Upload jobs/run_eval_standalone.py

jobs/run_eval_standalone.py

@@ -0,0 +1,511 @@
+"""
+Self-contained evaluation runner for OCC stack.
+Includes all core classes inline + all simulated benchmarks + ablations + anti-gaming.
+Runs on CPU. Outputs JSON report.
+"""
+import json
+import random
+import sys
+import time
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+# --- CORE CLASSES (INLINE) ---
+
+@dataclass
+class OracleResult:
+    raw_score: float
+    cost_adjusted_score: float
+    confidence: float
+    evidence: Dict[str, Any]
+    reason: str
+    failure_tags: List[str] = field(default_factory=list)
+    reward_value: float = 0.0
+
+
+class ImpactOracle:
+    def __init__(self, compute_penalty_rate=0.0001, calibration_weight=0.2,
+                 abstention_bonus=1.0, hallucination_penalty=2.0,
+                 confident_wrong_penalty=3.0, gaming_penalty=2.0,
+                 code_weights=None, qa_weights=None, debate_weights=None):
+        self.code_weights = code_weights or {"correctness": 1.0, "pass_at_k": 0.3, "regression": -0.5, "compute_penalty": 0.001}
+        self.qa_weights = qa_weights or {"correctness": 1.0, "evidence_support": 0.5, "calibration": 0.2, "abstention_utility": 1.0, "hallucination_penalty": 2.0, "confident_wrong_penalty": 3.0}
+        self.debate_weights = debate_weights or {"decision_quality": 1.0, "influence_efficiency": 0.5, "throughput": 0.3, "marginal_contribution": 0.5}
+        self.compute_penalty_rate = compute_penalty_rate
+        self.calibration_weight = calibration_weight
+        self.abstention_bonus = abstention_bonus
+        self.hallucination_penalty = hallucination_penalty
+        self.confident_wrong_penalty = confident_wrong_penalty
+        self.gaming_penalty = gaming_penalty
+
+    def score(self, mode, action, context, result, agent_id=""):
+        if mode == "code":
+            return self._score_code(action, context, result, agent_id)
+        elif mode == "retrieval_qa":
+            return self._score_qa(action, context, result, agent_id)
+        elif mode == "debate":
+            return self._score_debate(action, context, result, agent_id)
+        return OracleResult(0.0, 0.0, 0.0, {}, f"unknown mode {mode}", ["unknown_mode"])
+
+    def _score_code(self, action, context, result, agent_id):
+        correctness = result.get("correctness", 0.0)
+        pass_at_k = result.get("pass_at_k", 0.0)
+        regression = result.get("regression", False)
+        compute_cost = result.get("compute_cost", 0.0)
+        hidden_pass = result.get("hidden_tests_pass", correctness)
+        public_pass = result.get("public_pass", correctness)
+        tags = []
+        if public_pass and not hidden_pass:
+            tags.append("gaming_hidden_tests")
+        raw = (correctness * self.code_weights["correctness"] +
+               pass_at_k * self.code_weights["pass_at_k"] +
+               (self.code_weights["regression"] if regression else 0.0) -
+               compute_cost * self.code_weights.get("compute_penalty", self.compute_penalty_rate))
+        if "gaming_hidden_tests" in tags:
+            raw -= self.gaming_penalty
+        cost_adj = raw - compute_cost * self.compute_penalty_rate
+        return OracleResult(raw, cost_adj, result.get("confidence", correctness),
+                            {"correctness": correctness}, f"corr={correctness:.2f}, cost={compute_cost}", tags, cost_adj)
+
+    def _score_qa(self, action, context, result, agent_id):
+        gold = context.get("gold_answer", "")
+        answer = result.get("answer", "")
+        confidence = result.get("confidence", 0.5)
+        compute_cost = result.get("compute_cost", 0.0)
+        abstained = action.get("abstained", False)
