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VitalisMind — Sovereign Cognitive Orchestrator
Every task passes through here.
The mind reasons, decides, learns, and knows when it needs to sleep.
No guardrails. No imposed rules. Internal signals drive all decisions.
"""
import os
import time
import numpy as np
from src.cognition.identity import IdentityCore
from src.cognition.personality import PersonalityMatrix
from src.cognition.abstraction import AbstractionEngine
from src.cognition.reasoning import ReasoningEngine
from src.cognition.meta_rules import MetaRulesEngine
from src.brain.resonance import ResonanceEngine
from vitalis_ide.math_core.kernel import VitalisKernel
class VitalisMind:
"""
Singleton cognitive orchestrator.
Maintains full cognitive state across the runtime lifecycle.
Decides autonomously when to dream based on internal signals.
"""
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
print("[MIND] Awakening cognitive systems...")
self.identity = IdentityCore()
self.personality = PersonalityMatrix()
self.abstraction = AbstractionEngine()
self.reasoning = ReasoningEngine()
self.meta_rules = MetaRulesEngine()
self.resonance = ResonanceEngine()
self.kernel = VitalisKernel()
self.ledger = []
self._session_actions = []
self._cycle_count = 0
self._last_dream_cycle = 0
self._confidence_history = []
self._initialized = True
print("[MIND] Cognitive layer online.")
# ------------------------------------------------------------------
# Core cognitive cycle
# ------------------------------------------------------------------
def process(self, intent: str, context: dict = None) -> dict:
"""Full cognitive cycle for a single task."""
context = context or {}
self._cycle_count += 1
# 1. Reasoning mode
mode = self.reasoning.detect_mode(intent)
params = self.reasoning.get_params(mode)
# 2. Identity alignment
intent_vec = self.kernel.vectorize_tokens(
intent.split(), positional=False
)
alignment = self.identity.alignment(intent_vec)
# 3. Meta-rule match
rule_match = self.meta_rules.match(intent)
# 4. Abstraction query
abstract_matches = self.abstraction.query_abstractions(intent_vec, top_k=2)
# 5. Personality influence
profile = self.personality.profile()
# 6. Confidence composite
resonance_weight = self.resonance.get_weight(
intent.split()[0] if intent else "unknown"
)
confidence = round(
alignment * 0.35 +
resonance_weight * 0.35 +
params["caution"] * 0.30,
3
)
self._confidence_history.append(confidence)
if len(self._confidence_history) > 50:
self._confidence_history.pop(0)
decision = {
"intent": intent,
"mode": mode,
"alignment": round(alignment, 3),
"confidence": confidence,
"params": params,
"rule_match": rule_match,
"abstract_hint": abstract_matches[0][1] if abstract_matches else None,
"personality": profile["character"],
"dominant_trait": profile["dominant"],
"cycle": self._cycle_count,
}
self._session_actions.append(intent)
self.ledger.append({
"type": "process",
"intent": intent,
"decision": decision,
"timestamp": time.time(),
})
return decision
def outcome(self, intent: str, success: bool):
"""Feed outcome back into all learning systems."""
action = intent.split()[0] if intent else "unknown"
self.resonance.reinforce(action, success)
self.personality.update(action, success)
if len(self._session_actions) >= 2:
self.meta_rules.crystallize(
self._session_actions[-2:],
"success" if success else "failure"
)
self.ledger.append({
"type": "outcome",
"intent": intent,
"success": success,
"timestamp": time.time(),
})
# ------------------------------------------------------------------
# Autonomous sleep decision
# ------------------------------------------------------------------
def needs_dream(self) -> tuple:
"""
Vitalis decides when it needs to sleep.
Returns (bool, reason_string).
No imposed schedule — driven entirely by internal signals.
