diff --git a/docs/ATOMIZER_PODCAST_BRIEFING.md b/docs/ATOMIZER_PODCAST_BRIEFING.md
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--- a/docs/ATOMIZER_PODCAST_BRIEFING.md
+++ b/docs/ATOMIZER_PODCAST_BRIEFING.md
@@ -1,7 +1,7 @@
 # Atomizer: Intelligent FEA Optimization & NX Configuration Framework
 ## Complete Technical Briefing Document for Podcast Generation
 
-**Document Version:** 2.0
+**Document Version:** 2.1
 **Generated:** December 31, 2025
 **Purpose:** NotebookLM/AI Podcast Source Material
 
@@ -554,7 +554,115 @@ REPEAT until converged:
 
 ---
 
-# PART 8: THE EXTRACTOR LIBRARY
+# PART 8: SELF-AWARE TURBO (SAT) - VALIDATED BREAKTHROUGH
+
+## The Problem: Surrogates That Don't Know When They're Wrong
+
+Traditional neural surrogates have a fatal flaw: **they're confidently wrong in unexplored regions**.
+
+In V5, we trained an MLP on 129 FEA samples and ran L-BFGS gradient descent on the surrogate. It found a "minimum" at WS=280. We ran FEA. The actual result: WS=376 - a **30%+ error**.
+
+The surrogate had descended to a region with no training data and predicted with perfect confidence. L-BFGS loves smooth surfaces, and the MLP happily provided one - completely fabricated.
+
+**Root cause:** The surrogate doesn't know what it doesn't know.
+
+## The Solution: Self-Aware Turbo (SAT)
+
+SAT v3 achieved **WS=205.58**, beating all previous methods (V7 TPE: 218.26, V6 TPE: 225.41).
+
+### Core Principles
+
+1. **Never trust a point prediction** - Always require uncertainty bounds
+2. **High uncertainty = run FEA** - Don't optimize where you don't know
+3. **Actively fill gaps** - Prioritize FEA in high-uncertainty regions
+4. **Validate gradient solutions** - Check L-BFGS results before trusting
+
+### Key Innovations
+
+**1. Ensemble Surrogate (Epistemic Uncertainty)**
+
+Instead of one MLP, train **5 independent models** with different initializations:
+
+```python
+class EnsembleSurrogate:
+    def predict(self, x):
+        preds = [m.predict(x) for m in self.models]
+        mean = np.mean(preds, axis=0)
+        std = np.std(preds, axis=0)  # Epistemic uncertainty!
+        return mean, std
+```
+
+**Why this works:** Models trained on different seeds agree in well-sampled regions but **disagree wildly in extrapolation regions**.
+
+**2. Distance-Based Out-of-Distribution Detection**
+
+Track training data distribution and flag points that are "too far":
+
+```python
+def is_in_distribution(self, x, threshold=2.0):
+    """Check if point is within 2 std of training data."""
+    z_scores = np.abs((x - self.mean) / (self.std + 1e-6))
+    return z_scores.max() < threshold
+```
+
+**3. Adaptive Exploration Schedule**
+
+```python
+def get_exploration_weight(trial_num):
+    if trial_num <= 30:      return 0.15  # Phase 1: 15% exploration
+    elif trial_num <= 80:    return 0.08  # Phase 2: 8% exploration
+    else:                    return 0.03  # Phase 3: 3% exploitation
+```
+
+**4. Soft Mass Constraints in Acquisition**
+
+```python
+mass_penalty = max(0, pred_mass - 118.0) * 5.0  # Soft threshold at 118 kg
+acquisition = norm_ws - exploration_weight * norm_dist + norm_mass_penalty
+```
+
+### SAT Version History
+
+| Version | Training Data | Key Fix | Best WS |
+|---------|---------------|---------|---------|
+| v1 | 129 samples | - | 218.26 |
+| v2 | 196 samples | Duplicate prevention | 271.38 (regression!) |
+| **v3** | **556 samples (V5-V8)** | **Adaptive exploration + mass targeting** | **205.58** |
+
+### V9 Results (SAT v3)
+
+| Phase | Trials | Best WS | Mean WS |
+|-------|--------|---------|---------|
+| Phase 1 (explore) | 30 | 232.00 | 394.48 |
+| Phase 2 (balanced) | 50 | 222.01 | 360.51 |
+| Phase 3 (exploit) | 57+ | **205.58** | 262.57 |
+
+**Key metrics:**
+- 100% feasibility rate
+- 100% unique designs (no duplicates)
+- Surrogate R² = 0.99
+
+### When to Use SAT vs Pure TPE
+
+| Scenario | Recommendation |
+|----------|----------------|
+| < 100 existing samples | Pure TPE (not enough for good surrogate) |
+| 100-500 samples | SAT Phase 1-2 only (no L-BFGS) |
+| > 500 samples | Full SAT with L-BFGS refinement |
+| High-dimensional (>20 params) | Pure TPE (curse of dimensionality) |
+| Noisy FEA | Pure TPE (surrogates struggle with noise) |
+
+### The Core Insight
+
+> "A surrogate that knows when it doesn't know is infinitely more valuable than one that's confidently wrong."
+
+SAT doesn't just optimize faster - it **optimizes safer**. Every prediction comes with uncertainty bounds. Every gradient step is validated. Every extrapolation is flagged.
+
+This is the difference between a tool that works in demos and a system that works in production.
+
+---
+
+# PART 9: THE EXTRACTOR LIBRARY
 
