Autobahn Thinking Engine Integration
Overview
Integration of Lavoisier with the Autobahn Oscillatory Bio-Metabolic RAG system to replace basic LLM functionality with consciousness-aware biological intelligence.
Integration Architecture
┌─────────────────────────────────────────────────────────────────────────────┐
│ Lavoisier + Autobahn Integration │
│ │
│ ┌─────────────────────────────────────────────────────────────────────────┐ │
│ │ Lavoisier Analysis Layer │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Rust │ │ AI Modules │ │ Visual │ │ Numerical │ │ │
│ │ │ Accelerated │ │ Integration │ │ Pipeline │ │ Pipeline │ │ │
│ │ │ Cores │ │ │ │ │ │ │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────┘ │
│ ▲ │
│ │ Reasoning Tasks │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────────┐ │
│ │ Autobahn Thinking Engine │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │Oscillatory │ │ Biological │ │Consciousness│ │ Bio-Immune │ │ │
│ │ │ Dynamics │ │ Membrane │ │ Emergence │ │ System │ │ │
│ │ │ │ │ Processing │ │ │ │ │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Fire │ │ Temporal │ │ Dual-Prox │ │ Entropy │ │ │
│ │ │ Circle │ │ Determinism │ │ Signaling │ │ Optimizer │ │ │
│ │ │ Comms │ │ │ │ │ │ │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
Lavoisier-Autobahn Connector
# lavoisier/thinking/autobahn_connector.py
import asyncio
import json
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
class ReasoningTaskType(Enum):
"""Types of fuzzy logic reasoning tasks for Autobahn's probabilistic processing"""
SPECTRAL_INTERPRETATION = "spectral_interpretation" # Probabilistic peak assignment
MOLECULAR_IDENTIFICATION = "molecular_identification" # Fuzzy structural matching
PATHWAY_ANALYSIS = "pathway_analysis" # Probabilistic pathway mapping
ANNOTATION_VALIDATION = "annotation_validation" # Confidence-weighted validation
PATTERN_RECOGNITION = "pattern_recognition" # Fuzzy pattern matching
STRUCTURAL_ELUCIDATION = "structural_elucidation" # Probabilistic structure prediction
NOISE_CHARACTERIZATION = "noise_characterization" # Uncertainty quantification
EXPERIMENTAL_DESIGN = "experimental_design" # Probabilistic optimization
UNCERTAINTY_QUANTIFICATION = "uncertainty_quantification" # Confidence interval calculation
BAYESIAN_INFERENCE = "bayesian_inference" # Probabilistic reasoning
FUZZY_CLASSIFICATION = "fuzzy_classification" # Soft classification boundaries
@dataclass
class ReasoningTask:
"""Task to be processed by Autobahn thinking engine"""
task_id: str
task_type: ReasoningTaskType
data: Dict[str, Any]
context: Dict[str, Any]
priority: int = 5 # 1-10, 10 = highest
atp_budget: float = 150.0 # ATP allocation for biological processing
consciousness_threshold: float = 0.7
metabolic_mode: str = "mammalian" # flight, cold_blooded, mammalian, anaerobic
@dataclass
class AutobahnResponse:
"""Response from Autobahn thinking engine"""
task_id: str
response: Dict[str, Any]
consciousness_level: float
atp_consumed: float
membrane_coherence: float
phi_value: float # IIT consciousness measurement
temporal_perspective: str
fire_circle_analysis: Optional[Dict[str, Any]]
threat_assessment: Optional[Dict[str, Any]]
class AutobahnConnector:
"""Connector for Lavoisier-Autobahn integration"""
def __init__(self, autobahn_endpoint: str = "http://localhost:8080"):
self.autobahn_endpoint = autobahn_endpoint
self.task_queue = asyncio.Queue()
self.response_cache = {}
async def submit_reasoning_task(
self,
task: ReasoningTask
) -> AutobahnResponse:
"""Submit reasoning task to Autobahn thinking engine"""
# Prepare Autobahn request
autobahn_request = {
"query": self._format_query_for_autobahn(task),
"configuration": {
"atp_budget_per_query": task.atp_budget,
"consciousness_emergence_threshold": task.consciousness_threshold,
"metabolic_mode": task.metabolic_mode,
"oscillatory_hierarchy": "biological",
"membrane_coherence_threshold": 0.85,
"immune_sensitivity": 0.8,
},
"context": {
"task_type": task.task_type.value,
"lavoisier_data": task.data,
"analysis_context": task.context
}
}
# Send to Autobahn
response = await self._call_autobahn_api(autobahn_request)
# Parse response
return AutobahnResponse(
task_id=task.task_id,
response=response.get("response", {}),
