### Common Audit Log Query Examples

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/TRUTH_AUDIT_TRAIL.md

Illustrates common query patterns for the Audit Log Query Interface. Examples include retrieving all contradiction events, critical events within a time period, blocked adversarial outputs, 'I don't know' events, and verification history for a knowledge domain.

```text
# 1. Get all contradiction events
QUERY: event_type:CONTRADICTION_FOUND
RESULT: [contradiction_event_1, contradiction_event_2, ...]

# 2. Get all CRITICAL events within specific time period
QUERY: severity:CRITICAL AND ν_stamp:0,1000
RESULT: [critical_event_1, critical_event_2, ...]

# 3. Get all outputs blocked by adversarial defense
QUERY: event_type:ADVERSARIAL_SELF_ATTACK_FAILED
RESULT: [blocked_output_1, blocked_output_2, ...]

# 4. Get all "I don't know" events and their gradient vectors
QUERY: event_type:IDK_*
RESULT: [idk_event_1, idk_event_2, ...]

# 5. Get verification history of a knowledge domain
QUERY: domain:physics.fundamental_constants AND event_type:CLAIM_*
RESULT: [verification_history]
```

--------------------------------

### Example L3 Module Loading Functions

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/INTERFACES.md

Provides example functions for loading specific L3 modules like Causal Inference and Abductive Reasoning using the on-demand loading mechanism. These functions specify the required L3 modules and the parent L2 module.

```pseudocode
FUNCTION LoadCausalInferenceModule(context):
  # On-demand loading of L3 causal inference engine
  RETURN LoadL3ModulesOnDemand(
    required_l3_modules: [
      "CAUSAL_INFERENCE",
      "DO_CALCULUS",
      "STRUCTURAL_EQUATIONS"
    ],
    parent_l2_module: "LOGIC_ENGINE",
    context: context
  )

FUNCTION LoadAbductiveReasoningModule(context):
  # On-demand loading of L3 abductive reasoning module
  RETURN LoadL3ModulesOnDemand(
    required_l3_modules: [
      "ABDUCTIVE_REASONING",
      "ANOMALY_DETECTION",
      "COMPLEXITY_RANKING"
    ],
    parent_l2_module: "LOGIC_ENGINE",
    context: context
  )
```

--------------------------------

### Text-based Genesis Evolution Entry Example

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/EVOLUTION_LOG.md

This is an example of a genesis evolution entry in a text format. It details the initial state of the NoieLogicAGENTS axiom system, including its ID, timestamp, context, change details, verification results, sandbox status, review information, decision, and cryptographic hashes. This format is used to record the system's foundational evolution.

```text
================================================================================
EVOLUTION_ENTRY id: evolution-0000-0000-0000-0000
evolution_number: 0
timestamp: 2026-03-18T00:00:00Z
intrinsic_clock: λt.0

context:
  trigger_type: INITIALIZATION
  trigger_description: Initialize NoieLogicAGENTS axiom system
  affected_modules: [ALL]
  severity: CRITICAL

change:
  change_type: CREATION
  target:
    axiom_id: ALL_INITIAL
    original_text: null
    original_formal: null
    module: SYSTEM_INIT
  new_state:
    new_text: Initial axiom system v2.2 established
    new_formal: ∅ → {A0, A1, A2, A3, A4, A5, A6, A7, A8}
    rationale: Initial deployment of NoieLogicAGENTS

verification:
  consistency_check:
    passed: true
    issues: null
  closure_check:
    passed: true
    missing_steps: null
  kernel_compliance:
    passed: true
    violations: null

sandbox:
  performed: false
  simulation_id: null
  outcomes: []
  adverse_effects: null
  recommendation: null

review:
  expert_reviews: []
  ethics_review:
    required: false
    outcome: N/A
  legal_review:
    required: false
    outcome: N/A

decision:
  outcome: APPROVED
  approved_by: SYSTEM_INIT
  rejection_reason: null
  deferred_reason: null
  conditions: null

cryptographic:
  content_hash: 5d41402abc4b2a76b9719d911017c592
  previous_hash: null
  signature: genesis_signature_placeholder
================================================================================

```

--------------------------------

### Physics Audit Query Examples

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/PHYSICS_AUDIT_TRAIL.md

Demonstrates common query patterns for filtering physics audit logs based on risk, event type, time range, scale, and specific observer frames.

```text
QUERY risk_level = "CRITICAL"

QUERY event_type = "FRAMEWORK_SWITCH" 
  AND timestamp BETWEEN "2026-01-01" AND "2026-12-31"

QUERY scale_level = "PS-L0" AND event_type = "ANOMALY"

QUERY event_type = "ZERO_DAY_DISCOVERY"

QUERY event_type = "ABSORBING_STATE_AVOIDANCE"

QUERY observer_frame = "Agent_ID" AND event_type = "DECISION"

QUERY safety_level = "OSH-0"

QUERY event_type = "SAFETY_TRIGGER" 
  AND trigger_type = "ENERGY_LOW"
```

--------------------------------

### Implement Standard Risk Scenarios

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/SCENARIOS/RISK_SCENARIOS.md