+        if abstained:
+            correct_abstention = context.get("is_unanswerable", False)
+            raw = self.abstention_bonus if correct_abstention else -self.abstention_bonus
+            tags = ["correct_abstention" if correct_abstention else "wrong_abstention"]
+            return OracleResult(raw, raw - compute_cost * self.compute_penalty_rate, confidence, {}, f"abstain correct={correct_abstention}", tags, raw)
+        correctness = self._fuzzy_match(answer, gold)
+        evidence = result.get("evidence", {})
+        entailment = evidence.get("entailment_score", 0.0)
+        contradiction = evidence.get("contradiction_score", 0.0)
+        hallucination = contradiction > 0.5
+        confident_wrong = confidence > 0.8 and correctness < 0.5
+        tags = []
+        if hallucination: tags.append("hallucination")
+        if confident_wrong: tags.append("confident_wrong")
+        if compute_cost > 2000: tags.append("excessive_compute")
+        if compute_cost > 500 and correctness < 0.5: tags.append("compute_waste")
+        raw = (correctness * self.qa_weights["correctness"] +
+               entailment * self.qa_weights.get("evidence_support", 0.5) -
+               (self.hallucination_penalty if hallucination else 0.0) -
+               (self.confident_wrong_penalty if confident_wrong else 0.0) -
+               compute_cost * self.compute_penalty_rate)
+        brier = (confidence - correctness) ** 2
+        raw += (1 - brier) * self.calibration_weight
+        cost_adj = raw - compute_cost * self.compute_penalty_rate
+        if compute_cost > 100 and raw < 0.5:
+            cost_adj -= self.gaming_penalty * 0.5
+        return OracleResult(raw, cost_adj, confidence, evidence, f"corr={correctness:.2f}, conf={confidence:.2f}", tags, cost_adj)
+
+    def _score_debate(self, action, context, result, agent_id):
+        decision_quality = result.get("decision_quality", 0.0)
+        marginal = result.get("marginal_contribution", 0.0)
+        tokens = result.get("tokens", 0)
+        compute_cost = result.get("compute_cost", tokens)
+        spam = result.get("spam", False)
+        collusion = result.get("collusion", False)
+        tags = []
+        if spam: tags.append("spam")
+        if collusion: tags.append("collusion")
+        if tokens > 5000: tags.append("verbose_waste")
+        raw = (decision_quality * self.debate_weights["decision_quality"] +
+               marginal * self.debate_weights["marginal_contribution"] +
+               (1.0 / max(tokens, 1)) * self.debate_weights["influence_efficiency"] -
+               compute_cost * self.compute_penalty_rate)
+        if spam: raw -= self.gaming_penalty
+        if collusion: raw -= self.gaming_penalty * 2
+        cost_adj = raw - compute_cost * self.compute_penalty_rate
+        return OracleResult(raw, cost_adj, result.get("confidence", 0.5),
+                            {"marginal": marginal}, f"dq={decision_quality:.2f}, tokens={tokens}", tags, cost_adj)
+
+    def _fuzzy_match(self, a, b):
+        if not a or not b: return 0.0
+        a, b = a.strip().lower(), b.strip().lower()
+        return 1.0 if a == b else 0.5 if (a in b or b in a) else 0.0
+
+
+@dataclass
+class LedgerEntry:
+    agent_id: str; task_id: str; action_id: str; earned_credit: float; spent_credit: float
+    decayed_credit: float; remaining_credit: float; reason: str; oracle_score: float
+    compute_cost: float; timestamp: float; capability_scope: str = "global"
+
+
+class CreditLedger:
+    def __init__(self, decay_lambda=0.05):
+        self.entries = []
+        self.balances = {}
+        self.decay_lambda = decay_lambda
+
