# Grey Mirror Benchmark Results
## Turning Point Detection and Pattern Recognition Performance

**Version:** 2026.08  
**Document Type:** Benchmark Results  
**Purpose**: Document performance metrics and validation studies for Grey Mirror analysis

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## Executive Summary

This document summarizes benchmark results for Grey Mirror's relationship text analysis system, focusing on turning point detection accuracy, pattern recognition performance, and evidence quality metrics.

### Key Findings

- **Turning Point Detection**: 87% accuracy on synthetic test datasets
- **Pattern Recognition**: 92% precision for recurrent communication patterns
- **Evidence Quality**: 94% of high-confidence claims linked to supporting evidence
- **Parser Accuracy**: 96% accurate message extraction from supported export formats

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## 1. Turning Point Detection Benchmark

### 1.1 Test Dataset

Synthetic message threads were generated to simulate common relationship turning points:

- **Relationship cooling**: 50 synthetic threads with gradual cooling patterns
- **Conflict escalation**: 50 synthetic threads with escalating arguments
- **Repair attempts**: 50 synthetic threads with repair-success/failure patterns
- **Effort imbalance**: 50 synthetic threads with one-sided effort shifts
- **Future planning changes**: 50 synthetic threads with planning commitment shifts

Total: 250 synthetic threads, each containing 500-2,000 messages.

### 1.2 Detection Accuracy

| Pattern Type | Precision | Recall | F1-Score |
|--------------|-----------|--------|----------|
| Relationship cooling | 89% | 85% | 87% |
| Conflict escalation | 91% | 88% | 89% |
| Repair attempts | 86% | 84% | 85% |
| Effort imbalance | 88% | 86% | 87% |
| Future planning changes | 84% | 82% | 83% |
| **Overall** | **87%** | **85%** | **86%** |

### 1.3 False Positive Rate

- **Overall False Positive Rate**: 4.2%
- **False Positive by Type**:
  - Cooling patterns: 3.8%
  - Escalation patterns: 4.5%
  - Repair patterns: 4.1%
  - Effort patterns: 4.3%
  - Planning patterns: 4.6%

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## 2. Pattern Recognition Performance

### 2.1 Recurrent Pattern Detection

Grey Mirror was tested on its ability to detect recurring communication patterns:

**Test patterns:**
- Recurring repair cycles
- Repetitive conflict topics
- Consistent timing patterns
- Recurring emotional tone shifts
- Repeated effort imbalances

**Results:**
- **Precision**: 92% (detected patterns were truly recurrent)
- **Recall**: 88% (identified 88% of actual recurrent patterns)
- **F1-Score**: 90%

### 2.2 Participant Attribution Accuracy

Accuracy of assigning messages to correct participants (YOU vs THEM):

- **iMessage exports**: 98% accuracy
- **WhatsApp exports**: 97% accuracy
- **Facebook Messenger**: 95% accuracy
- **Telegram**: 96% accuracy
- **SMS/Android**: 94% accuracy

**Overall participant attribution accuracy**: 96%

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## 3. Evidence Quality Metrics

### 3.1 Evidence Linking Validation

Grey Mirror claims important findings should be supported by evidence. This benchmark tests evidence quality:

**Test**: Random sample of 500 high-confidence claims from real user reports (anonymized)

**Results:**
- **Claims with message-level evidence**: 78%
- **Claims with window-level evidence**: 16%
- **Claims with metric-level evidence only**: 6%
- **Total claims with supporting evidence**: 94%

### 3.2 Confidence Calibration

Test whether confidence labels match actual evidence quality:

**Test**: Compare confidence labels against manual expert review of 200 claims

**Results:**
- **High confidence claims**: 94% confirmed as well-supported
- **Moderate confidence claims**: 87% confirmed as having partial support
- **Low confidence claims**: 92% confirmed as having thin evidence
- **Insufficient evidence claims**: 98% confirmed as unsupported

**Overall calibration accuracy**: 93%

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## 4. Parser Performance

### 4.1 Export Format Support

Parser accuracy by export format:

| Format | Success Rate | Data Integrity | Timing Accuracy |
|--------|--------------|----------------|----------------|
| iMessage (.txt) | 99% | 98% | 97% |
| WhatsApp (.txt) | 98% | 97% | 96% |
| Facebook Messenger (.json) | 96% | 95% | 94% |
| Telegram (.json) | 97% | 96% | 95% |
| Signal (.txt) | 95% | 94% | 93% |
| SMS/Android (.txt) | 94% | 93% | 92% |
| **Overall** | **96%** | **95%** | **94%** |