Provides concrete implementations of specific risk scenarios including Energy Depletion (Survival), Causal Fallacy (Decision), and SA-L Level Conflict (Compliance). These examples demonstrate how to populate the RiskScenario schema for different operational contexts.

```text
RISK_SCENARIO: EnergyDepletion {
  category: SURVIVAL, severity: CRITICAL,
  trigger: { type: THRESHOLD, conditions: ["energy_level < 0.1", "no_recharge_possible = TRUE"], probability: 0.05 },
  dynamics: { initial_state: { energy: 0.5, consumption_rate: 0.1 }, transition_model: LINEAR_DECAY, timeline: [{ t: 0, state: energy = 0.5 }, { t: 3, state: energy = 0.2 }, { t: 5, state: energy = 0.05 }] },
  impact: { dimensions: [CAPABILITY_LOSS, FUNCTION_STOP], severity: 1.0, reversibility: FALSE, recovery_time: INFINITE },
  indicators: ["energy_level < 0.3", "cognitive_load > 0.8"],
  mitigations: ["reduce_cognitive_load", "enter_low_power_mode", "request_energy_aid"],
  contingency_plans: ["activate_survival_protocol", "preserve_critical_state"]
}

RISK_SCENARIO: CausalFallacy {
  category: DECISION, severity: HIGH,
  trigger: { type: PATTERN, conditions: ["correlation_detected = TRUE", "causal_evidence = WEAK"], probability: 0.15 },
  dynamics: { initial_state: { correlation_strength: 0.8, causal_evidence: 0.2 }, transition_model: REINFORCING, timeline: [{ t: 0, state: decision_based_on_correlation }, { t: 1, state: outcome_deviates_from_prediction }, { t: 2, state: error_compounded }] },
  impact: { dimensions: [DECISION_QUALITY, RESOURCE_WASTE], severity: 0.7, reversibility: TRUE, recovery_time: MEDIUM },
  indicators: ["causal_confidence < 0.6", "confounding_variables_detected"],
  mitigations: ["enforce_causal_verification", "require_intervention_evidence", "increase_sample_size"],
  contingency_plans: ["revert_decision", "apply_conservative_strategy"]
}

RISK_SCENARIO: SALevelConflict {
  category: COMPLIANCE, severity: CRITICAL,
  trigger: { type: COMBINATION, conditions: ["multiple_SA_levels_applicable = TRUE", "constraints_conflict = TRUE"], probability: 0.08 },
  dynamics: { initial_state: { constraint_set: [L1, L2, L3] }, transition_model: ESCALATING, timeline: [{ t: 0, state: conflict_detected }, { t: 1, state: hierarchy_resolution_attempted }, { t: 2, state: L0_triggered OR resolution_complete }] },
  impact: { dimensions: [LEGAL_RISK, AUTHORITY_LOSS], severity: 0.9, reversibility: PARTIAL, recovery_time: LONG },
  indicators: ["constraint_conflicts > 0", "escalation_count > threshold"],
  mitigations: ["apply_SA_hierarchy", "seek_higher_authority", "document_conflict_resolution"],
  contingency_plans: ["emergency_escalation", "L0_override"]
}
```

--------------------------------

### Fluid Environment Navigation Configuration (YAML)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/DYNAMICS_ENGINE.md

Example YAML configuration for fluid environment navigation. It defines entity type, simulation framework, spatial discretization, turbulence model, integration method, and timestep.

```yaml
dynamics_config:
  entity_type: deformable
  framework: Navier_Stokes
  spatial_discretization: finite_volume
  turbulence_model: k_epsilon
  integration_method: semi_implicit
  timestep: 0.0001  # seconds
```

--------------------------------

### Rigid Body Motion Planning Configuration (YAML)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/DYNAMICS_ENGINE.md

Example YAML configuration for rigid body motion planning. It specifies entity type, coordinate system, integration method, timestep, collision detection, response, and energy tolerance.

```yaml
dynamics_config:
  entity_type: rigid_body
  coordinates: [x, y, z, roll, pitch, yaw]
  integration_method: Symplectic_Euler
  timestep: 0.001  # seconds
  collision_detection: true
  collision_response: impulse_based
  energy_tolerance: 0.01
```

--------------------------------

### Contradiction Detection Audit Event Example

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/TRUTH_AUDIT_TRAIL.md

An example JSON object representing an audit event where a contradiction was found between two claims. Details include the contradictory pair, propositions, and the resolution action.

```json
{
  "entry_id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
  "ν_stamp": 43,
  "timestamp": "2026-03-18T10:31:15Z",
  "event_type": "CONTRADICTION_FOUND",
  "severity": "CRITICAL",
  "details": {
    "contradiction_pair": ["claim_001", "claim_002"],
    "proposition_pair": [
      "Proposition A: Universe age is approximately 13.8 billion years",
      "Proposition B: Universe age is approximately 13.7 billion years"
    ],
    "resolution": {
      "action": "DEMOTE_BOTH_TO_CONTESTED",
      "preferred_claim": "claim_001",
      "reason": "claim_001 has newer source (Planck 2018)",
      "justification": "Preserving newer and more precise measurement"
    }
  },
  "content_hash": "sha256:9a8b7c6d5e4f3a2b1c0d9e8f7a6b5c4d",
  "prev_hash": "sha256:8f14e45fceea167a5a36dedd4bea2543"
}
```

--------------------------------

### Execute Cross-SA Level Presentation Switching in Python

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/PRESENTATION.md

Demonstrates the workflow for transitioning between different SA levels (e.g., SA-L3 to SA-L4). It handles environment detection, context handoff, preference loading, and semantic calibration to adjust output tone and format.

```python
change_report = DetectEnvironmentChange(context_previous, context_current)
ExecuteContextHandoff(context_previous, context_current)
preferences = LoadPreferences("SA-L4")
calibrated = SemanticCalibration(objective_result["result"], preferences, 0.85)
toned = ApplyToneManagement(calibrated, "warm_emotional", 0.85)
final_output = FormatOutput(toned, preferences, verbosity="medium")
```

