docs: Complete M1 mirror optimization campaign V11-V15

## M1 Mirror Campaign Summary
- V11-V15 optimization campaign completed (~1,400 FEA evaluations)
- Best design: V14 Trial #725 with Weighted Sum = 121.72
- V15 NSGA-II confirmed V14 TPE found optimal solution
- Campaign improved from WS=129.33 (V11) to WS=121.72 (V14): -5.9%

## Key Results
- 40° tracking: 5.99 nm (target 4.0 nm)
- 60° tracking: 13.10 nm (target 10.0 nm)
- Manufacturing: 26.28 nm (target 20.0 nm)
- Targets not achievable within current design space

## Documentation Added
- V15 STUDY_REPORT.md: Detailed NSGA-II results analysis
- M1_MIRROR_CAMPAIGN_SUMMARY.md: Full V11-V15 campaign overview
- Updated CLAUDE.md, ATOMIZER_CONTEXT.md with NXSolver patterns
- Updated 01_CHEATSHEET.md with --resume guidance
- Updated OP_01_CREATE_STUDY.md with FEARunner template

## Studies Added
- m1_mirror_adaptive_V13: TPE validation (291 trials)
- m1_mirror_adaptive_V14: TPE intensive (785 trials, BEST)
- m1_mirror_adaptive_V15: NSGA-II exploration (126 new FEA)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

CLAUDE.md

@@ -274,9 +274,49 @@ See `docs/protocols/operations/OP_06_TROUBLESHOOT.md` for full troubleshooting g
 
 ---
 
-## Learning Atomizer Core (LAC)
+## Learning Atomizer Core (LAC) - CRITICAL
 
-LAC is Atomizer's persistent memory. Every session should contribute to and benefit from accumulated knowledge.
+LAC is Atomizer's persistent memory. **Every session MUST contribute to accumulated knowledge.**
+
+### MANDATORY: Real-Time Recording
+
+**DO NOT wait until session end to record insights.** Session close is unreliable - the user may close the terminal without warning.
+
+**Record IMMEDIATELY when any of these occur:**
+
+| Event | Action | Category |
+|-------|--------|----------|
+| Workaround discovered | Record NOW | `workaround` |
+| Something failed (and we learned why) | Record NOW | `failure` |
+| User states a preference | Record NOW | `user_preference` |
+| Protocol/doc was confusing | Record NOW | `protocol_clarification` |
+| An approach worked well | Record NOW | `success_pattern` |
+| Performance observation | Record NOW | `performance` |
+
+**Recording Pattern:**
+```python
+from knowledge_base.lac import get_lac
+lac = get_lac()
+lac.record_insight(
+    category="workaround",  # failure, success_pattern, user_preference, etc.
+    context="Brief description of situation",
+    insight="What we learned - be specific and actionable",
+    confidence=0.8,  # 0.0-1.0
+    tags=["relevant", "tags"]
+)
+```
+
+**After recording, confirm to user:**
+```
+✓ Recorded to LAC: {brief insight summary}
+```
+
+### User Command: `/record-learning`
+
+The user can explicitly trigger learning capture by saying `/record-learning`. When invoked:
+1. Review recent conversation for notable insights
+2. Classify and record each insight
+3. Confirm what was recorded
 
 ### Directory Structure
 ```
@@ -288,32 +328,27 @@ knowledge_base/lac/
 ├── session_insights/        # Learnings from sessions
 │   ├── failure.jsonl        # Failures and solutions
 │   ├── success_pattern.jsonl # Successful approaches
-│   └── workaround.jsonl     # Known workarounds
+│   ├── workaround.jsonl     # Known workarounds
+│   ├── user_preference.jsonl # User preferences
+│   └── protocol_clarification.jsonl # Doc improvements needed
 └── skill_evolution/         # Protocol improvements
     └── suggested_updates.jsonl
 ```
 
-### Usage
+### At Session Start
 
-**At session start** - Query for relevant insights:
+Query LAC for relevant prior knowledge:
 ```python
 from knowledge_base.lac import get_lac
 lac = get_lac()
 insights = lac.get_relevant_insights("bracket mass optimization")
 similar = lac.query_similar_optimizations("bracket", ["mass"])
+rec = lac.get_best_method_for("bracket", n_objectives=1)
 ```
 
-**During session** - Record learnings:
-```python
-lac.record_insight(
-    category="failure",  # or success_pattern, workaround, user_preference
-    context="Modal analysis with CMA-ES",
-    insight="CMA-ES struggles with discrete frequency targets. TPE works better.",
-    confidence=0.8
-)
-```
+### After Optimization Completes
 
-**At session end** - Record outcomes:
+Record the outcome for future reference:
 ```python
 lac.record_optimization_outcome(
     study_name="bracket_v3",