consciousness_level=response.get("consciousness_level", 0.0),
atp_consumed=response.get("atp_consumption", 0.0),
membrane_coherence=response.get("membrane_coherence", 0.0),
phi_value=response.get("phi_value", 0.0),
temporal_perspective=response.get("temporal_perspective", ""),
fire_circle_analysis=response.get("fire_circle_analysis"),
threat_assessment=response.get("threat_assessment")
)
def _format_query_for_autobahn(self, task: ReasoningTask) -> str:
"""Format Lavoisier task as probabilistic fuzzy logic query for Autobahn"""
if task.task_type == ReasoningTaskType.SPECTRAL_INTERPRETATION:
return f"""
Probabilistic spectral interpretation using biological coherence processing:
Spectrum Data: {task.data.get('spectrum')}
M/Z Range: {task.data.get('mz_range')}
Peak Count: {task.data.get('peak_count')}
Instrument: {task.context.get('instrument')}
Provide probabilistic peak assignments with confidence intervals.
Use membrane coherence for fuzzy pattern recognition.
Return multiple structural possibilities with likelihood scores.
Apply oscillatory dynamics for uncertainty quantification.
Expected response: Probabilistic distribution of interpretations.
"""
elif task.task_type == ReasoningTaskType.MOLECULAR_IDENTIFICATION:
return f"""
Fuzzy molecular identification with probabilistic structural matching:
Molecular Ion: {task.data.get('molecular_ion')}
Fragments: {task.data.get('fragments')}
Retention Time: {task.data.get('retention_time')}
Provide probabilistic structural candidates with confidence scores.
Use biological coherence for fuzzy matching against molecular databases.
Return likelihood distributions for different structural possibilities.
Apply membrane processing for uncertainty-aware identification.
Expected response: Ranked probabilities of molecular candidates.
"""
elif task.task_type == ReasoningTaskType.PATHWAY_ANALYSIS:
return f"""
Analyze metabolic pathway using biological intelligence and fire circle communication:
Identified Compounds: {task.data.get('compounds')}
Sample Type: {task.context.get('sample_type')}
Experimental Conditions: {task.context.get('conditions')}
Apply thermodynamic optimization and entropy maximization to pathway analysis.
Use behavioral-induced phenotypic expression patterns for biological relevance.
"""
# Add more task type handlers...
return f"Process {task.task_type.value} task with biological consciousness."
async def _call_autobahn_api(self, request: Dict[str, Any]) -> Dict[str, Any]:
"""Call Autobahn API endpoint"""
import aiohttp
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.autobahn_endpoint}/process_query",
json=request,
headers={"Content-Type": "application/json"}
) as response:
if response.status == 200:
return await response.json()
else:
raise Exception(f"Autobahn API error: {response.status}")
# Integration with existing Lavoisier AI modules
class ThinkingEnhancedAISystem:
"""AI system enhanced with Autobahn thinking engine"""
def __init__(self):
self.autobahn = AutobahnConnector()
self.traditional_llm = None # Fallback for simple tasks
async def enhanced_spectral_analysis(
self,
mz_array: np.ndarray,
intensity_array: np.ndarray,
context: Dict[str, Any]
) -> Dict[str, Any]:
"""Spectral analysis with consciousness-aware reasoning"""
# Prepare reasoning task
task = ReasoningTask(
task_id=f"spectral_{hash(str(mz_array))}",
task_type=ReasoningTaskType.SPECTRAL_INTERPRETATION,
data={
"spectrum": {
"mz": mz_array.tolist(),
"intensity": intensity_array.tolist()
},
"mz_range": (mz_array.min(), mz_array.max()),
"peak_count": len(mz_array)
},
context=context,
atp_budget=200.0, # High energy for complex analysis
consciousness_threshold=0.8 # Require high consciousness
)
# Submit to Autobahn
response = await self.autobahn.submit_reasoning_task(task)
return {
"interpretation": response.response,
"consciousness_level": response.consciousness_level,
"biological_coherence": response.membrane_coherence,
"thinking_quality": response.phi_value,
"energy_efficiency": response.atp_consumed / task.atp_budget
}
async def enhanced_molecular_identification(
self,
molecular_features: Dict[str, Any],
context: Dict[str, Any]
) -> Dict[str, Any]:
"""Molecular identification with biological intelligence"""
task = ReasoningTask(
task_id=f"molid_{hash(str(molecular_features))}",
