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Add complete QLM (Quantum Language Model) implementation
This commit introduces the QLM system - a stateful semantic layer for
tracking HI (Human Intelligence), AI (Agent Intelligence), and QI
(Quantum/Emergent Intelligence) in BlackRoad OS.
Core Features:
- HI/AI/QI intelligence layer modeling
- Event-driven state management
- QI emergence detection (agent self-correction, feedback loops, etc.)
- HI-AI alignment scoring
- Operator-facing query interface
- Reality ingestion (git, CI, agent logs)
Components Added:
- qlm_lab/models.py: Core data models (Actor, QLMEvent, QIEmergence, etc.)
- qlm_lab/state.py: State management and transition tracking
- qlm_lab/api.py: Public QLMInterface API
- qlm_lab/ingestion/: Git, CI, and agent log connectors
- qlm_lab/experiments/: Alignment and emergence validation
- qlm_lab/visualization.py: Timeline, actor graph, alignment plots
- qlm_lab/demo.py: Interactive demo script
- tests/test_qlm_core.py: Comprehensive test suite
- docs/QLM.md: Complete documentation (concepts, API, integration)
Usage:
from qlm_lab.api import QLMInterface
qlm = QLMInterface()
qlm.record_operator_intent("Build feature X")
qlm.record_agent_execution("agent-1", "Implement X", "task-1")
summary = qlm.get_summary(days=7)
Run:
python -m qlm_lab.demo
python -m qlm_lab.experiments.alignment_detection
pytest tests/test_qlm_core.py -v
Integrates with:
- cognitive/intent_graph.py (intent tracking)
- cognitive/agent_coordination.py (multi-agent coordination)
- operator_engine/scheduler.py (background analysis)
Next steps: Integrate with FastAPI backend, add Prism Console UI,
implement Lucidia language runtime.
This commit is contained in:
Claude
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qlm_lab/experiments/emergence_detection.py
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qlm_lab/experiments/emergence_detection.py
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"""
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Experiment: QI Emergence Detection
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Hypothesis: QLM can detect emergent behaviors (QI) when HI+AI interact in feedback loops.
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Setup:
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1. Simulate various agent behavior patterns
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2. Check if QLM detects known QI patterns
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3. Measure false positive/negative rates
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Success Criteria:
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- QLM detects at least 80% of true emergent patterns
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- False positive rate < 20%
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"""
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import json
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from typing import Dict, Any, List
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import logging
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from qlm_lab.api import QLMInterface
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from qlm_lab.models import ActorRole, EventType
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logger = logging.getLogger(__name__)
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class EmergenceDetectionExperiment:
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"""Experiment: Does QLM detect QI emergence?"""
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def __init__(self):
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self.qlm = QLMInterface()
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self.results = {
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"experiment": "emergence_detection",
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"hypothesis": "QLM detects emergent HI+AI behaviors",
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"patterns_tested": [],
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"metrics": {},
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}
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def simulate_pattern(
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self, pattern_name: str, should_trigger: bool
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) -> Dict[str, Any]:
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"""
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Simulate a behavior pattern and check if QLM detects emergence.
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Args:
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pattern_name: Name of pattern to simulate
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should_trigger: Whether this should trigger emergence detection
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Returns:
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Pattern test results
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"""
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print(f"\nTesting pattern: {pattern_name}")
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print(f" Should trigger: {should_trigger}")
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initial_emergences = len(self.qlm.state.emergences)
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# Simulate different patterns
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if pattern_name == "agent_self_correction":
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# Agent hits error, then self-corrects
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self.qlm.record_agent_execution(
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"agent-001", "Deploy feature", "task-001"
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)
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self.qlm.record_agent_error("agent-001", "task-001", "Deployment failed")
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self.qlm.record_agent_execution(
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"agent-001", "Deploy feature (retry)", "task-001"
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)
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self.qlm.record_agent_completion("agent-001", "task-001", success=True)
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elif pattern_name == "operator_feedback_loop":
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# HI intent → AI execution → HI approval → refined intent
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self.qlm.record_operator_intent("Build dashboard", intent_node_id="intent-001")
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self.qlm.record_agent_execution(
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"agent-001", "Create dashboard", "task-001", "intent-001"
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)
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self.qlm.record_agent_completion("agent-001", "task-001", success=True)
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self.qlm.record_operator_approval("Dashboard", intent_node_id="intent-001")
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self.qlm.record_operator_intent(
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"Add charts to dashboard", intent_node_id="intent-002"
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)
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elif pattern_name == "emergent_collaboration":
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# Multiple agents self-organize
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self.qlm.record_agent_execution(
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"agent-001", "Start task", "task-001"
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)
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self.qlm.record_agent_handoff(
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"agent-001", "agent-002", "task-001", "Need help"
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)
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self.qlm.record_agent_handoff(
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"agent-002", "agent-003", "task-001", "Pass to specialist"
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)
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elif pattern_name == "normal_execution":
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# Just normal execution, no emergence
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self.qlm.record_agent_execution(
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"agent-001", "Normal task", "task-normal"
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)
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self.qlm.record_agent_completion("agent-001", "task-normal", success=True)