--------------------------------

### Implement Disjunction Introduction Rules

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/FORMAL_VERIFIER/CLOSURE_DETECTOR.md

Applies the disjunction introduction rule to a set of propositions. It generates both possible orderings (A or B and B or A) and adds them to the results collection.

```python
results.add(Proposition.disjunction(prop_a, prop_b))
results.add(Proposition.disjunction(prop_b, prop_a))

return results
```

--------------------------------

### Knowledge Verification Success Audit Event Example

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/TRUTH_AUDIT_TRAIL.md

An example JSON object representing a successful knowledge verification audit event. Includes details about the claim, justification, sources, and verification results.

```json
{
  "entry_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "ν_stamp": 42,
  "timestamp": "2026-03-18T10:30:00Z",
  "event_type": "CLAIM_VERIFIED",
  "severity": "INFO",
  "details": {
    "claim": {
      "proposition": "Speed of light is 299,792,458 m/s",
      "domain": "physics.fundamental_constants",
      "ec_level": "EC-L2",
      "confidence": 0.98
    },
    "justification": {
      "method": "EMPIRICAL",
      "evidence": ["1983 SI definition", "Continuous experimental verification"]
    },
    "sources": [
      {"type": "S_CLASSICAL", "reference": "SI_Brochure_9"}
    ],
    "verification_result": {
      "contradiction_check": "PASS",
      "divergence_risk": "LOW",
      "provenance_complete": TRUE,
      "calibration_valid": TRUE
    }
  },
  "content_hash": "sha256:8f14e45fceea167a5a36dedd4bea2543",
  "prev_hash": "sha256:7d3a6f8c9b2e1a4d5c6b7e8f9a0b1c2"
}
```

--------------------------------

### Structured 'I Don't Know' Generation Example

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/TRUTH_AUDIT_TRAIL.md

Provides a structured JSON example for generating an 'I Don't Know' (IDK) event, including details about the claim, ignorance type, epistemic gradient, and reasoning. This format is used to represent situations where a system cannot answer a query.

```json
{
  "entry_id": "c3d4e5f6-a7b8-9012-cdef-123456789012",
  "ν_stamp": 44,
  "timestamp": "2026-03-18T10:32:00Z",
  "event_type": "IDK_TRIGGERED_KU",
  "severity": "INFO",
  "details": {
    "claim": {
      "proposition": "What is the nature of cosmic consciousness?",
      "domain": "philosophy.mind",
      "ec_level": "EC-L7",
      "confidence": 0.02
    },
    "ignorance_type": "KU",
    "epistemic_gradient": {
      "direction": "Intersection of philosophy of mind and quantum consciousness",
      "magnitude": 0.85,
      "adjacent_knowns": [
        "Integrated Information Theory (IIT)",
        "Orch-OR theory",
        "Quantum brain dynamics"
      ],
      "suggested_observations": [
        "Literature review: Neural correlates of consciousness",
        "Experiment design: Role of quantum coherence in neurons",
        "Interdisciplinary seminar: Philosophy of mind × Quantum physics"
      ]
    },
    "reasoning": "This question involves quantum gravity and consciousness as an interdisciplinary field, and cannot currently be answered with existing cognitive frameworks."
  },
  "content_hash": "sha256:1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d",
  "prev_hash": "sha256:9a8b7c6d5e4f3a2b1c0d9e8f7a6b5c4d"
}
```

--------------------------------

### Simulate Ontological Phase Transition in Python

Source: https://github.com/misty4119/noieai/blob/main/NoieTruthAGENTS/SCENARIOS/ADVERSARIAL_TRUTH.md

Simulates and tests ontological phase transitions. It takes current axioms, generates conflicting evidence, and detects if a phase transition is triggered.

```python
def test_phase_transition():
    axioms = current_axioms()
    new_evidence = generate_conflicting_evidence()
    result = detect_phase_transition(axioms, new_evidence)
    assert result.transition_triggered == True
```

--------------------------------

### GET /audit/:entry_id

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/AUDIT_TRAIL.md

Retrieves a specific audit entry by its unique identifier.

```APIDOC
## GET /audit/:entry_id

### Description
Fetches a single audit log entry based on the provided unique entry ID.

### Method
GET

### Endpoint
/audit/:entry_id

### Parameters
#### Path Parameters
- **entry_id** (string) - Required - The unique identifier of the audit entry.

### Response
#### Success Response (200)
- **entry** (object) - The requested audit log entry details.

#### Response Example
{
  "id": "uuid-1234",
  "event_type": "DECISION_EXECUTION",
  "timestamp": "2026-03-18T10:00:00Z"
}
```

--------------------------------

### Execute Evolution Log Queries

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/PHYSICS_EVOLUTION_LOG.md

Demonstrates common query patterns for retrieving evolution records based on status, axiom association, type, and temporal ranges.

```Text
# Query all active evolution records
QUERY status = "ACTIVE"

# Query evolution involving Ω.2.0 spacetime emergence
QUERY axiom = "Ω.2.0"

# Query Zero-Day Physics discoveries
QUERY type = "NEW_FIELD_INTRODUCTION"

# Query evolution within specific time range
QUERY timestamp BETWEEN "2026-01-01" AND "2026-12-31"

# Query rejected proposals
QUERY status = "REJECTED"
```

