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EmergentReasoningModes — Vitalis FSI
The system doesn't think the same way about every problem.
Context activates different reasoning modes.
Modes emerge from accumulated resonance — not hardcoded rules.
Four primary modes:
EXPLORATORY — novel territory, high creativity bias
ANALYTICAL — known domain, high precision bias
RECOVERY — failure context, high caution bias
EXECUTION — clear task, high speed bias
"""
import numpy as np
import os
import json
from vitalis_ide.math_core.kernel import VitalisKernel
from src.cognition.personality import PersonalityMatrix
MODE_SIGNATURES = {
"EXPLORATORY": "new unknown explore discover create novel invent",
"ANALYTICAL": "analyze verify test validate check audit review",
"RECOVERY": "fail error broken fix repair debug recover heal",
"EXECUTION": "run execute deploy build scaffold write generate fast",
}
MODE_PARAMS = {
"EXPLORATORY": {"similarity_threshold": 0.3, "creativity_boost": 1.5, "caution": 0.3},
"ANALYTICAL": {"similarity_threshold": 0.6, "creativity_boost": 0.7, "caution": 0.8},
"RECOVERY": {"similarity_threshold": 0.4, "creativity_boost": 0.5, "caution": 1.5},
"EXECUTION": {"similarity_threshold": 0.5, "creativity_boost": 1.0, "caution": 0.5},
}
class ReasoningEngine:
def __init__(self):
self.kernel = VitalisKernel()
self.personality = PersonalityMatrix()
self.mode_vectors = {
mode: self.kernel.vectorize_tokens(sig.split(), positional=False)
for mode, sig in MODE_SIGNATURES.items()
}
self.current_mode = "EXECUTION"
self.mode_history = []
def detect_mode(self, context: str) -> str:
"""
Given a context string, find the closest reasoning mode.
This is emergence — the mode isn't chosen, it crystallizes
from the similarity between context and mode signatures.
"""
ctx_vec = self.kernel.vectorize_tokens(context.lower().split(), positional=False)
best_mode = "EXECUTION"
best_sim = -1.0
for mode, mode_vec in self.mode_vectors.items():
sim = self.kernel.similarity(ctx_vec, mode_vec)
if sim > best_sim:
best_sim = sim
best_mode = mode
# Personality modulation — caution trait biases toward RECOVERY mode
p = self.personality.traits
if p["CAUTION"] > 0.7 and best_mode == "EXECUTION":
best_mode = "ANALYTICAL"
elif p["CREATIVITY"] > 0.7 and best_mode == "ANALYTICAL":
best_mode = "EXPLORATORY"
if best_mode != self.current_mode:
print(f"[REASONING] Mode shift: {self.current_mode} → {best_mode} (sim={best_sim:.3f})")
self.current_mode = best_mode
self.mode_history.append(best_mode)
return best_mode
def get_params(self, mode: str = None) -> dict:
return MODE_PARAMS.get(mode or self.current_mode, MODE_PARAMS["EXECUTION"])
def report(self) -> dict:
from collections import Counter
history_counts = Counter(self.mode_history)
return {
"current_mode": self.current_mode,
"mode_distribution": dict(history_counts),
"personality_influence": self.personality.dominant_trait(),
"params": self.get_params()
}
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