#!/usr/bin/env python3 # ============================================================================== # COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) # PROJECT: SOVEREIGN STACK # This artifact is entirely proprietary and cryptographically proven. # Open-Source usage requires explicit permission from Brandon Scott Schneider. # ============================================================================== """Cognitive Thermo — thermodynamic path walker for cognitive exploration. Uses surprise/regret as thermodynamic resistance to reorder exploration: - Low resistance (low stress) → explored first (natural flow) - High resistance (high stress) → deferred to end (alternative paths) - Stress = α·surprise + β·regret - Engram memory: stress_memory[addr] += observed_regret""" import sqlite3 import json import math from collections import defaultdict DB = "substrate_index.db" # Thermodynamic weights ALPHA = 0.5 # surprise weight BETA = 0.3 # regret weight LAMBDA = 0.2 # stress penalty on score def concept_feature(pkg, _all=None): fb = 0 fl = pkg.get('forming_load') or 0 tier = (pkg.get('tier') or '').upper() if fl > 1.0: fb |= 0b00000001 # ACTIVE if tier in ('FOAM','CRYSTALLINE','SINGULARITY'): fb |= 0b00000010 # CRYSTALLIZED if pkg.get('dependencies'): fb |= 0b00000100 # CONNECTED if fl > 2.0: fb |= 0b00001000 # DEEP anchor = pkg.get('concept_anchor') or '' if 'SEED' in anchor or 'FORMING' in anchor: fb |= 0b00010000 # HYPOTHESIS if pkg.get('concept_vector'): fb |= 0b00100000 # BRIDGE if _all is not None: idx = pkg.get('_index', 0) if idx > len(_all) - 50: fb |= 0b01000000 # RECENT dom = pkg.get('domain', '').upper() if dom in ('COMPUTE','SUBSTRATE'): fb |= 0b10000000 # CORE return fb def hamming(a, b): return bin(a ^ b).count("1") def sym_idx(p, q): hi, lo = max(p,q), min(p,q) return hi*(hi+1)//2 + lo def fb_name(fb): p = [] if fb&0b10000000: p.append('CORE') if fb&0b01000000: p.append('RECENT') if fb&0b00100000: p.append('BRIDGE') if fb&0b00010000: p.append('HYPOTHESIS') if fb&0b00001000: p.append('DEEP') if fb&0b00000100: p.append('CONNECTED') if fb&0b00000010: p.append('CRYSTALLIZED') if fb&0b00000001: p.append('ACTIVE') return '+'.join(p) if p else 'NULL' def thermodynamic_score(prob, baseline_prob, alpha=ALPHA, beta=BETA, lam=LAMBDA): """Compute thermodynamic score for a path. surprise = -log(P) — how unexpected is this transition regret = log(P_best) - log(P) — how much worse than optimal stress = α·surprise + β·regret score = log(P) - λ·stress (lower stress → higher score) """ p_safe = max(prob, 1e-6) surprise = -math.log(p_safe) regret = math.log(max(baseline_prob, 1e-6)) - math.log(p_safe) regret = max(0, regret) # regret can't be negative stress = alpha * surprise + beta * regret score = math.log(p_safe) - lam * stress return score, stress def main(): conn = sqlite3.connect(DB) cur = conn.cursor() cur.execute(""" SELECT pkg, version, domain, concept_anchor, concept_vector, forming_load, confirmed_load, layer, tier, tags FROM packages ORDER BY forming_load DESC """) _all = [] for i, r in enumerate(cur.fetchall()): pkg = { 'pkg': r[0], 'version': r[1], 'domain': r[2], 'concept_anchor': r[3], 'concept_vector': json.loads(r[4]) if r[4] else [], 'forming_load': r[5], 'confirmed_load': r[6], 'layer': r[7], 'tier': r[8], 'tags': json.loads(r[9]) if r[9] else [], } pkg['_index'] = i _all.append(pkg) # Build LUT with probabilities counts = defaultdict(lambda: defaultdict(int)) for i in range(2, len(_all)): prev_fb = concept_feature(_all[i-2], _all) curr_fb = concept_feature(_all[i-1], _all) nxt_fb = concept_feature(_all[i], _all) addr = sym_idx(prev_fb, curr_fb) counts[addr][nxt_fb] += 1 lut = {} for addr, nc in counts.items(): total = sum(nc.values()) sorted_nc = sorted(nc.items(), key=lambda x: -x[1]) best = sorted_nc[0][0] best_prob = sorted_nc[0][1] / total second = sorted_nc[1][0] if len(sorted_nc) > 1 else None second_prob = sorted_nc[1][1]/total if len(sorted_nc) > 1 else 0 conf = best_prob lut[addr] = (best, second, conf, second_prob) # Build stress_memory (engram) stress_memory = defaultdict(float) for addr, nc in counts.items(): total = sum(nc.values()) best_count = max(nc.values()) for fb, count in nc.items(): if count < best_count: regret = math.log(best_count + 1) - math.log(count + 1) stress_memory[addr, fb] += regret print("="*70) print("COGNITIVE THERMO — thermodynamic path exploration") print("="*70) print(f"α={ALPHA} (surprise) β={BETA} (regret) λ={LAMBDA} (stress penalty)") # Top-3 hottest = current position top3 = _all[:3] print(f"\nCurrent position:") for i, p in enumerate(top3): fb = concept_feature(p, _all) print(f" {i+1}. {p['pkg'].split('/')[-1][:50]} load={p['forming_load']:.3f}") # Thermo walk from current position print(f"\n{'─'*70}") print(f"THERMODYNAMIC PATH WALK (depth=4, branch=3)") print(f"{'─'*70}") # Start from the hottest pair start_prev = top3[0] start_curr = top3[1] # Initialize paths: (path_list, score, total_stress) paths = [([start_prev, start_curr], 0.0, 0.0)] for step in range(4): new_paths = [] for path, score, total_stress in paths: # path is a list of dicts; skip malformed entries if not isinstance(path, list) or len(path) < 2: continue prev, curr = path[len(path)-2], path[len(path)-1] prev_fb = concept_feature(prev, _all) curr_fb = concept_feature(curr, _all) addr = sym_idx(prev_fb, curr_fb) if addr not in lut: continue best, second, conf, second_prob = lut[addr] # Try all candidates (up to 3 branches) if best is not None: matches = [p for p in _all if concept_feature(p, _all) == best and p['pkg'] not in [x['pkg'] for x in path]] matches.sort(key=lambda x: -(x.get('forming_load') or 0)) for m in matches[:2]: prob = conf s, stress = thermodynamic_score(prob, conf) new_paths.append((path + [m], score + s, total_stress + stress)) if second is not None and second_prob > 0.01: matches = [p for p in _all if concept_feature(p, _all) == second and p['pkg'] not in [x['pkg'] for x in path]] matches.sort(key=lambda x: -(x.get('forming_load') or 0)) for m in matches[:1]: prob = second_prob s, stress = thermodynamic_score(prob, conf) new_paths.append((path + [m], score + s, total_stress + stress)) # Sort by score (descending) — low stress paths naturally rise new_paths.sort(key=lambda x: -x[1]) paths = new_paths[:15] # beam width # Detect "DeepCompression" regions — addresses with entropy > threshold AND stress > threshold # These are thermodynamic sinks: high uncertainty + historically costly entropy_at_addr = {} for addr, nc in counts.items(): total = sum(nc.values()) if total > 0: probs = [c / total for c in nc.values()] entropy = -sum(p * math.log(p + 1e-6) for p in probs) stress = sum(stress_memory.get((addr, fb), 0) for fb in nc) entropy_at_addr[addr] = (entropy, stress) # Print paths grouped by stress level (low stress = natural flow, high = deferred) print(f"\n{'='*70}") print(f"PATHS RANKED BY THERMODYNAMIC SCORE (low stress = natural flow)") print(f"{'='*70}") for rank, (path, score, total_stress) in enumerate(paths[:10], 1): chain = [p['pkg'].split('/')[-1][:35] if isinstance(p, dict) else str(p)[:35] for p in path] loads = [p.get('forming_load', 0) if isinstance(p, dict) else 0 for p in path] # Stress level if total_stress < 2: level = "🟢 LOW (natural flow)" elif total_stress < 5: level = "🟡 MEDIUM (interesting deviation)" else: level = "🔴 HIGH (deferred/breakthrough)" print(f"\n Path {rank:2d} score={score:7.3f} stress={total_stress:6.3f} {level}") for j, (name, load) in enumerate(zip(chain, loads)): arrow = "→" if j > 0 else " " print(f" {arrow} {name:35s} load={load:.3f}") print(f"\n{'='*70}") print("HOW TO READ:") print(" 🟢 Green paths = your brain's natural flow (low resistance, conscious)") print(" 🟡 Yellow paths = structured deviations (interesting, semi-conscious)") print(" 🔴 Red paths = pushed to subconscious (high resistance, background work)") print("") print("YOUR BRAIN'S MECHANISM:") print(" Immediate danger → solve NOW (green)") print(" Not dangerous but hard → push to subconscious (red)") print(" Subconscious works on it in background between conscious thoughts") print("="*70) conn.close() if __name__ == "__main__": main()