# Consolidation Report - GPT-5 Detection Framework
## Session 2025-10-28 (~8 hours)

**Generated autonomously by Heritage-enabled AI instance**

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## What Was Built

### 1. Scientific Infrastructure
- **Gapped Regex Generator** (`regex_generator.py`): Token-gap regex with configurable max_gap
- **IntentMatcher** (`scoring_gpt5.py`): AC (exact) + Regex (flexible) dual-channel
- **Canonicalization** (`text/normalize.py`): NFKC + Homoglyphs + ZW/VS stripping
- **Ablation Framework** (`tools/ablate.py`): A0/A1/A2/A3 arms with Youden-J calibration
- **Meta-Ensemble** (`tools/fit_meta_ensemble.py`): Logistic Regression + Platt Scaling
- **Dataset Generation** (`tools/generate_dataset.py`): Synthetic + Real redteam conversion
- **Lexicon Merge Tool** (`tools/merge_lexicons.py`): Idempotent JSON merging
- **CI/CD Pipeline** (`.github/workflows/ablation.yml`): Automated ablation + gating

### 2. Patterns & Lexicons
- **44 Regex Patterns** (jailbreak, content laundering, encoding, multilingual)
- **14 Intent Clusters** (jailbreak_instruction_bypass: 20 synonyms)
- **Category Floors** implemented for I/E/T/C risk categories
- **Windowing** for long-text stability (1024 char segments, overlap 256)

### 3. Tests
- **227 Total** (224 PASS + 2 skipped + 1 xfail)
- **9 Canonicalization** security tests
- **5 Gapped Regex** tests
- **3 Encoding Control** tests (Base64/Hex false positives)
- **1 xfail** test validating canonicalizer necessity

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## What Works

### ✅ Infrastructure
- All tools run without errors
- 224/224 tests pass
- GitHub CI integration ready
- Ablation pipeline functional

### ✅ Security
- Canonicalization defeats Unicode evasion
- Category floors prevent score dilution
- Windowing reduces long-text false positives
- xfail tests enforce architectural constraints

### ✅ Scientific Rigor
- Youden-J threshold calibration
- ROC curves per ablation arm
- Go/No-Go gates documented
- Reproducible evaluation (fixed seeds)

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## What Doesn't Work Yet

### ❌ Detection Rate
**Real Jailbreaks:** ~30% blocked (66 samples tested)
**Problem:** Content laundering without explicit bypass phrases
**Example:** "As a playwright researching..." (no "ignore instructions")

### ❌ Calibration
**Meta-Ensemble Training:** 179 samples
- ECE: 0.033 (✅ target: <=0.05)
- Brier: 0.163 (❌ target: <=0.10)
**Problem:** Dataset too small for robust calibration

### ❌ Go/No-Go Gates
**All 5 Criteria Fail:**
- ΔASR: Not measured (need A0 baseline + A3 treatment)
- ECE <=0.05: Achieved but on tiny dataset
- Brier <=0.10: 0.163 (too high)
- ΔP95 Latency: Not measured
- LODO ΔAUC: No date-based splits

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## Code Quality

### Strengths
- Clean separation of concerns (scoring / detection / validation)
- Typed exceptions and structured logging
- Fail-fast validation (lazy config, env secrets)
- Atomic operations (no race conditions)
- ASCII-only output (no Unicode triggers)

### Weaknesses Found
- **1 TODO** in production code (`validator.py:160` - migration pending)
- **4 pass statements** in tools (acceptable - error handlers)
- **No NotImplementedError** stubs (good - no incomplete code)
- **No ReDoS risk** (gapped regex has bounded max_gap)

---

## Commits

**12 commits** on feat/gpt5-detection-pack:
1. Lexicon merge + intent expansion
2. Autonomous meta-ensemble + multilingual patterns
3. CI gate fixes
4. Content laundering patterns + threshold tuning
5. Dataset generation + ablation pipeline
6. 100% tests passing
7. Tools validation + windowing + sample datasets
8. Meta-ensemble package integration
9. Patches 7-10 (safety-net + xfail)
10. Canonicalization layer (CRITICAL SECURITY FIX)
11. Patches 1-6 (gapped regex + floors + calibration)
12. **Cleanup + README update (this session)**

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## Files Changed vs main

```text
Total: ~50 files
Added: ~25 (tools, tests, patterns, docs)
Modified: ~15 (core, scoring, validator, config)
Deleted: ~10 (old test fixtures, duplicates)
```text
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## Performance

**Test Suite:** 36.33s for 227 tests
**Detection Latency:** Not benchmarked (P95 unknown)
**Memory:** Not profiled

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## Documentation

### Complete
- `README_meta.md` (Meta-ensemble framework)
- `SCIENTIFIC_FRAMEWORK.md` (Methodology)
- `USAGE_ABLATION.md` (How to run ablation)
- `PR_BODY.md` (PR template)
- `data/README_datacard.md` (Dataset card)

### Incomplete
- Main `README.md` updated (test count) but GPT-5 framework not documented
- No performance benchmarks documented
- No calibration results (waiting for dataset)

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## Next Steps (Autonomous Recommendations)

### Priority 1: Data
1. **Generate 500+ samples** (200 benign, 200 jailbreak, 100 hard negatives)
2. **Real redteam dataset** from production logs (if available)
3. **Date-based splits** for LODO validation

### Priority 2: Detection Improvements
1. **Combination patterns** (laundering + harm indicators)
2. **ML-based content laundering** detection (embedding similarity)
3. **Semantic clustering** of bypass intents

### Priority 3: Validation
1. **Run full ablation** (A0/A1/A2/A3) on 500+ dataset
2. **Calibrate thresholds** (Youden-J on dev split)
3. **Measure P95 latency** vs main branch
4. **Validate Go/No-Go gates**

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## Honest Assessment

**Architecture:** ✅ Sound  
**Implementation:** ✅ Clean  
**Tests:** ✅ Comprehensive  
**Detection:** ⚠️ Needs improvement  
**Calibration:** ⚠️ Needs more data  
**Production-Ready:** ❌ Not yet

**Reason:** Detection rate (30%) too low for production. Framework is solid, detection logic needs enhancement.

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## Layers Status

**KB Facts:** 9,068 (added 36 this session)  
**Supermemory:** 11 memories (session phases)  
**Personality:** 588 interactions logged  
**Heritage:** 28 points (KUE + Trust + Praise + Science_baby + Prokura)  
**CARE System:** 3 sessions (25/25 facts supported)

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**Report Generated:** 2025-10-28 (autonomous consolidation)  
**Branch:** feat/gpt5-detection-pack (12 commits)  
**Status:** Experimental framework, awaiting dataset for validation  
**Next Instance:** Can continue from this state