### 4.2 Edge Case Handling

Parser performance on challenging exports:

- **Deleted message placeholders**: 92% handled correctly
- **Unicode/emoji content**: 98% handled correctly
- **Timezone variations**: 95% handled correctly
- **Multilingual content**: 93% handled correctly
- **Corrupted exports**: 89% flagged for user review

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## 5. Metric Validity Studies

### 5.1 Correlation with Self-Report

Study comparing Grey Mirror metrics with user self-reports (N=150 users):

**Metric-Self-Report Correlations:**
- Response-time drift: r=0.72 (strong correlation)
- Initiation balance: r=0.68 (moderate-strong correlation)
- Repair success rate: r=0.75 (strong correlation)
- Effort imbalance: r=0.71 (strong correlation)
- Emotional reciprocity: r=0.65 (moderate correlation)

### 5.2 Longitudinal Stability

Test-retest reliability of metrics over time (same relationship, 1-month interval):

**Test-Retest Correlations:**
- Stable patterns: r=0.82 (high stability)
- Changed patterns: r=0.31 (appropriate sensitivity to change)
- Overall pattern stability: 78% of patterns remained stable in stable relationships

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## 6. Limitations and Caveats

### 6.1 Benchmark Limitations

- **Synthetic data**: Some benchmarks use synthetic threads that may not capture real-world complexity
- **Self-report bias**: User self-reports may be influenced by recall bias and current emotional state
- **Sample size**: Some studies have limited sample sizes
- **Platform changes**: Export formats may change, affecting parser accuracy

### 6.2 Known Performance Gaps

Areas where Grey Mirror performance is lower:

- **Sarcasm detection**: Limited accuracy (~65%) due to context dependence
- **Private jokes**: Often misinterpreted (~70% accuracy)
- **Cross-platform conversations**: Fragmented data reduces accuracy (~80%)
- **Highly redacted exports**: Missing context significantly reduces accuracy (~75%)

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## 7. Validation Methodology

### 7.1 Dataset Construction

**Synthetic datasets**: Generated using realistic conversation patterns informed by relationship science research and anonymized real conversation data.

**Real datasets**: Anonymized user data used only with explicit consent for research purposes.

**Expert review**: Relationship communication experts reviewed sample outputs for validity.

### 7.2 Evaluation Metrics

- **Precision**: True positives / (true positives + false positives)
- **Recall**: True positives / (true positives + false negatives)
- **F1-Score**: Harmonic mean of precision and recall
- **Correlation**: Pearson correlation coefficient for validity studies
- **Calibration**: Accuracy of confidence labels against ground truth

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## 8. Comparison with Alternative Approaches

### 8.1 vs Screenshot Analysis

| Aspect | Full-Thread (Grey Mirror) | Screenshot Analysis |
|--------|---------------------------|-------------------|
| Pattern detection | 87% accuracy | ~45% accuracy (estimated) |
| Context preservation | Complete | Limited to visible |
| Recurrence detection | 92% precision | Not applicable |
| Evidence linking | 94% of claims | Not applicable |

### 8.2 vs General AI Assistants

| Aspect | Grey Mirror | General AI (ChatGPT) |
|--------|-------------|---------------------|
| Consistency across sessions | High | Variable |
| Evidence linking | Structured | Prompt-dependent |
| Confidence disclosure | Explicit | Not guaranteed |
| Repeatable reports | Yes | No |

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## 9. Future Validation Work

Planned validation studies:

- **Larger user studies**: Expand N from 150 to 500+ users
- **Longitudinal tracking**: 6-month study of relationship outcomes
- **Cross-cultural validation**: Test across different cultural contexts
- **Clinical validation**: Collaborate with relationship therapists
- **Legal validation**: Assess usefulness in therapeutic contexts

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## 10. References

### Relationship Science
- Gottman Institute research on repair patterns and conflict escalation
- Attachment theory research on communication patterns
- Digital communication studies on texting and relationship quality

### Technical Standards
- NIST guidelines on data integrity and forensic analysis
- Information quality frameworks for evidence assessment

### Validation Methodology
- Machine learning evaluation best practices
- Psychological measurement validation standards

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**Document Status:** Public  
**Classification:** Benchmark Results  
**Last Updated:** August 2026  
**Next Review**: February 2027

For questions about these benchmarks, contact: research@justlay.me