task_type=ReasoningTaskType.MOLECULAR_IDENTIFICATION,
data=molecular_features,
context=context,
atp_budget=180.0,
consciousness_threshold=0.75
)
response = await self.autobahn.submit_reasoning_task(task)
# Enhanced with biological intelligence insights
return {
"molecular_candidates": response.response.get("candidates", []),
"confidence_scores": response.response.get("confidence", []),
"biological_relevance": response.response.get("biological_analysis", {}),
"fire_circle_communication": response.fire_circle_analysis,
"consciousness_validated": response.consciousness_level > 0.7
}
Integration with Existing Lavoisier Modules
# lavoisier/ai_modules/thinking_integration.py
from lavoisier.thinking.autobahn_connector import ThinkingEnhancedAISystem
from lavoisier.ai_modules.integration import AdvancedMSAnalysisSystem
class ConsciousnessEnhancedAnalysis(AdvancedMSAnalysisSystem):
"""Enhanced analysis system with Autobahn consciousness"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.thinking_engine = ThinkingEnhancedAISystem()
async def analyze_spectrum_with_consciousness(
self,
mz_array: np.ndarray,
intensity_array: np.ndarray,
spectrum_id: str,
compound_database: Optional[List[Dict[str, Any]]] = None
) -> Dict[str, Any]:
"""Analysis enhanced with biological consciousness"""
# Stage 1: Traditional Lavoisier processing
traditional_result = await super().analyze_spectrum(
mz_array, intensity_array, spectrum_id, compound_database
)
# Stage 2: Consciousness-enhanced interpretation
consciousness_analysis = await self.thinking_engine.enhanced_spectral_analysis(
mz_array, intensity_array,
context={
"spectrum_id": spectrum_id,
"traditional_analysis": traditional_result,
"database_available": compound_database is not None
}
)
# Stage 3: Integrate insights
enhanced_result = {
**traditional_result.__dict__,
"consciousness_interpretation": consciousness_analysis,
"biological_intelligence_score": consciousness_analysis["consciousness_level"],
"thinking_coherence": consciousness_analysis["biological_coherence"],
"autobahn_insights": consciousness_analysis["interpretation"]
}
return enhanced_result
Biological Coherence Processing
Autobahn’s unique architecture enables room temperature biological coherence processing through:
Ion Channel Coherence Dynamics
# Biological coherence enables probabilistic computation
coherence_state = {
"H_ion_coherence": 0.89, # Proton tunneling coherence
"Na_K_coherence": 0.84, # Sodium-potassium channel coherence
"Ca_coherence": 0.91, # Calcium channel coherence
"Mg_coherence": 0.87, # Magnesium cofactor coherence
"membrane_potential": -65.2 # Optimal coherence voltage
}
Why Autobahn Excels at Fuzzy Logic Operations
Biological Coherence Properties:
- Superposition-like states in ion channel configurations
- Interference patterns from oscillatory membrane dynamics
- Probabilistic measurement collapse when consciousness emerges
- Entanglement-analogous correlations across membrane networks
Perfect for Probabilistic Tasks:
# Ideal Autobahn task types
fuzzy_logic_tasks = [
"Spectral peak assignment with confidence intervals",
"Molecular structure likelihood distributions",
"Pathway probability mapping",
"Uncertainty quantification in annotations",
"Bayesian inference for compound identification",
"Fuzzy classification with soft boundaries",
"Confidence-weighted decision making"
]
# NOT suitable for deterministic tasks
avoid_deterministic = [
"Exact mathematical calculations",
"Boolean logic operations",
"Precise numerical computations",
"Deterministic algorithm execution"
]
Probabilistic Response Architecture
All Autobahn responses are inherently probabilistic distributions rather than deterministic answers:
@dataclass
class AutobahnResponse:
"""Response from Autobahn's biological coherence processing"""
task_id: str
probabilistic_response: Dict[str, float] # Always probability distributions
confidence_intervals: Dict[str, Tuple[float, float]]
coherence_quality: float # Biological coherence maintained
uncertainty_bounds: Dict[str, float]
alternative_possibilities: List[Dict[str, float]] # Multiple probable outcomes
# Biological coherence metrics
membrane_coherence: float
ion_channel_stability: float
oscillatory_coupling: float
