloud", "entropy"],

    )

    store.add(ev1)

    store.add(ev2)


    # Ledger

    ledger = EGLedger()


    # Agents (TPA)

    agents = [Agent("proposer"), Agent("critic"), Agent("verifier")]


    # Query (could be an incoming incident, question, or decision)

    query = "Should we scale out across AWS and GCP for this traffic burst?"

    context = {"domain": "ops", "system": "edge-broker", "jurisdiction": "N/A", "branch": "main"}


    # Vector 0: retrieval

    retrieved = store.retrieve(query, k=5)


    # Propose a claim

    claim = agents[0].propose(query, retrieved, context)


    # Add claim to ledger early as draft (so contradictions can be detected)

    ledger.add_claim(claim)


    # Add provenance edges claim -> evidence

    for eid in claim.evidence_ids:

        ledger.add_edge(EdgeType.DERIVED_FROM, claim.claim_id, eid, meta={"role": "supporting_evidence"})


    # Validators (Vector 3)

    validators = [

        schema_validator(["subject", "predicate", "object"]),

        evidence_coverage_validator(min_support=1),

        freshness_validator(max_age_days=90),

        no_unresolved_contradiction_validator(),

    ]


    # Converge / publish via process (Vector 7 + Vector 6)

    state, refusal = converge(

        agents=agents,

        claim=claim,

        store=store,

        ledger=ledger,

        validators=validators,

        high_stakes=True,  # treat ops decisions as high stakes

    )


    print(f"\nPublish state: {state}")

    if refusal:

        print("Refusal:", json.dumps(asdict(refusal), indent=2))

    else:

        print("Published claim epistemics:", json.dumps(asdict(claim.epistemic), indent=2))


    # Demonstrate temporal truth modeling (Vector 2): supersede with new policy

    # (simulate future update)

    print("\n--- Temporal update: new policy changes threshold ---")

    fork_branch(ledger, "main", "policy_update")

    updated_context = dict(context)

    updated_context["branch"] = "policy_update"


    # New evidence (later time)

    ev3 = Evidence(

        evidence_id=new_id("ev"),

        title="Updated Runbook: Autoscaling thresholds v2",

        source="internal_runbook",

        authority=0.92,

        observed_at=utc_now_iso(),

        content="Autoscaling triggers when CPU > 60% for 3m. Coordinated scaling requires entropy gate pass.",

        tags=["ops", "autoscaling", "policy", "v2"],

    )

    store.add(ev3)


    retrieved2 = store.retrieve("updated autoscaling threshold", k=5)

    claim2 = agents[0].propose("Autoscaling threshold updated to CPU>60% for 3m", retrieved2, updated_context)

    ledger.add_claim(claim2)

    supersede_claim(ledger, old_claim_id=claim.claim_id, new_claim=claim2)


    # Validate and publish

    for eid in claim2.evidence_ids:

        ledger.add_edge(EdgeType.DERIVED_FROM, claim2.claim_id, eid, meta={"role": "supporting_evidence"})


    state2, refusal2 = converge(agents, claim2, store, ledger, validators, high_stakes=False)

    print(f"Publish state (updated claim): {state2}")

    if refusal2:

        print("Refusal:", json.dumps(asdict(refusal2), indent=2))


    # Demonstrate queryable slices (EGL output as graph slice)

    print("\n--- EGL Slice (as-of now, branch=policy_update) ---")

    slice_claims = [c for c in ledger.claims.values()

                    if c.state == PublishState.PUBLISHED and c.branch_id == "policy_update"]

    for c in slice_claims:

        print(json.dumps({

            "claim_id": c.claim_id,

            "proposition": c.proposition,

            "valid_from": c.valid_from,

            "valid_to": c.valid_to,

            "supersedes": c.supersedes,

            "epistemic": asdict(c.epistemic),

            "validators": [asdict(v) for v in c.validators],

        }, indent=2))


    # Artifact seal: hash entire ledger snapshot (PNC + audit)

    snapshot = {

        "ts": utc_now_iso(),

        "claims": {cid: asdict(c) for cid, c in ledger.claims.items()},

        "edges": {eid: asdict(e) for eid, e in ledger.edges.items()},

        "branches": {b: sorted(list(ids)) for b, ids in ledger.branches.items()},

    }

    print("\n--- Ledger Snapshot Seal ---")

    print("timestamp_utc:", snapshot["ts"])

    print("sha256:", stable_hash(snapshot))



if __name__ == "__main__":

    demo()


What this prototype gives you (in one breath)

  • EGL substrate (claims + edges + bitemporal fields + lineage)

  • RAG retrieval as a plug-in evidence fetch (Vector 0)

  • EST computed epistemics (confidence/consensus/volatility/uncertainty)

  • TTM via validity windows + supersession

  • CBG via hard validators (reject = “type error”)

  • PNC via explicit evidence links + derivation steps + sealed snapshot hash

  • DAM via branching + explicit merges/supersession (no overwrite)

  • MSLP via policy gates that refuse/escalate

  • TPA via multi-agent propose/critique/verify/converge workflow

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