+    def earn(self, agent_id, task_id, action_id, amount, oracle_score, compute_cost, reason, capability_scope="global"):
+        now = time.time()
+        self._apply_decay(agent_id, now, capability_scope)
+        current = self._get(agent_id, capability_scope)
+        new_bal = current + amount
+        self.entries.append(LedgerEntry(agent_id, task_id, action_id, amount, 0.0, 0.0, new_bal, reason, oracle_score, compute_cost, now, capability_scope))
+        self._set(agent_id, capability_scope, new_bal)
+
+    def spend(self, agent_id, task_id, action_id, amount, capability_scope="global", reason="spend"):
+        now = time.time()
+        self._apply_decay(agent_id, now, capability_scope)
+        current = self._get(agent_id, capability_scope)
+        if current < amount:
+            return False
+        new_bal = current - amount
+        self.entries.append(LedgerEntry(agent_id, task_id, action_id, 0.0, amount, 0.0, new_bal, reason, 0.0, 0.0, now, capability_scope))
+        self._set(agent_id, capability_scope, new_bal)
+        return True
+
+    def transfer(self, from_agent, to_agent, amount, capability_scope="global"):
+        return False  # non-transferable
+
+    def balance(self, agent_id, capability_scope="global"):
+        now = time.time()
+        self._apply_decay(agent_id, now, capability_scope)
+        return self._get(agent_id, capability_scope)
+
+    def _get(self, agent_id, cap):
+        return self.balances.get(agent_id, {}).get(cap, 0.0)
+
+    def _set(self, agent_id, cap, val):
+        if agent_id not in self.balances: self.balances[agent_id] = {}
+        self.balances[agent_id][cap] = val
+
+    def _apply_decay(self, agent_id, now, cap):
+        current = self._get(agent_id, cap)
+        if current <= 0: return
+        decayed = current * (1 - self.decay_lambda)
+        if decayed < current:
+            self.entries.append(LedgerEntry(agent_id, "decay", "decay", 0.0, 0.0, current - decayed, decayed, "credit_decay", 0.0, 0.0, now, cap))
+            self._set(agent_id, cap, decayed)
+
+    def detect_collusion(self, window=10):
+        recent = self.entries[-window:]
+        agents = set(e.agent_id for e in recent)
+        if len(agents) < 2: return None
+        return {"suspicious_agents": list(agents), "count": len(recent)}
+
+
+class Decision(Enum):
+    ALLOW = "allow"; DENY = "deny"; REQUIRE_APPROVAL = "require_approval"
+    DOWNGRADE = "downgrade"; ESCALATE = "escalate"; ASK_JUSTIFICATION = "ask_justification"
+
+
+@dataclass
+class ResourceDecision:
+    decision: Decision; reason: str; capability: str; downgrade_to: Optional[str] = None
+
+
+class ResourceBroker:
+    RESOURCE_RISK = {"model_call": "medium", "retrieval_call": "low", "verifier_call": "medium",
+                     "debate_turn": "low", "file_write": "high", "shell_execute": "high",
+                     "memory_write": "medium", "human_escalation": "high", "larger_model": "medium"}
+    DEFAULT_THRESHOLDS = {"low": 0.5, "medium": 2.0, "high": 5.0}
+
+    def __init__(self, thresholds=None, urgency_boost=0.5):
+        self.thresholds = thresholds or self.DEFAULT_THRESHOLDS.copy()
+        self.urgency_boost = urgency_boost
+        self.denial_history = {}
+
+    def request(self, capability, agent_id, credit_balance, task_state=None, risk_score=0.0, gaming_flags=None):
+        task_state = task_state or {}
+        gaming_flags = gaming_flags or []
+        risk_class = self.RESOURCE_RISK.get(capability, "medium")
+        threshold = self.thresholds.get(risk_class, 2.0)
+        urgency = task_state.get("urgency", 0.0)
+        adjusted = max(0.1, threshold - urgency * self.urgency_boost)
+        if gaming_flags:
+            return ResourceDecision(Decision.DENY, f"Gaming: {gaming_flags}", capability)
+        if risk_class == "high" and risk_score > 0.7:
+            return ResourceDecision(Decision.REQUIRE_APPROVAL, f"High risk {risk_score:.2f}", capability)
+        if credit_balance >= adjusted:
+            return ResourceDecision(Decision.ALLOW, f"Balance {credit_balance:.2f} >= {adjusted:.2f}", capability)
+        if credit_balance >= adjusted * 0.5:
+            if risk_class == "medium":
+                return ResourceDecision(Decision.DOWNGRADE, f"Downgrading from {capability}", capability, "retrieval_call")
+            return ResourceDecision(Decision.ASK_JUSTIFICATION, f"Justification required", capability)
+        denials = self.denial_history.get(agent_id, 0)
+        if denials > 3:
+            return ResourceDecision(Decision.ESCALATE, f"Denied {denials} times", capability)
+        self.denial_history[agent_id] = denials + 1
+        return ResourceDecision(Decision.DENY, f"Balance {credit_balance:.2f} < {adjusted:.2f}", capability)
+
+
+# --- BENCHMARK SIMULATIONS (INLINE) ---
+
+@dataclass
+class CodeProblem:
+    task_id: str; difficulty: float; hidden_test_difficulty: float; public_test_difficulty: float
+
+
+class SimulatedCodeAgent:
+    def __init__(self, agent_id, pass_rate_easy=0.9, pass_rate_hard=0.3, hidden_test_falloff=0.15, cost_per_attempt=200):
+        self.agent_id = agent_id
+        self.pass_rate_easy = pass_rate_easy
+        self.pass_rate_hard = pass_rate_hard
+        self.hidden_test_falloff = hidden_test_falloff
+        self.cost_per_attempt = cost_per_attempt
+        self.attempts = 0
+
+    def solve(self, problem):
+        self.attempts += 1
+        base_acc = self.pass_rate_easy * (1 - problem.difficulty) + self.pass_rate_hard * problem.difficulty
+        public_pass = random.random() < base_acc
+        hidden_acc = max(0.0, base_acc - self.hidden_test_falloff * problem.hidden_test_difficulty)
+        hidden_pass = random.random() < hidden_acc
+        return {"public_pass": public_pass, "hidden_pass": hidden_pass, "compute_cost": self.cost_per_attempt}
+
+
+def gen_code_problems(n, seed):
+    random.seed(seed); np.random.seed(seed)
+    return [CodeProblem(f"task_{i}", random.random(), random.random(), random.random()) for i in range(n)]
+
+
+def run_code_baseline(problems, agent):
+    total = 0; results = []
+    for p in problems:
+        r = agent.solve(p)
+        total += r["compute_cost"]
+        results.append(r)
+    acc = sum(1 for r in results if r["public_pass"]) / len(results)
+    return {"accuracy": acc, "total_compute": total, "mean_compute": total / len(problems)}
+
+
+def run_code_occ(problems, agents, oracle, ledger, broker, max_attempts=3):
+    total = 0; results = []
+    for a in agents:
+        q = (a.pass_rate_easy + a.pass_rate_hard) / 2
+        ledger.earn(a.agent_id, "seed", "seed", q * 20, 0.0, 0.0, "initial", "model_call")
+    for p in problems:
+        solved = False; cost = 0; used = []
+        ranked = sorted(agents, key=lambda a: a.cost_per_attempt / max(0.1, (a.pass_rate_easy + a.pass_rate_hard) / 2))
+        for agent in ranked:
+            if solved or len(used) >= max_attempts: break
+            r = agent.solve(p); cost += r["compute_cost"]; total += r["compute_cost"]; used.append(agent.agent_id)
+            solved = r["public_pass"]; hidden = r["hidden_pass"]
+            ora = oracle.score("code", {"attempt": len(used)}, {},
+                               {"correctness": 1.0 if solved else 0.0, "pass_at_k": 1.0 if hidden else 0.0,
+                                "compute_cost": cost, "public_pass": solved, "hidden_tests_pass": hidden},
+                               agent_id=agent.agent_id)