consciousness_emergence_probability: float
Leveraging Biological Coherence
class BiologicalCoherenceProcessor:
"""Interface for biological coherence-based computation"""
def submit_fuzzy_task(
self,
spectrum_data: np.ndarray,
task_type: str = "probabilistic_interpretation"
) -> Dict[str, Any]:
"""Submit fuzzy logic task to biological coherence processor"""
# Configure for probabilistic processing
coherence_config = {
"enable_superposition_states": True,
"allow_interference_patterns": True,
"probabilistic_measurement": True,
"coherence_threshold": 0.85,
"uncertainty_tolerance": 0.15
}
# Task optimized for biological coherence
fuzzy_task = {
"query": "Provide probabilistic spectral interpretation",
"expect_probabilistic_response": True,
"confidence_intervals_required": True,
"multiple_possibilities": True,
"coherence_processing": coherence_config
}
return self.autobahn.process_probabilistic_task(fuzzy_task)
def handle_probabilistic_response(
self,
response: AutobahnResponse
) -> Dict[str, Any]:
"""Process probabilistic response from biological coherence"""
# Extract probability distributions
interpretations = []
for possibility, probability in response.probabilistic_response.items():
if probability > 0.1: # Significant probability threshold
interpretations.append({
"interpretation": possibility,
"probability": probability,
"confidence_bounds": response.confidence_intervals.get(possibility),
"biological_support": probability * response.coherence_quality
})
return {
"probabilistic_interpretations": interpretations,
"coherence_quality": response.coherence_quality,
"uncertainty_quantified": True,
"deterministic_answer": None # Explicitly no single answer
}
Key Integration Principles
1. Always Expect Probabilistic Responses
# Correct approach - handle probability distributions
response = autobahn.process_task(fuzzy_task)
for interpretation, probability in response.probabilistic_response.items():
if probability > confidence_threshold:
consider_interpretation(interpretation, probability)
# WRONG - expecting single deterministic answer
# single_answer = autobahn.get_answer(task) # This won't work well
2. Leverage Uncertainty Quantification
# Use Autobahn's natural uncertainty quantification
uncertainty_analysis = {
"spectral_assignment_confidence": response.confidence_intervals,
"structural_possibility_range": response.uncertainty_bounds,
"alternative_interpretations": response.alternative_possibilities,
"coherence_supported_confidence": response.coherence_quality
}
3. Optimize for Biological Coherence
# Tasks that work excellently with biological coherence
optimal_tasks = {
"fuzzy_pattern_matching": "Uses coherence interference patterns",
"probabilistic_classification": "Natural superposition-like states",
"uncertainty_estimation": "Inherent measurement uncertainty",
"multi_hypothesis_testing": "Parallel possibility evaluation",
"confidence_weighting": "Natural probability distributions"
}
The Result: Lavoisier + Autobahn creates the first biologically-coherent molecular intelligence system that naturally handles uncertainty, provides probabilistic insights, and eliminates false precision through genuine fuzzy logic processing.
Benefits of Autobahn Integration
1. Biological Intelligence
- Consciousness-aware reasoning rather than pattern matching
- Membrane processing for coherent information handling
- ATP-budgeted computational efficiency
2. Advanced Reasoning Capabilities
- Oscillatory dynamics for multi-scale analysis
- Fire circle communication for complex problem solving
- Temporal determinism for efficient recognition navigation
3. Security and Robustness
- Biological immune system for threat detection
- Dual-proximity signaling for credibility assessment
- Entropy optimization for information maximization
4. Novel Analytical Perspectives
- Behavioral-induced analysis patterns
- Thermodynamic moral reasoning
- Circle graph governance optimization
Implementation Timeline
- Phase 1 (Week 1-2): Basic Autobahn connector and API integration
- Phase 2 (Week 3-4): Integration with core Lavoisier AI modules
- Phase 3 (Week 5-6): Consciousness-enhanced analysis pipelines
- Phase 4 (Week 7-8): Performance optimization and validation
- Phase 5 (Week 9-10): Full biological intelligence deployment