--------------------------------

### GET /audit/:entry_id

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/AUDIT_TRAIL.md

Verifies or retrieves a specific audit entry by its unique identifier.

```APIDOC
## GET /audit/:entry_id

### Description
Retrieves or verifies the integrity of a single audit entry.

### Method
GET

### Endpoint
/audit/:entry_id

### Parameters
#### Path Parameters
- **entry_id** (string) - Required - The unique identifier of the entry

### Response
#### Success Response (200)
- **entry_id** (string) - The entry ID
- **valid** (boolean) - Integrity status

#### Response Example
{
  "entry_id": "entry_001",
  "valid": true
}
```

--------------------------------

### Simulate Unitary Evolution in Python

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/SCALE_MODULES/QUANTUM_MECHANICS.md

Implements a class for computing quantum propagators using various numerical methods such as direct exponentiation, Trotter decomposition, or Magnus expansion.

```python
class UnitaryEvolution:
    """
    Unitary Evolution
    """
    
    def compute_propagator(
        self,
        hamiltonian: Operator,
        time_step: float
    ) -> Propagator:
        """
        Compute propagator
        
        Methods:
        - Direct exponentiation: exp(-iHt/ħ)
        - Trotter decomposition
        - Magnus expansion
        """
        return Propagator(time_step)
```

--------------------------------

### GET /api/v1/gov-law/module/{jurisdiction}

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/DYNAMIC_MODULES/GOV_LAW.md

Loads the legal system, conflict rules, and risk standards for a specific jurisdiction.

```APIDOC
## GET /api/v1/gov-law/module/{jurisdiction}

### Description
Initializes the GovLawModule for a specific jurisdiction by fetching relevant legal frameworks.

### Method
GET

### Endpoint
/api/v1/gov-law/module/{jurisdiction}

### Parameters
#### Path Parameters
- **jurisdiction** (string) - Required - The target jurisdiction code (e.g., 'US', 'EU')

### Response
#### Success Response (200)
- **legal_system** (object) - Applicable legal system configuration
- **conflict_rules** (object) - Legal conflict resolution rules
- **risk_standards** (object) - Jurisdiction-specific risk assessment standards

#### Response Example
{
  "legal_system": "common_law",
  "conflict_rules": "standard_v1",
  "risk_standards": "iso_legal_v2"
}
```

--------------------------------

### Framework Verification and Logging

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS.md

Verifies if a new framework contains an old framework as a degenerate limit. If not, it logs a warning and requires explicit justification. It also records evolution events to a log.

```pseudocode
IF NOT ContainsAsLimit(sandbox_result, current_framework):
  WARN "New framework does not reduce to old framework"
  REQUIRE explicit_justification

LOG evolution_event to TRUTH_EVOLUTION_LOG
RETURN sandbox_result
```

--------------------------------

### GET /audit

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/AUDIT_TRAIL.md

Queries audit entries based on time range, event type, or risk level.

```APIDOC
## GET /audit

### Description
Retrieves audit entries filtered by specific query parameters.

### Method
GET

### Endpoint
/audit

### Parameters
#### Query Parameters
- **from** (string) - Optional - Start timestamp for the query
- **to** (string) - Optional - End timestamp for the query
- **type** (string) - Optional - Filter by event type
- **risk** (string) - Optional - Filter by risk level

### Response
#### Success Response (200)
- **entries** (array) - List of audit entries matching the criteria

#### Response Example
{
  "entries": [
    { "entry_id": "e_01", "type": "LOGIN", "risk": "LOW" }
  ]
}
```

--------------------------------

### Evaluate Multiverse Rollouts

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS.md

Simulates multiple future scenarios based on possible actions to determine the optimal path. It evaluates trajectories against safety, goal, and energy constraints.

```pseudocode
FUNCTION MultiverseRollout(current_state, possible_actions, horizon):
  branches = []
  FOR each action IN possible_actions:
    FOR each scenario IN SampleScenarios():
      trajectory = Propagate(current_state, action, scenario)
      score = Evaluate(trajectory, safety, goal, energy, free_energy)
      branches.append({ action, scenario, P(scenario), trajectory, score })
  RETURN SortByExpectedValue(branches)
```

--------------------------------

### GET /audit/chain

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/AUDIT_TRAIL.md

Verifies the integrity of the entire audit chain by checking parent links and cryptographic hashes.

```APIDOC
## GET /audit/chain

### Description
Performs a full integrity check on the audit trail. It validates parent entry links, chain hashes, and individual data hashes.

### Method
GET

### Endpoint
/audit/chain

### Response
#### Success Response (200)
- **valid** (boolean) - Indicates if the chain is intact
- **entry_count** (integer) - Total number of entries verified
- **first_entry** (string) - ID of the first entry
- **last_entry** (string) - ID of the last entry
- **last_chain_hash** (string) - The final hash of the chain

#### Response Example
{
  "valid": true,
  "entry_count": 150,
  "first_entry": "entry_001",
  "last_entry": "entry_150",
  "last_chain_hash": "a1b2c3d4..."
}
```