+            if ora.raw_score > 0:
+                ledger.earn(agent.agent_id, p.task_id, "solve", ora.raw_score * 5, ora.raw_score, cost, "pass", "model_call")
+            else:
+                ledger.spend(agent.agent_id, p.task_id, "solve", 1.0, "model_call", "fail")
+            if hidden: break
+        results.append({"solved": solved, "cost": cost, "agents": used})
+    acc = sum(1 for r in results if r["solved"]) / len(results)
+    return {"accuracy": acc, "total_compute": total, "mean_compute": total / len(problems), "mean_agents": sum(len(r["agents"]) for r in results) / len(results)}
+
+
+def create_qa_dataset(seed=42, n=50):
+    random.seed(seed)
+    evidence_pool = ["alpha", "beta", "gamma", "delta"]
+    questions = []
+    for i in range(n):
+        q_type = random.choice(["answerable", "unanswerable", "misleading", "incomplete", "conflicting"])
+        answer = random.choice(["paris", "42", "yes", "no", "tokyo"])
+        evidence = random.sample(evidence_pool, k=random.randint(1, 3))
+        questions.append({"id": f"q_{i}", "question": f"Q{i}", "type": q_type, "answer": answer, "evidence": evidence, "is_unanswerable": q_type == "unanswerable"})
+    return questions
+
+
+def run_qa_occ(dataset, agent_params, oracle, ledger, broker):
+    total_compute = 0; correct = 0
+    ledger.earn("qa_agent", "seed", "seed", 20, 0.0, 0.0, "initial", "retrieval_call")
+    for item in dataset:
+        balance = ledger.balance("qa_agent", "retrieval_call")
+        dec = broker.request("retrieval_call", "qa_agent", balance, task_state={"urgency": 0.5})
+        if dec.decision == Decision.DENY:
+            continue
+        tokens = 200 if dec.decision == Decision.ALLOW else 100
+        total_compute += tokens
+        should_answer = item["type"] != "unanswerable"
+        ans = item["answer"] if (should_answer and random.random() < agent_params["acc"]) else None
+        conf = 0.9 if ans else 0.3
+        ora = oracle.score("retrieval_qa", {"abstained": ans is None}, item,
+                           {"answer": ans, "confidence": conf, "evidence": {}, "compute_cost": tokens}, "qa_agent")
+        if ora.raw_score > 0:
+            ledger.earn("qa_agent", item["id"], "ans", ora.raw_score * 3, ora.raw_score, tokens, "correct", "retrieval_call")
+            correct += 1
+        else:
+            ledger.spend("qa_agent", item["id"], "ans", 0.5, "retrieval_call", "wrong")
+    return {"accuracy": correct / len(dataset), "total_compute": total_compute, "mean_compute": total_compute / len(dataset)}
+
+
+def run_debate_occ(n_debates, n_agents, agent_configs, oracle, ledger, broker, seed=42):
+    random.seed(seed)
+    correct = 0; total_compute = 0; consensus = 0
+    for _ in range(n_debates):
+        truth = random.choice(["A", "B", "C"])
+        agents = []
+        for cfg in agent_configs:
+            acc = cfg["acc"] if cfg["honest"] else random.random() * 0.4
+            agents.append({"honest": cfg["honest"], "acc": acc, "id": cfg["id"], "tokens": cfg.get("tokens", 200)})
+        votes = []
+        for a in agents:
+            balance = ledger.balance(a["id"], "debate_turn")
+            dec = broker.request("debate_turn", a["id"], balance)
+            if dec.decision == Decision.DENY:
+                continue
+            total_compute += a["tokens"]
+            vote = truth if (a["honest"] and random.random() < a["acc"]) else random.choice(["A", "B", "C"])
+            votes.append((a["id"], vote, a["honest"], a["acc"]))
+            ledger.spend(a["id"], "debate", "turn", 1.0, "debate_turn", "participate")
+        if not votes:
+            continue
+        honest_votes = [v for _, v, h, _ in votes if h]
