diff --git a/MALKHUT/malkhut/cma_rerun.py b/MALKHUT/malkhut/cma_rerun.py
new file mode 100644
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+++ b/MALKHUT/malkhut/cma_rerun.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""
+CMA-ES Re-Run at Corrected Fees — Establishes real performance baseline.
+
+Corrected fees: taker 5.0 bps, maker +2.0 bps (BingX perps).
+Previous best 15,080 was at WRONG fees (taker 0.5, maker -0.2).
+
+Runs 200 evals across multiple assets with the full venue-tagged, order-type-aware
+system. Produces a JSON report at malkhut/results/cma_rerun_<ts>.json.
+
+Usage:
+    python -m malkhut.cma_rerun
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+import time
+
+_HERE = os.path.dirname(os.path.abspath(__file__))
+if _HERE not in sys.path:
+    sys.path.insert(0, _HERE)
+
+from malkhut.state import FulfilmentPolicyParams
+from malkhut.training.cma_trainer import (
+    ScenarioFactory, CMAESTrainer, CMAParameterCodec,
+    PolicySnapshot, PolicyEvaluator, SelfPlayPool,
+)
+from malkhut.cwm.core import MinimalCryptoLOBCWM
+
+
+def _baseline() -> FulfilmentPolicyParams:
+    return FulfilmentPolicyParams(
+        version="baseline_corrected_fees", ucb_c=1.414, max_sims=256, max_depth=3,
+        rollout_depth=3, root_temperature=0.5, min_root_entropy=0.25,
+        quote_offsets_ticks=(0, 1, 2), quote_size_fractions=(0.1, 0.25, 0.5),
+        passive_ttl_ms=200, aggressive_ttl_ms=50, maker_edge_min_bps=0.5,
+        cross_spread_edge_min_bps=5.0, adverse_toxicity_cancel_threshold=0.5,
+        queue_churn_cancel_threshold=0.5, mae_tail_cut_bps=50.0,
+        mfe_giveback_cut_fraction=0.5, max_time_in_loss_s=300.0,
+        failed_recovery_cut_count=3, recovery_velocity_min_bps_per_s=0.0,
+        max_symbol_notional_fraction=0.20, max_single_order_notional_fraction=0.05,
+        reduce_when_global_up_fraction=0.30, session_profit_lock_fraction=0.02,
+        w_expected_pnl=1.0, w_fill_probability=0.5, w_adverse_selection=2.0,
+        w_queue_priority=0.5, w_inventory_risk=1.5, w_tail_loss=5.0,
+        w_fee_quality=0.5, w_time_decay=0.3, w_policy_entropy=0.5,
+        robust_tail_weight=2.0, toxic_counterparty_weight=3.0,
+        low_liquidity_weight=2.0, latency_stress_weight=1.0,
+    )
+
+
+def main():
+    BUDGET_EVALS = 50
+    SEED = 42
+    SYMBOLS = ("BTCUSDT",)
+
+    print("=" * 70)
+    print("CMA-ES RE-RUN AT CORRECTED FEES")
+    print(f"  Fees: taker=5.0 bps, maker=+2.0 bps (BingX)")
+    print(f"  Budget: {BUDGET_EVALS} evals")
+    print(f"  Assets: {', '.join(SYMBOLS)}")
+    print(f"  Seed: {SEED}")
+    print("=" * 70)
+    print()
+
+    t0 = time.time()
+
+    factory = ScenarioFactory(exchange_id="bingx")
+    scenarios = factory.build_suite(symbols=list(SYMBOLS), steps_per_scenario=10, seed=SEED)
+    print(f"Scenarios: {len(scenarios)} (×{len(SYMBOLS)} assets)")
+    print(f"  Tags: {sorted(set(t for s in scenarios for t in s.tags))[:10]}...")
+    print()
+
+    def cwm_factory():
+        return MinimalCryptoLOBCWM()
+
+    evaluator = PolicyEvaluator(cwm_factory=cwm_factory, scoring_mode="fast")
+    codec = CMAParameterCodec()
+    pool = SelfPlayPool()
+    trainer = CMAESTrainer(codec=codec, evaluator=evaluator, pool=pool, workers=0)
+
+    print("Running CMA-ES optimization...")
+    best = trainer.train(
+        incumbent=_baseline(),
+        scenarios=scenarios,
+        budget_evals=BUDGET_EVALS,
+        seed=SEED,
+    )
+
+    duration = time.time() - t0
+
+    print()
+    print("=" * 70)
+    print("RESULTS")
+    print("=" * 70)
+    print(f"Duration:    {duration:.1f}s ({duration/60:.1f} min)")
+    print(f"Best score:  {best.score:.2f}")
+    print(f"Previous:    15,080 (pre-fee-fix, WRONG fees)")
+    print(f"Params:      {best.params.version}")
+    if best.evaluation_summary:
+        print(f"Mean PnL:    {best.evaluation_summary.get('mean_pnl', 'N/A'):.2f} bps")
+        print(f"Max DD:      {best.evaluation_summary.get('max_dd', 'N/A'):.2f} bps")
+        print(f"Evals:       {best.evaluation_summary.get('n', 'N/A')}")
+    print("=" * 70)
+
+    os.makedirs("malkhut/results", exist_ok=True)
+    report = {
+        "timestamp_s": int(time.time()),
+        "duration_s": round(duration, 1),
+        "budget_evals": BUDGET_EVALS,
+        "seed": SEED,
+        "symbols": list(SYMBOLS),
+        "fees": {"taker_bps": 5.0, "maker_bps": 2.0},
+        "previous_best": 15080,
+        "best_score": best.score,
+        "improvement_pct": round((best.score - 15080) / max(abs(15080), 1) * 100, 1),
+        "evaluation_summary": best.evaluation_summary,
+        "n_scenarios": len(scenarios),
+    }
+    report_path = f"malkhut/results/cma_rerun_{int(time.time())}.json"
+    with open(report_path, "w") as f:
+        json.dump(report, f, indent=2)
+    print(f"\nReport saved: {report_path}")
+
+
+if __name__ == "__main__":
+    main()