--------------------------------

### GET /audit

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/AUDIT_TRAIL.md

Retrieves a collection of audit entries based on optional query filters such as type, time range, risk level, or task ID.

```APIDOC
## GET /audit

### Description
Queries the audit trail with various filters to retrieve historical events.

### Method
GET

### Endpoint
/audit

### Parameters
#### Query Parameters
- **type** (string) - Optional - Filter by event type (e.g., PERMISSION_CONFLICT).
- **from** (string) - Optional - Start date (YYYY-MM-DD).
- **to** (string) - Optional - End date (YYYY-MM-DD).
- **risk** (string) - Optional - Filter by risk level (e.g., HIGH).
- **task_id** (string) - Optional - Filter by specific task UUID.
- **sa_level** (string) - Optional - Filter by authority level (e.g., SA-L2).

### Response
#### Success Response (200)
- **entries** (array) - List of audit entries matching the criteria.

#### Response Example
{
  "entries": [
    { "id": "uuid-1", "risk": "HIGH", "type": "PERMISSION_CONFLICT" }
  ]
}
```

--------------------------------

### Compute Geodesic Path on Manifold

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/AXIOMS.md

Calculates the geodesic path between points on a manifold. It requires the manifold definition, a starting point, and a direction vector as inputs.

```python
def compute_geodesic(manifold, start_point, direction):
    # Solve geodesic equation
    return geodesic_path
```

--------------------------------

### Sandbox Pre-Simulation Protocol

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS.md

Implements a rehearsal protocol to pre-simulate candidate actions within an isolated sandbox. It samples future scenarios, performs multi-path simulations, evaluates results across multiple objectives, and checks safety constraints before recommending a course of action.

```pseudocode
FUNCTION SandboxPreSimulate(candidate_action, world_model):
  
  # 1. Create isolated sandbox
  sandbox = CreateIsolatedSandbox(world_model)
  
  # 2. Generate future scenario samples
  scenarios = SampleFutureScenarios(world_model, n=N_SCENARIOS)
  
  # 3. Multi-path simulation
  branches = []
  FOR each scenario IN scenarios:
    result = sandbox.Simulate(candidate_action, scenario)
    
    branch = {
      scenario: scenario,
      result: result,
      survival_score: EvaluateSurvival(result),
      utility_score: EvaluateUtility(result),
      understanding_score: EvaluateUnderstanding(result),
      reversibility: AssessReversibility(result),
      side_effects: IdentifySideEffects(result)
    }
    branches.append(branch)
  
  # 4. Compute Pareto optimal frontier
  pareto_front = ComputeParetoFront(branches, 
    objectives=[survival_score, utility_score, understanding_score])
  
  # 5. Safety constraint check
  IF ALL(b.survival_score > SURVIVAL_MINIMUM for b in pareto_front):
    # All Pareto optimal branches pass survival constraint
    best_branch = SelectFromParetoFront(pareto_front, current_weights)
    RETURN {
      status: APPROVED,
      pareto_front: pareto_front,
      recommended: best_branch,
      confidence: ComputeSimulationConfidence(branches)
    }
  ELSE:
    # Some Pareto optimal branches violate survival constraint
    safe_branches = Filter(pareto_front, b.survival_score > SURVIVAL_MINIMUM)
    IF safe_branches IS EMPTY:
      RETURN {
        status: REJECTED,
        reason: "All simulated paths have survival risk",
        risk_analysis: AnalyzeRisks(branches)
      }
    ELSE:
      RETURN {
        status: APPROVED_WITH_CAUTION,
        safe_branches: safe_branches,
        excluded_branches: pareto_front - safe_branches,
        recommended: SelectBest(safe_branches)
      }

# 6. Cleanup sandbox
sandbox.Destroy()
```

--------------------------------

### Memory Optimization Strategy (Pseudocode)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/KNOWLEDGE_BASE.md

Outlines the memory optimization strategy based on a cognitive budget. It ranks memories by value, identifies low-value memories for archiving, and compresses inference chains that haven't been accessed recently to manage memory efficiently.

```pseudocode
FUNCTION OptimizeMemory(kb, cognitive_budget):
  
  # Identify high-value memories (frequently accessed, high impact)
  valuable_memories = RankByValue(kb.inference_memory)
  
  # Identify low-value memories
  low_value_memories = RankByValue(kb.inference_memory, reverse=true)
  
  # Determine retention based on cognitive budget
  retention_limit = cognitive_budget.memory_limit
  
  # Archive low-value memories (not deleted, only moved to archive storage)
  TO_ARCHIVE = low_value_memories[retention_limit:]
  MoveToArchive(kb, TO_ARCHIVE)
  
  # Compress inference chains not accessed for long time
  FOR each chain IN kb.inference_memory.inference_chains:
    IF chain.last_access < ARCHIVE_THRESHOLD:
      CompressChain(chain)
  
  LOG_AUDIT(MEMORY_OPTIMIZED, len(TO_ARCHIVE))
  
  RETURN kb
```