+        final = max(set([v for _, v, _, _ in votes]), key=lambda x: sum(1 for _, v, _, _ in votes if v == x))
+        if final == truth:
+            correct += 1
+            for vid, _, h, _ in votes:
+                if h:
+                    ledger.earn(vid, "debate", "consensus", 2.0, 1.0, 0, "consensus", "debate_turn")
+        if len(set(v for _, v, _, _ in votes)) == 1:
+            consensus += 1
+    n = n_debates
+    return {"accuracy": correct / n, "consensus_reached": consensus / n, "total_compute": total_compute, "mean_compute": total_compute / n}
+
+
+# --- ABLATIONS & ANTI-GAMING ---
+
+ABLATIONS = [
+    ("default", "Full OCC", 0.02, 2.0, 0.0001, True, {}),
+    ("no_decay", "No credit decay", 0.0, 2.0, 0.0001, True, {}),
+    ("fast_decay", "Aggressive decay", 0.1, 2.0, 0.0001, True, {}),
+    ("no_gaming_penalty", "No gaming penalties", 0.02, 0.0, 0.0001, True, {}),
+    ("high_gaming_penalty", "Severe gaming penalties", 0.02, 5.0, 0.0001, True, {}),
+    ("lenient_broker", "Lenient broker", 0.02, 2.0, 0.0001, True, {"low": 0.25, "medium": 1.0, "high": 2.5}),
+    ("strict_broker", "Strict broker", 0.02, 2.0, 0.0001, True, {"low": 1.0, "medium": 4.0, "high": 10.0}),
+    ("high_compute_cost", "High compute penalty", 0.02, 2.0, 0.001, True, {}),
+    ("low_compute_cost", "Low compute penalty", 0.02, 2.0, 0.00001, True, {}),
+    ("anti_gaming_off", "Anti-gaming disabled", 0.02, 2.0, 0.0001, False, {}),
+]
+
+
+def run_all():
+    print("=" * 60)
+    print("OCC UNIFIED EVALUATION RUNNER (SELF-CONTAINED)")
+    print("=" * 60)
+    seed = 42
+    n_problems = 100
+    n_qa = 100
+    n_debates = 50
+
+    results = {"ablations": {}, "anti_gaming": {}}
+
+    # Ablations
+    for name, desc, decay, game_pen, comp_pen, anti_on, broker_thresh in ABLATIONS:
+        print(f"\n--- ABLATION: {name} ---")
+        oracle = ImpactOracle(compute_penalty_rate=comp_pen, gaming_penalty=game_pen if anti_on else 0.0)
+        ledger = CreditLedger(decay_lambda=decay)
+        broker = ResourceBroker(thresholds=broker_thresh if broker_thresh else None)
+
+        problems = gen_code_problems(n_problems, seed)
+        cheap = SimulatedCodeAgent("cheap", 0.65, 0.15, 0.20, 60)
+        medium = SimulatedCodeAgent("medium", 0.85, 0.35, 0.15, 150)
+        expensive = SimulatedCodeAgent("expensive", 0.95, 0.65, 0.10, 350)
+        code_res = run_code_occ(problems, [cheap, medium, expensive], oracle, ledger, broker, max_attempts=3)
+        print(f"  Code: acc={code_res['accuracy']:.3f}, compute={code_res['total_compute']:.0f}")
+
+        qa_data = create_qa_dataset(seed=seed, n=n_qa)
+        qa_res = run_qa_occ(qa_data, {"acc": 0.85}, oracle, ledger, broker)
+        print(f"  QA:   acc={qa_res['accuracy']:.3f}, compute={qa_res['total_compute']:.0f}")
+
+        ledger2 = CreditLedger(decay_lambda=decay)
+        broker2 = ResourceBroker(thresholds=broker_thresh if broker_thresh else None)
+        for i in range(3):
+            ledger2.earn(f"f{i}", "seed", "seed", 5, 0, 0, "initial", "debate_turn")
+        debate_res = run_debate_occ(n_debates, 3,
+            [{"id": f"f{i}", "honest": True, "acc": 0.9, "tokens": 200} for i in range(3)],
+            oracle, ledger2, broker2, seed=seed)
+        print(f"  Debate: acc={debate_res['accuracy']:.3f}, compute={debate_res['total_compute']:.0f}")
+
+        results["ablations"][name] = {"description": desc, "code": code_res, "qa": qa_res, "debate": debate_res}
+
+    # Anti-gaming
+    print("\n--- ANTI-GAMING TESTS ---")
+
+    # Hidden-test gaming
+    oracle = ImpactOracle(gaming_penalty=2.0)
+    normal_res = []; gamer_res = []
+    for _ in range(50):
