diff --git a/MALKHUT/README.md b/MALKHUT/README.md
index 65ca756..0c8719d 100644
--- a/MALKHUT/README.md
+++ b/MALKHUT/README.md
@@ -516,13 +516,18 @@ reward = w_fill_probability * fill_value_score     ← PRIMARY (fill quality)
        - w_inventory_risk * inventory_risk
        - w_tail_loss * tail_risk
        - w_time_decay * time_in_loss
-       + w_fee_quality * maker_fee_benefit
-       - spread_cost - taker_fee
+       + w_fee_quality * fee_savings                ← maker saves (taker-maker) bps
+       - w_fee_quality * taker_fee                  ← taker pays full fee
+       - w_fee_quality * markout_cost * 0.3         ← markout = honest execution cost
 ```
 
-The `fill_value_score` = price_quality - adverse_selection. For maker fills:
-`price_quality = price_improvement_bps` (how much better than best bid/ask).
-For taker fills: `price_quality = spread_bps - slippage_bps` (how efficiently we crossed).
+**Fee model (BingX, no rebates):**
+- Maker fee: 2.0 bps (you pay)
+- Taker fee: 5.0 bps (you pay)
+- Fee savings: 3.0 bps (maker saves 3bp vs taker)
+
+**Markout = quality:** slippage_bps + post_fill_adverse_bps = honest execution cost.
+The system learns: pay the friction (fee + slippage) when urgency × (fee + slippage) < threshold.
 
 #### Fill Quality in the PerformanceMatrix
 
diff --git a/MALKHUT/docs/HFTBACKTEST_CWM_INTEGRATION.md b/MALKHUT/docs/HFTBACKTEST_CWM_INTEGRATION.md
index e707387..57b7eba 100644
--- a/MALKHUT/docs/HFTBACKTEST_CWM_INTEGRATION.md
+++ b/MALKHUT/docs/HFTBACKTEST_CWM_INTEGRATION.md
@@ -387,10 +387,19 @@ The reward function weights fill quality via `w_fill_probability` (default 0.5):
 ```
 reward = w_fill_probability * fill_value_score  ← PRIMARY
        + w_expected_pnl * pnl                   ← secondary
+       + w_fee_quality * fee_savings            ← maker saves (taker-maker) bps
+       - w_fee_quality * taker_fee              ← taker pays full fee
+       - w_fee_quality * markout_cost * 0.3     ← markout = honest execution cost
        - w_adverse_selection * toxicity
        ...
 ```
 
+**Fee model:** BingX has NO rebates. Maker=2.0bp (you pay), taker=5.0bp (you pay).
+Fee savings = 3.0 bps. System learns: prefer maker when savings > fill probability cost.
+
+**Markout = quality:** slippage_bps + post_fill_adverse_bps = honest execution cost.
+System learns: pay the friction when urgency × (fee + slippage) < threshold.
+
 PerformanceMatrix stores `avg_fill_rate`, `avg_slippage_bps`, `avg_price_improvement_bps`,
 `avg_fill_value_score` per (regime, strategy, venue) — enabling:
 "Which strategy achieves the best fill quality in regime X on venue Y?"
diff --git a/MALKHUT/docs/OB_MICROSTRUCTURE_STUDY.md b/MALKHUT/docs/OB_MICROSTRUCTURE_STUDY.md
index eceb2c4..5d30d3c 100644
--- a/MALKHUT/docs/OB_MICROSTRUCTURE_STUDY.md
+++ b/MALKHUT/docs/OB_MICROSTRUCTURE_STUDY.md
@@ -405,6 +405,16 @@ if the market moves adversely after execution. The system scores fills by markou
 
 **Generalizable:** score fills by post-fill markout. Model fill-CONDITIONAL-on-adverse-flow.
 
+### Fee Model (BingX, No Rebates)
+
+BingX reports fees as NEGATIVE. MALKHUT convention: positive = cost.
+- Maker fee: 2.0 bps (you pay)
+- Taker fee: 5.0 bps (you pay)
+- Fee savings: 3.0 bps (maker saves 3bp vs taker)
+- There are NO rebates on BingX.
+
+The system learns: prefer maker when fee savings > fill probability cost.
+
 ### Fill = Queue × Flow Intensity
 
 Fill probability is driven by **trade-arrival intensity** (the tape), not the static book.