 ## 24 Physics Extractors
 
@@ -583,7 +691,7 @@ If you're writing more than 20 lines of extraction code in your study, you're pr
 
 ---
 
-# PART 9: DASHBOARD & VISUALIZATION
+# PART 10: DASHBOARD & VISUALIZATION
 
 ## Real-Time Monitoring
 
@@ -607,7 +715,7 @@ Automatic markdown reports with:
 
 ---
 
-# PART 10: STATISTICS & METRICS
+# PART 11: STATISTICS & METRICS
 
 ## Codebase
 
@@ -636,7 +744,7 @@ Automatic markdown reports with:
 
 ---
 
-# PART 11: KEY TAKEAWAYS
+# PART 12: KEY TAKEAWAYS
 
 ## What Makes Atomizer Different
 
@@ -645,6 +753,7 @@ Automatic markdown reports with:
 3. **Protocol evolution** - Safe, validated extensibility
 4. **MCP-first development** - Documentation-driven, not guessing
 5. **Simulation focus** - Not CAD, not mesh - optimization of simulation performance
+6. **Self-aware surrogates (SAT)** - Know when predictions are uncertain, validated WS=205.58
 
 ## Sound Bites for Podcast
 
@@ -653,6 +762,8 @@ Automatic markdown reports with:
 - "New capabilities go through research, review, and approval - just like engineering change orders."
 - "4.5 milliseconds per prediction means we can explore 50,000 designs before lunch."
 - "Every study makes the system smarter. That's not marketing - that's LAC."
+- "SAT knows when it doesn't know. A surrogate that's confidently wrong is worse than no surrogate at all."
+- "V5 surrogate said WS=280. FEA said WS=376. That's a 30% error from extrapolating into the unknown. SAT v3 fixed that - WS=205.58."
 
 ## The Core Message
 
@@ -672,9 +783,10 @@ This isn't just automation - it's **accumulated engineering intelligence**.
 ---
 
 **Document Statistics:**
-- Sections: 11
+- Sections: 12
 - Focus: Simulation optimization (not CAD/mesh)
-- Key additions: Study characterization, protocol evolution, MCP-first development
+- Key additions: Study characterization, protocol evolution, MCP-first development, SAT v3
 - Positioning: Optimizer & NX configurator, not "LLM-first"
+- SAT Performance: Validated WS=205.58 (best ever, beating V7 TPE at 218.26)
 
 **Prepared for NotebookLM/AI Podcast Generation**