| """ |
| 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 |
|
|
| |
| 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() |
| } |
|
|