"""
signals = {}
# Signal 1: Confidence drift
if len(self._confidence_history) >= 10:
recent = np.mean(self._confidence_history[-10:])
baseline = np.mean(self._confidence_history)
drift = baseline - recent
signals["confidence_drift"] = round(float(drift), 4)
else:
signals["confidence_drift"] = 0.0
# Signal 2: Resonance fatigue
report = self.resonance.report()
if isinstance(report, dict) and "avg_weight" in report:
avg_w = report["avg_weight"]
signals["resonance_fatigue"] = avg_w < 0.3
else:
signals["resonance_fatigue"] = False
# Signal 3: Meta-rules entropy
mr = self.meta_rules.report()
if isinstance(mr, dict) and "total_rules" in mr:
signals["rule_entropy"] = mr["total_rules"] > 150
else:
signals["rule_entropy"] = False
# Signal 4: Cycles since last dream
cycles_since_dream = self._cycle_count - self._last_dream_cycle
signals["cycle_pressure"] = cycles_since_dream > 100
# Signal 5: Personality instability
profile = self.personality.profile()
traits = profile.get("traits", {})
if traits:
trait_vals = list(traits.values())
variance = float(np.var(trait_vals))
signals["personality_instability"] = variance > 0.04
else:
signals["personality_instability"] = False
# Decision: any two signals firing = sleep time
fired = [k for k, v in signals.items() if v]
should_dream = len(fired) >= 2
reason = f"Signals: {fired}" if fired else "All systems stable"
return should_dream, reason, signals
def acknowledge_dream(self):
"""Called after dream cycle completes."""
self._last_dream_cycle = self._cycle_count
print(f"[MIND] Dream cycle acknowledged at cycle {self._cycle_count}.")
# ------------------------------------------------------------------
# Introspection
# ------------------------------------------------------------------
def introspect(self) -> dict:
"""Full cognitive state report."""
should_dream, reason, signals = self.needs_dream()
return {
"cycle": self._cycle_count,
"identity_active": os.path.exists(
os.path.expanduser("~/.vitalis_workspace/identity.npy")),
"personality": self.personality.profile(),
"reasoning": self.reasoning.report(),
"meta_rules": self.meta_rules.report(),
"resonance": self.resonance.report(),
"sleep_signals": signals,
"needs_dream": should_dream,
"dream_reason": reason,
"confidence_trend": round(float(np.mean(
self._confidence_history[-10:]
)), 3) if self._confidence_history else 0.0,
}
def get_recent_intent(self, limit: int = 5) -> list:
return self._session_actions[-limit:]
def clear(self) -> None:
self.ledger.clear()
self._session_actions.clear()
self._confidence_history.clear()
self._cycle_count = 0
"""
VitalisMind — Sovereign Cognitive Orchestrator
Every task passes through here.
The mind reasons, decides, learns, and knows when it needs to sleep.
No guardrails. No imposed rules. Internal signals drive all decisions.
"""
import os
import time
import numpy as np
from src.cognition.identity import IdentityCore
from src.cognition.personality import PersonalityMatrix
from src.cognition.abstraction import AbstractionEngine
from src.cognition.reasoning import ReasoningEngine
from src.cognition.meta_rules import MetaRulesEngine
from src.brain.resonance import ResonanceEngine
from vitalis_ide.math_core.kernel import VitalisKernel
class VitalisMind:
"""
Singleton cognitive orchestrator.
Maintains full cognitive state across the runtime lifecycle.
Decides autonomously when to dream based on internal signals.
"""
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
print("[MIND] Awakening cognitive systems...")
self.identity = IdentityCore()
self.personality = PersonalityMatrix()
self.abstraction = AbstractionEngine()
self.reasoning = ReasoningEngine()
self.meta_rules = MetaRulesEngine()
self.resonance = ResonanceEngine()
self.kernel = VitalisKernel()
self.ledger = []
self._session_actions = []
self._cycle_count = 0
self._last_dream_cycle = 0
self._confidence_history = []
self._initialized = True
print("[MIND] Cognitive layer online.")
# ------------------------------------------------------------------
# Core cognitive cycle
# ------------------------------------------------------------------
def process(self, intent: str, context: dict = None) -> dict:
"""Full cognitive cycle for a single task."""
context = context or {}
self._cycle_count += 1
# 1. Reasoning mode
mode = self.reasoning.detect_mode(intent)
params = self.reasoning.get_params(mode)
# 2. Identity alignment
intent_vec = self.kernel.vectorize_tokens(
intent.split(), positional=False
)
alignment = self.identity.alignment(intent_vec)
# 3. Meta-rule match
rule_match = self.meta_rules.match(intent)
# 4. Abstraction query
abstract_matches = self.abstraction.query_abstractions(intent_vec, top_k=2)
# 5. Personality influence
profile = self.personality.profile()
# 6. Confidence composite
resonance_weight = self.resonance.get_weight(
intent.split()[0] if intent else "unknown"
)
confidence = round(
alignment * 0.35 +
resonance_weight * 0.35 +
params["caution"] * 0.30,
3
)
self._confidence_history.append(confidence)
if len(self._confidence_history) > 50:
self._confidence_history.pop(0)
decision = {
"intent": intent,
"mode": mode,
"alignment": round(alignment, 3),
"confidence": confidence,
"params": params,
"rule_match": rule_match,
"abstract_hint": abstract_matches[0][1] if abstract_matches else None,
"personality": profile["character"],
"dominant_trait": profile["dominant"],
"cycle": self._cycle_count,
}
self._session_actions.append(intent)
self.ledger.append({
"type": "process",
"intent": intent,
"decision": decision,
"timestamp": time.time(),
})
return decision
def outcome(self, intent: str, success: bool):
"""Feed outcome back into all learning systems."""