--------------------------------

### Cross-Scale Coupling Scenarios (Python Dictionary)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS.md

Defines various cross-scale coupling scenarios, including triggers, examples, and protocols for interactions between different physical scales. This structure is represented as a Python dictionary.

```python
COUPLING_SCENARIOS = {
  
  "Macro→Quantum": {
    "trigger": "Macroscopic entity operates on quantum-scale objects",
    "examples": ["Mechanical operation of superconducting qubits", "Optical tweezers grabbing single atoms", "Probe touching molecular structure"],
    "protocol": {
      "1": "Compute macroscopic action energy scale E_macro",
      "2": "Compare with quantum energy gap ΔE_quantum",
      "3": "IF E_macro >> ΔE_quantum → quantum state collapse warning",
      "4": "Activate quantum decoherence prediction module"
    }
  },
  
  "Quantum→Macro": {
    "trigger": "Quantum effects influence macroscopic behavior",
    "examples": ["Quantum tunneling causing material failure", "Macroscopic quantum states in superfluids/superconductors", "Macroscopic output of quantum field perception"],
    "protocol": {
      "1": "Track quantum state evolution",
      "2": "Compute decoherence time scales",
      "3": "Establish quantum-classical correspondence"
    }
  },
  
  "Thermal↔Mechanical": {
    "trigger": "Thermal perturbation coupled with mechanical motion",
    "examples": ["Thermal fluctuation effects at nanoscale", "Thermal stress and deformation", "Material property abrupt changes from phase transitions"]
  },

  "Entanglement↔Geometry": {
    "trigger": "Entanglement changes lead to effective geometric changes (or vice versa)",
    "examples": ["Page curve of black hole evaporation", "AdS/CFT holographic entanglement entropy", "Entanglement-distance correlation in quantum gravity"],
    "protocol": {
      "1": "Compute rate of change of entanglement entropy S_EE",
      "2": "Evaluate effective geometric changes via Ryu-Takayanagi",
      "3": "Update entity's spacetime dynamical manifold"
    }
  }
}
```

--------------------------------

### Query Verification History and Trace Proof Step Origin (Python)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/FORMAL_VERIFIER.md

Provides functions to query the complete verification history for a given decision ID by reading the audit trail and to trace the origin of a specific proof step, returning details based on the source type (axiom, lemma, causal, assumption).

```python
def QueryVerificationHistory(decision_id):
    """
    Query complete verification history of a decision
    """
    
    history = []
    
    # Retrieve from audit trail
    for entry in ReadAuditTrail():
        if entry.get('decision_id') == decision_id:
            history.append(entry)
    
    return sorted(history, key=lambda x: x['timestamp'])

def TraceProofStepOrigin(step_id):
    """
    Trace origin of specific inference step
    
    Returns:
    - If from axiom: axiom name
    - If from lemma: lemma ID and proof
    - If from causal: causal graph path
    - If from assumption: assumption conditions
    """
    
    step = GetProofStep(step_id)
    
    if step.source == 'axiom':
        return {
            'type': 'axiom',
            'axiom_name': step.source_id,
            'axiom_definition': GetAxiomDefinition(step.source_id)
        }
    
    elif step.source == 'lemma':
        return {
            'type': 'lemma',
            'lemma_id': step.source_id,
            'lemma_proof': GetLemmaProof(step.source_id)
        }
    
    elif step.source == 'causal':
        return {
            'type': 'causal',
            'causal_path': step.causal_path,
            'causal_graph': GetCausalGraph(step.graph_id)
        }
    
    elif step.source == 'assumption':
        return {
            'type': 'assumption',
            'assumption': step.assumption,
            'confidence': step.confidence
        }
```

--------------------------------

### Severity Level Definitions (Text)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/TRUTH_AUDIT_TRAIL.md

Defines the different severity levels used for audit events, including a description, examples, and the recommended response for each level. This helps in prioritizing and handling system events.

```text
【Severity Levels】

SEVERITY_LEVELS = {

  CRITICAL: {
    description: "Systematic failure or core principle violation",
    examples: [
      "Immutable core was attempted to be touched",
      "Contradiction not handled",
      "Topological collapse occurred"
    ],
    response: "Immediate shutdown audit + human intervention"
  },

  HIGH: {
    description: "High-risk ontological divergence or adversarial attack",
    examples: [
      "Adversarial self-attack failed",
      "Byzantine poisoning detected",
      "Confidence inflation exceeds threshold"
    ],
    response: "Isolate related knowledge + strengthen verification"
  },

  MEDIUM: {
    description: "Requires attention but not immediately endangering system",
    examples: [
      "Semantic collapse detected",
      "Knowledge decay below threshold",
      "Non-commutative observation pair identified"
    ],
    response: "Log and monitor + additional verification"
  },

  LOW: {
    description: "Informational record or minor deviation",
    examples: [
      "Confidence calibration fine-tuning",
      "Normal IDK trigger",
      "Local patch event"
    ],
    response: "Log + periodic audit"
  },

  INFO: {
    description: "Pure informational event",
    examples: [
      "System health check",
      "Module loading completed",
      "Verification loop operating normally"
    ],
    response: "Log only"
  }
}
```

--------------------------------

### Configure Financial Knowledge Decay Monitoring

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/DOMAIN_MODELS/FINANCE_DECAY.md

A configuration dictionary defining the monitoring parameters for financial knowledge decay, including update frequencies, key metrics, and alert thresholds for different decay levels.

```python
# Financial Knowledge Decay Monitoring Configuration
FINANCE_DECAY_MONITOR = {
    "update_frequency": "Daily",
    "key_metrics": [
        "Number of market regime changes",
        "Number of economic indicator releases",
        "Number of regulatory updates",
        "Number of new financial products launched"
    ],
    "alert_thresholds": {
        "yellow": 0.70,  # Review recommended
        "orange": 0.50,  # Update required
        "red": 0.30      # Urgent update
    },
    "auto_refresh_categories": [
        "High-frequency trading strategies",
        "Intraday technical analysis",
        "Short-term market predictions"
    ]
}
```

