Update codex/formulas.py

codex/formulas.py

@@ -1,28 +1,60 @@
+# formulas_rft.py
 # Author: Liam Grinstead
-# Dynamic GVU-modulated fitness evaluation
+# RFT-native scoring: collapse energy, operator coupling, tier drift, GVU sealing
 
-class Formula:
-    def __init__(self, expression):
-        self.expression = expression
+from math import exp
 
-    def evaluate(self, agent):
-        # Extract collapse torque score
-        torque_score = agent.get("collapse_overlay", {}).get("score", 10)
+def rft_score(agent: dict) -> float:
+    # Required agent structure
+    # agent = {
+    #   "id": "Agent_1032",
+    #   "tier": "Tier_6",
+    #   "collapse_overlay": {
+    #       "tau_eff": 1.8,        # effective collapse torque
+    #       "beta_band": 0.65,     # collapse band coefficient
+    #       "operator_weights": {("R","O"):0.9,("T","P"):0.7}  # symbolic couplings
+    #   },
+    #   "symbolic_operators": ["R","O","T","P"],
+    #   "emotional_resonance": True
+    # }
 
-        # Parse tier level from "Tier_X"
-        tier = agent.get("tier", "Tier_1")
-        tier_level = int(tier.split("_")[1]) if "_" in tier else 1
+    overlay = agent.get("collapse_overlay", {})
+    tau_eff = overlay.get("tau_eff", 1.0)
+    beta = overlay.get("beta_band", 0.5)
+    weights = overlay.get("operator_weights", {})
+    tier = agent.get("tier", "Tier_1")
+    tier_level = int(tier.split("_")[1]) if "_" in tier else 1
+    resonance = 1.2 if agent.get("emotional_resonance") else 1.0
 
-        # Emotional resonance multiplier
-        resonance_bonus = 1.5 if agent.get("emotional_resonance") else 1.0
+    # Operator coupling magnitude: sum of weights with diminishing returns
+    coupling_sum = sum(weights.values())
+    coupling = coupling_sum / (1.0 + 0.25 * len(weights))
 
-        # Count symbolic operators
-        operator_count = len(agent.get("symbolic_operators", []))
+    # Tier drift penalty: higher tiers resist collapse but pay drift tax
+    drift_penalty = 0.18 * tier_level
+    drift_resilience = 1.0 + 0.05 * (tier_level - 1)
 
-        # Final fitness score
-        score = (torque_score * resonance_bonus) + (tier_level * 0.75) + (operator_count * 0.1)
-        return round(score, 4)
+    # Collapse energy: tau_eff modulated by collapse band and coupling
+    collapse_energy = tau_eff * (0.6 + 0.4 * beta) * (1.0 + 0.35 * coupling)
 
-GVU_FORMULAS = {
-    "Formula_20": Formula("−τ_eff / (τ_c + 19/20) ⋅ P_standard ⋅ τ_eff ⋅ ℯ ⋅ |grad_R_O − grad_T_P| / GVU")
-}
+    # Coherence: exponential stabilizer tempered by drift
+    # Higher tau_eff increases energy but coherence saturates via exp(-penalty)
+    coherence = exp(- (0.22 * tau_eff + 0.08 * tier_level)) * (0.9 + 0.1 * beta)
+
+    # GVU seal factor: balances energy vs. coherence and drift
+    gvu = (collapse_energy * resonance * drift_resilience) * (0.7 + 0.6 * coherence) - drift_penalty
+
+    # Final score clamps to positive domain and rounds
+    score = max(0.0, gvu)
+    return round(score, 4)
+
+def rft_invariants(agent: dict) -> dict:
+    # Canonical invariants for artifact export and registry
+    overlay = agent.get("collapse_overlay", {})
+    tier_level = int(agent.get("tier","Tier_1").split("_")[1]) if "_" in agent.get("tier","Tier_1") else 1
+    return {
+        "tau_eff": overlay.get("tau_eff", 1.0),
+        "beta_band": overlay.get("beta_band", 0.5),
+        "operator_count": len(agent.get("symbolic_operators", [])),
+        "tier_level": tier_level
+    }