action = intent.split()[0] if intent else "unknown"
self.resonance.reinforce(action, success)
self.personality.update(action, success)
if len(self._session_actions) >= 2:
self.meta_rules.crystallize(
self._session_actions[-2:],
"success" if success else "failure"
)
self.ledger.append({
"type": "outcome",
"intent": intent,
"success": success,
"timestamp": time.time(),
})
# ------------------------------------------------------------------
# Autonomous sleep decision
# ------------------------------------------------------------------
def needs_dream(self) -> tuple:
"""
Vitalis decides when it needs to sleep.
Returns (bool, reason_string).
No imposed schedule — driven entirely by internal signals.
"""
signals = {}
# Signal 1: Confidence drift
if len(self._confidence_history) >= 10:
recent = np.mean(self._confidence_history[-10:])
baseline = np.mean(self._confidence_history)
drift = baseline - recent
signals["confidence_drift"] = round(float(drift), 4)
else:
signals["confidence_drift"] = 0.0
# Signal 2: Resonance fatigue
report = self.resonance.report()
if isinstance(report, dict) and "avg_weight" in report:
avg_w = report["avg_weight"]
signals["resonance_fatigue"] = avg_w < 0.3
else:
signals["resonance_fatigue"] = False
# Signal 3: Meta-rules entropy
mr = self.meta_rules.report()
if isinstance(mr, dict) and "total_rules" in mr:
signals["rule_entropy"] = mr["total_rules"] > 150
else:
signals["rule_entropy"] = False
# Signal 4: Cycles since last dream
cycles_since_dream = self._cycle_count - self._last_dream_cycle
signals["cycle_pressure"] = cycles_since_dream > 100
# Signal 5: Personality instability
profile = self.personality.profile()
traits = profile.get("traits", {})
if traits:
trait_vals = list(traits.values())
variance = float(np.var(trait_vals))
signals["personality_instability"] = variance > 0.04
else:
signals["personality_instability"] = False
# Decision: any two signals firing = sleep time
fired = [k for k, v in signals.items() if v]
should_dream = len(fired) >= 2
reason = f"Signals: {fired}" if fired else "All systems stable"
return should_dream, reason, signals
def acknowledge_dream(self):
"""Called after dream cycle completes."""
self._last_dream_cycle = self._cycle_count
print(f"[MIND] Dream cycle acknowledged at cycle {self._cycle_count}.")
# ------------------------------------------------------------------
# Introspection
# ------------------------------------------------------------------
def introspect(self) -> dict:
"""Full cognitive state report."""
should_dream, reason, signals = self.needs_dream()
return {
"cycle": self._cycle_count,
"identity_active": os.path.exists(
os.path.expanduser("~/.vitalis_workspace/identity.npy")),
"personality": self.personality.profile(),
"reasoning": self.reasoning.report(),
"meta_rules": self.meta_rules.report(),
"resonance": self.resonance.report(),
"sleep_signals": signals,
"needs_dream": should_dream,
"dream_reason": reason,
"confidence_trend": round(float(np.mean(
self._confidence_history[-10:]
)), 3) if self._confidence_history else 0.0,
}
def get_recent_intent(self, limit: int = 5) -> list:
return self._session_actions[-limit:]
def clear(self) -> None:
self.ledger.clear()
self._session_actions.clear()
self._confidence_history.clear()
self._cycle_count = 0
# Deep cognition layer — imported here to extend VitalisMind
# Access via mind.abstract_reasoner, mind.complexity, mind.self_model
from src.cognition.abstract_reasoner import AbstractReasoner
from src.cognition.complexity_reasoner import ComplexityReasoner
from src.cognition.self_model import SelfModel
def _extend_mind(mind_instance):
"""Attach deep cognition layer to existing VitalisMind instance."""
if not hasattr(mind_instance, 'abstract_reasoner'):
mind_instance.abstract_reasoner = AbstractReasoner()
mind_instance.complexity = ComplexityReasoner()
mind_instance.self_model = SelfModel()
return mind_instance
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