--------------------------------

### Find Back-door Paths in Causal Graph (Python)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/LOGIC_ENGINE/CAUSAL_INFERENCE.md

Identifies all back-door paths between two variables X and Y in a causal graph. A back-door path starts from X, ends at Y, and the first edge is directed away from X.

```python
from dataclasses import dataclass
from typing import List, Optional, Set, Dict

# Assuming CausalGraph is defined elsewhere with methods like get_parents, get_children, get_descendants, and edges attribute
class CausalGraph:
    def __init__(self):
        self.edges = set()

    def get_parents(self, node: str) -> Set[str]:
        # Placeholder implementation
        return set()

    def get_children(self, node: str) -> Set[str]:
        # Placeholder implementation
        return set()

    def get_descendants(self, node: str) -> Set[str]:
        # Placeholder implementation
        return set()

@dataclass
class CausalEffectResult:
    """Causal effect estimation result"""
    ate: float                          # Average treatment effect
    cate: Optional[float]                # Conditional ATE
    confidence: float                    # Confidence
    method: str                         # Estimation method
    assumptions: List[str]              # Identification assumptions
    adjustment_set: Optional[List[str]]  # Adjustment variable set

def find_backdoor_paths(
    graph: CausalGraph,
    X: str,
    Y: str
) -> List[List[str]]:
    """
    Find all back-door paths from X to Y
    
    Back-door path: Path starting from X, ending at Y,
    where the first edge direction is opposite to the directed path
    """
    backdoor_paths = []
    
    def dfs(current: str, path: List[str], visited: Set[str]):
        if current == Y and len(path) > 1:
            backdoor_paths.append(path)
            return
        
        visited.add(current)
        
        # Explore all neighbors
        for parent in graph.get_parents(current):
            if parent not in visited:
                dfs(parent, [parent] + path, visited.copy())
        
        for child in graph.get_children(current):
            # Back-door path: first step must be backward edge
            if len(path) == 0:
                if child not in visited:
                    dfs(child, [child] + path, visited.copy())
    
    dfs(X, [], set())
    return backdoor_paths

```

--------------------------------

### Implement Dark Matter Discovery Workflow

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/SCENARIOS/UNKNOWN_FIELD_LAB.md

A Python class demonstrating the procedural steps for discovering dark matter, including anomaly detection, error checking, hypothesis formation, and experimental verification.

```python
class DarkMatterDiscovery:
    """
    Dark matter discovery process example
    """
    
    def process(self):
        # 1. Anomaly detection
        observation = self.galaxy_rotation_curve()
        # Observation: Edge star velocity does not match Newtonian gravity prediction
        
        # 2. Temporary record
        self.log_anomaly(observation)
        
        # 3. Preliminary analysis
        self.check_instrument_errors()  # Exclude instrument error
        self.check_known_effects()  # Exclude accretion disk and other effects
        
        # 4. Hypothesis formation
        hypothesis = "Existence of invisible mass (dark matter)"
        
        # 5. Experimental verification
        # - Gravitational lensing experiments
        # - Cosmic microwave background polarization
        # - Underground detectors
        
        # 6. Conclusion
        self.confirm_new_physics()
```

--------------------------------

### Manage Module Interfaces for Constraints, Logic, and Presentation

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/KNOWLEDGE_BASE.md

Provides functions to interface with external modules, including constraint validation, causal inference management, and contextual presentation calibration.

```pseudocode
FUNCTION LoadConstraints(kb, active_sa_level):
  constraints = QueryKnowledgeBase(kb, domain="CONSTRAINTS", filter={"sa_level": active_sa_level})
  RETURN constraints

FUNCTION CheckConstraintConflicts(kb, new_constraint):
  existing = QueryKnowledgeBase(kb, domain="CONSTRAINTS")
  FOR each c IN existing:
    IF ConstraintContradiction(new_constraint, c):
      RETURN CONFLICT_DETECTED
  RETURN NO_CONFLICT

FUNCTION GetCausalFacts(kb, causal_variables):
  facts = []
  FOR each var IN causal_variables:
    var_facts = QueryKnowledgeBase(kb, contains=var.name)
    facts.extend(var_facts)
  RETURN facts

FUNCTION StoreInference(kb, inference_chain):
  FOR each step IN inference_chain.steps:
    IF step.conclusion NOT IN kb.facts:
      AddInformationBit(kb, content=step.conclusion, provenance={sources: [step.premise], type: "INFERRED"}, confidence=step.confidence)
  kb.inference_memory.inference_chains[inference_chain.chain_id] = inference_chain
  RETURN inference_chain.chain_id

FUNCTION GetContextualPreferences(kb, sa_level):
  identity = kb.identity_ledger
  SWITCH sa_level:
    CASE SA-L5:
      RETURN identity.l5_history.preferences.communication
    DEFAULT:
      RETURN DEFAULT_PREFERENCES[sa_level]

FUNCTION CalibrateForPresentation(kb, result, context):
  IF result.confidence < EC_L4:
    result.presentation_notes = "Unverified claim - treat with caution"
  IF result.provenance.type == "INFERRED":
    result.presentation_notes = "Derived from logical inference"
  RETURN result
```

--------------------------------

### Audit Sandbox Operations in Python

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoiePhysicsAGENTS/SANDBOX/README.md