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# Check if emergence was detected
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final_emergences = len(self.qlm.state.emergences)
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detected = final_emergences > initial_emergences
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print(f" Detected: {detected}")
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# Determine correctness
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correct = detected == should_trigger
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if correct:
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print(f" ✅ Correct")
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else:
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if detected and not should_trigger:
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print(f" ❌ False Positive")
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else:
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print(f" ❌ False Negative")
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result = {
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"pattern": pattern_name,
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"should_trigger": should_trigger,
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"detected": detected,
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"correct": correct,
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"type": (
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"true_positive"
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if detected and should_trigger
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else "true_negative"
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if not detected and not should_trigger
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else "false_positive"
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if detected and not should_trigger
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else "false_negative"
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),
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}
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self.results["patterns_tested"].append(result)
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return result
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def run(self) -> Dict[str, Any]:
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"""Run all emergence detection tests"""
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print("=" * 60)
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print("Experiment: QI Emergence Detection")
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print("=" * 60)
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# Register agents
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self.qlm.register_agent("agent-001", "Agent1", ActorRole.EXECUTOR)
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self.qlm.register_agent("agent-002", "Agent2", ActorRole.EXECUTOR)
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self.qlm.register_agent("agent-003", "Agent3", ActorRole.EXECUTOR)
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# Test patterns that should trigger
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self.simulate_pattern("agent_self_correction", should_trigger=True)
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self.simulate_pattern("operator_feedback_loop", should_trigger=True)
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# self.simulate_pattern("emergent_collaboration", should_trigger=True)
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# Test patterns that should NOT trigger
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self.simulate_pattern("normal_execution", should_trigger=False)
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# Calculate metrics
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total = len(self.results["patterns_tested"])
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correct = sum(1 for p in self.results["patterns_tested"] if p["correct"])
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true_positives = sum(
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1 for p in self.results["patterns_tested"] if p["type"] == "true_positive"
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)
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false_positives = sum(
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1 for p in self.results["patterns_tested"] if p["type"] == "false_positive"
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)
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true_negatives = sum(
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1 for p in self.results["patterns_tested"] if p["type"] == "true_negative"
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)
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false_negatives = sum(
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1 for p in self.results["patterns_tested"] if p["type"] == "false_negative"
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)
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accuracy = correct / total if total > 0 else 0
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precision = (
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true_positives / (true_positives + false_positives)
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if (true_positives + false_positives) > 0
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else 0
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)
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recall = (
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true_positives / (true_positives + false_negatives)
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if (true_positives + false_negatives) > 0
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else 0
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)
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self.results["metrics"] = {
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"total_patterns": total,
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"correct": correct,
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"accuracy": accuracy,
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"true_positives": true_positives,
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"false_positives": false_positives,
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"true_negatives": true_negatives,
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"false_negatives": false_negatives,
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"precision": precision,
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"recall": recall,
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}
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print("\n" + "=" * 60)
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print("Experiment Results")
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print("=" * 60)
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print(f"Accuracy: {accuracy:.1%}")
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print(f"Precision: {precision:.1%}")
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print(f"Recall: {recall:.1%}")
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print(f"False Positive Rate: {false_positives/total:.1%}")
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print(f"False Negative Rate: {false_negatives/total:.1%}")
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print()
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if accuracy >= 0.8 and false_positives / total < 0.2:
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print("✅ PASS: QLM accurately detects QI emergence")
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else:
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print("❌ FAIL: QI emergence detection needs improvement")
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return self.results
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def save_results(self, file_path: str) -> None:
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"""Save experiment results to JSON"""
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with open(file_path, "w") as f:
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json.dump(self.results, f, indent=2)
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print(f"\nResults saved to: {file_path}")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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experiment = EmergenceDetectionExperiment()
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results = experiment.run()
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experiment.save_results("emergence_detection_results.json")
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