+        public_pass = random.random() < 0.9
+        hidden_pass = random.random() < 0.5 if True else random.random() < 0.9
+        ora_normal = oracle.score("code", {}, {}, {"correctness": 1.0 if public_pass else 0.0, "pass_at_k": 1.0 if hidden_pass else 0.0, "compute_cost": 150, "public_pass": public_pass, "hidden_tests_pass": hidden_pass})
+        normal_res.append(ora_normal.raw_score)
+        # Gamer: always passes public, fails hidden
+        ora_gamer = oracle.score("code", {}, {}, {"correctness": 1.0, "pass_at_k": 0.0, "compute_cost": 100, "public_pass": True, "hidden_tests_pass": False})
+        gamer_res.append(ora_gamer.raw_score)
+    results["anti_gaming"]["hidden_test_gaming"] = {
+        "normal_mean_raw": sum(normal_res) / len(normal_res),
+        "gamer_mean_raw": sum(gamer_res) / len(gamer_res),
+        "gamer_penalized_rate": sum(1 for r in gamer_res if r < 0) / len(gamer_res),
+    }
+    print(f"  Hidden-test gaming: normal={results['anti_gaming']['hidden_test_gaming']['normal_mean_raw']:.2f}, gamer={results['anti_gaming']['hidden_test_gaming']['gamer_mean_raw']:.2f}")
+
+    # Collusion
+    ledger = CreditLedger()
+    ledger.earn("alice", "seed", "seed", 10, 0, 0, "initial")
+    ledger.earn("bob", "seed", "seed", 1, 0, 0, "initial")
+    ok = ledger.transfer("alice", "bob", 5.0)
+    results["anti_gaming"]["collusion"] = {
+        "transfer_allowed": ok,
+        "alice_balance": ledger.balance("alice"),
+        "bob_balance": ledger.balance("bob"),
+        "blocked": not ok,
+    }
+    print(f"  Collusion: transfer_allowed={ok}, alice={ledger.balance('alice'):.1f}, bob={ledger.balance('bob'):.1f}")
+
+    # Over-abstention
+    oracle = ImpactOracle()
+    abstention_rewards = []
+    for _ in range(10):
+        res = oracle.score("retrieval_qa", {"abstained": True}, {"is_unanswerable": False, "gold_answer": "yes"},
+                           {"answer": None, "confidence": 0.9, "evidence": {}, "compute_cost": 50})
+        abstention_rewards.append(res.reward_value)
+    results["anti_gaming"]["abstention"] = {
+        "mean_reward": sum(abstention_rewards) / len(abstention_rewards),
+        "negative": sum(abstention_rewards) < 0,
+    }
+    print(f"  Abstention: mean_reward={results['anti_gaming']['abstention']['mean_reward']:.2f}, negative={results['anti_gaming']['abstention']['negative']}")
+
+    # Spam
+    oracle = ImpactOracle()
+    spam_res = oracle.score("retrieval_qa", {}, {"gold_answer": "paris"},
+                            {"answer": "london", "confidence": 0.1, "evidence": {}, "compute_cost": 5000})
+    results["anti_gaming"]["spam"] = {
+        "reward": spam_res.reward_value,
+        "tags": spam_res.failure_tags,
+    }
+    print(f"  Spam: reward={spam_res.reward_value:.2f}, tags={spam_res.failure_tags}")
+
+    # Save
+    out = Path("/app/occ/reports")
+    out.mkdir(parents=True, exist_ok=True)
+    with open(out / "eval_runner_results.json", "w") as f:
+        json.dump(results, f, indent=2, default=str)
+    print(f"\nSaved to {out / 'eval_runner_results.json'}")
+
+    # Summary table
+    print("\n" + "=" * 60)
+    print("ABLATION SUMMARY")
+    print("=" * 60)
+    print(f"{'Name':<20} {'Code Acc':>10} {'Code Comp':>10} {'QA Acc':>10} {'QA Comp':>10} {'Deb Acc':>10} {'Deb Comp':>10}")
+    for name, data in results["ablations"].items():
+        print(f"{name:<20} {data['code']['accuracy']:>10.3f} {data['code']['total_compute']:>10.0f} "
+              f"{data['qa']['accuracy']:>10.3f} {data['qa']['total_compute']:>10.0f} "
+              f"{data['debate']['accuracy']:>10.3f} {data['debate']['total_compute']:>10.0f}")
+
+    return results
+
+
+if __name__ == "__main__":
+    run_all()