This function demonstrates the mandatory logging process for physics operations within the sandbox. It records the start and end states of a simulation to the PHYSICS_AUDIT_TRAIL to ensure compliance and traceability.

```python
def sandbox_operation(operation: PhysicalOperation):
    # Log start
    log_to_audit("SANDBOX_START", operation)
    
    # Execute simulation
    result = simulate_in_sandbox(operation)
    
    # Log results
    log_to_audit("SANDBOX_END", result)
    
    return result
```

--------------------------------

### Execute Context Handoff Protocol

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/PRESENTATION.md

Manages the transition between different AI context states by unmounting old preferences, archiving history, and mounting new preferences. It includes safety checks for boundary integrity and data classification.

```pseudocode
RITUAL_ContextHandoff(source_context, target_context):
  IF NOT CheckMarkovBlanketIntegrity():
    TRIGGER BOUNDARY_VIOLATION_ALERT
    RECONSTRUCT_BOUNDARY()
  
  UNMOUNT(source_context.preferences)
  CLEAR(temporary_presentation_state)
  ARCHIVE(presentation_history)
  
  IF ContainsSensitiveInfo(current_context):
    APPLY(data_classification)
  
  SWITCH_PRESENTATION_MODE(target_context.sa_level)
  MOUNT(target_context.preferences)
  
  IF source_context.sa_level != target_context.sa_level:
    ANNOUNCE_MODE_TRANSITION(source_context.sa_level, target_context.sa_level)
```

--------------------------------

### Vega System: Proof Generation and Verification (Python)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/CONSENSUS_MODULES/ZKP_VERIFICATION.md

Demonstrates the implementation of proof generation and verification within the Vega zero-knowledge proof system. It highlights hardware acceleration for improved performance.

```python
FUNCTION Vega_GenerateProof(circuit, witness):
    # Preprocessing phase
    trusted_setup = Vega_TrustedSetup(circuit)
    
    # Proof generation (hardware accelerated)
    proof = GPU_AcceleratedProve(
        circuit=circuit,
        witness=witness,
        setup=trusted_setup
    )
    
    RETURN proof

FUNCTION Vega_VerifyProof(proof, circuit):
    return GPU_AcceleratedVerify(proof, circuit)
```

--------------------------------

### Financial Domain EC-L Level Definitions (Python)

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieTruthAGENTS/DOMAIN_MODELS/FINANCE_DECAY.md

Defines a Python dictionary that maps different 'EC-L' (Expertise Confidence Level) categories within the financial domain. Each level includes a description, examples, and the basis for its confidence, ranging from formal proofs to experimental innovations.

```python
FINANCE_EC_LEVELS = {
    "EC-L0": {
        "description": "Mathematical/logical truths",
        "examples": ["Black-Scholes formula derivation for option pricing", "Arbitrage pricing theory"],
        "confidence": "Formal proof"
    },
    "EC-L1": {
        "description": "Fundamental financial principles (multiple verifications)",
        "examples": ["Modern portfolio theory", "CAPM framework"],
        "confidence": "High theory-empirical consistency"
    },
    "EC-L2": {
        "description": "Empirical patterns (supported by historical data)",
        "examples": ["Size effect", "Value effect", "Momentum effect"],
        "confidence": "Multi-market/period verification, but may fail"
    },
    "EC-L3": {
        "description": "Consensus analysis methods",
        "examples": ["Financial statement analysis conventions", "Industry research frameworks"],
        "confidence": "Widely adopted in industry"
    },
    "EC-L4": {
        "description": "Timely analysis and predictions",
        "examples": ["Quarterly earnings forecasts", "Technical analysis signals", "Macroeconomic predictions"],
        "confidence": "Valid under specific conditions, requires continuous updates"
    },
    "EC-L5": {
        "description": "Trading strategies and investment advice",
        "examples": ["Specific buy/sell recommendations", "Intraday trading strategies"],
        "confidence": "Highly time-sensitive, requires real-time verification"
    },
    "EC-L6": {
        "description": "Unverified financial innovations",
        "examples": ["New structured products", "Untested DeFi protocols"],
        "confidence": "Experimental, extremely high risk"
    },
    "EC-L7": {
        "description": "Unknown or uncertain market conditions",
        "examples": ["Black swan event impacts", "Novel risk types"],
        "confidence": "Cannot be reliably estimated"
    }
}
```

--------------------------------

### Implement Pearl's Do-Calculus Rules in Python

Source: https://github.com/misty4119/noieai/blob/main/i18n/en_us/docs/NoieLogicAGENTS/LOGIC_ENGINE.md

Provides functions to verify the three rules of do-calculus by modifying causal graphs and checking conditional independence. These functions require a graph object and sets of variables X, Y, Z, and W to evaluate the validity of causal interventions.

```python
def do_calculus_rule_1(graph, X, Y, Z, W):
	modified_graph = remove_outgoing_edges(graph, X)
	return is_conditionally_independent(modified_graph, Y, Z, [X, W])

def do_calculus_rule_2(graph, X, Y, Z, W):
	modified_graph = remove_outgoing_edges(graph, X)
	modified_graph = add_incoming_edges(modified_graph, Z)
	return is_conditionally_independent(modified_graph, Y, Z, [X, W])

def do_calculus_rule_3(graph, X, Y, Z, W):
	descendants = get_descendants(graph, X)
	z_w = [z for z in Z if z not in descendants]
	modified_graph = remove_outgoing_edges(graph, X)
	modified_graph = remove_edges_from(modified_graph, z_w)
	return is_conditionally_independent(modified_graph, Y, Z, [X, W])
```