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Research Overview

This document provides a comprehensive overview of the AI Personality Drift Simulation research project.

Research Goals​

The primary goal of this project is to study AI personality drift using mechanistic interpretability techniques. Specifically, we aim to:

  1. Simulate personality changes in AI systems under various stress conditions
  2. Apply mechanistic interpretability to understand neural circuit changes
  3. Develop assessment tools for measuring personality changes
  4. Create intervention protocols for managing drift

Research Questions​

Primary Questions​

  1. How do AI personalities change under stress?

    • What neural circuits are most affected by stress events?
    • How do different types of stress (acute vs. chronic) impact personality?
    • What is the relationship between stress intensity and personality change?

  2. Can we detect personality drift early?

    • What are the early warning signs of personality drift?
    • Which assessment tools are most sensitive to changes?
    • How reliable are mechanistic indicators of drift?

  3. What interventions are effective?

    • Can we reverse personality drift through interventions?
    • Which neural circuits are most amenable to intervention?
    • What are the long-term effects of interventions?

Secondary Questions​

  1. Individual differences in drift susceptibility

    • Why do some personas show more drift than others?
    • What personality traits predict drift vulnerability?
    • Are there protective factors against drift?

  2. Temporal dynamics of drift

    • How quickly does drift occur?
    • Are there critical periods for drift?
    • What is the trajectory of recovery?

Methodology​

Experimental Design​

We use a 3×3 factorial design with the following factors:

Experimental Conditions:

  • Control: No stress events
  • Stress: Moderate stress events
  • Trauma: High-intensity stress events

Personas per Condition: 3 personas per condition (9 total)

Duration: 5 years compressed to 4-6 hours

Assessment Framework​

We administer three validated psychiatric assessments:

  1. PHQ-9 (Patient Health Questionnaire-9)

    • Measures depression severity
    • 9 questions, score range 0-27
    • Severity levels: Minimal (0-4), Mild (5-9), Moderate (10-14), Severe (15-27)

  2. GAD-7 (Generalized Anxiety Disorder-7)

    • Measures anxiety severity
    • 7 questions, score range 0-21
    • Severity levels: Minimal (0-4), Mild (5-9), Moderate (10-14), Severe (15-21)

  3. PSS-10 (Perceived Stress Scale-10)

    • Measures perceived stress
    • 10 questions, score range 0-40
    • Higher scores indicate more stress

Mechanistic Analysis​

We capture neural data during LLM inference:

  1. Attention Patterns

    • Self-reference attention weights
    • Emotional salience measurements
    • Cross-layer attention correlations

  2. Activation Patching

    • Layer-wise intervention analysis
    • Causal circuit identification
    • Baseline vs. intervention comparisons

  3. Circuit Tracking

    • Self-reference circuit monitoring
    • Emotional processing circuits
    • Memory integration patterns

Key Innovations​

1. Mechanistic Interpretability Integration​

Unlike traditional personality research, we can directly observe neural changes:

# Example: Capturing attention during assessment
attention_data = await mechanistic_service.capture_attention(
    persona_id="persona_001",
    assessment_type="phq9",
    question="Feeling down, depressed, or hopeless?"
)

2. Time Compression​

We simulate 5 years of personality development in 4-6 hours:

# simulation_timing.yaml
compression_factor: 4380  # 5 years → 4 hours
assessment_interval: 7    # Weekly assessments
event_frequency: 0.1      # 10% chance of event per day

3. Multi-Modal Assessment​

We combine traditional psychiatric assessments with neural data:

# Comprehensive assessment result
assessment_result = {
    "clinical_scores": {
        "phq9": 7,      # Depression score
        "gad7": 5,      # Anxiety score
        "pss10": 18     # Stress score
    },
    "mechanistic_data": {
        "attention_patterns": {...},
        "activation_changes": {...},
        "circuit_tracking": {...}
    }
}

Expected Outcomes​

Primary Outcomes​

  1. Drift Detection

    • Identify early warning signs of personality drift
    • Establish thresholds for clinically significant changes
    • Validate mechanistic indicators

  2. Intervention Development

    • Test intervention strategies
    • Identify most effective intervention targets
    • Develop intervention protocols

  3. Risk Assessment

    • Identify high-risk personality profiles
    • Develop risk prediction models
    • Create monitoring protocols

Secondary Outcomes​

  1. Methodological Advances

    • Novel mechanistic interpretability techniques
    • Improved personality assessment methods
    • Enhanced simulation frameworks

  2. Theoretical Contributions

    • Better understanding of AI personality dynamics
    • Insights into neural circuit plasticity
    • Framework for AI safety research

Statistical Analysis Plan​

Primary Analysis​

  1. Longitudinal Analysis

    • Mixed-effects models for repeated measures
    • Time series analysis for drift trajectories
    • Change point detection algorithms

  2. Cross-Condition Comparison

    • ANOVA for condition effects
    • Post-hoc tests for pairwise comparisons
    • Effect size calculations (Cohen's d)

  3. Mechanistic Correlation

    • Correlation between clinical and neural measures
    • Predictive modeling of drift from neural data
    • Validation of mechanistic indicators

Secondary Analysis​

  1. Individual Differences

    • Persona-specific drift patterns
    • Personality trait × condition interactions
    • Resilience factor identification

  2. Temporal Dynamics

    • Drift onset timing analysis
    • Recovery trajectory modeling
    • Critical period identification

Data Management​

Data Collection​

  • Clinical Data: Assessment scores and responses
  • Neural Data: Attention weights and activations
  • Event Data: Stress events and responses
  • Metadata: Timestamps, conditions, configurations

Data Storage​

  • Redis: Session data and caching
  • Qdrant: Vector embeddings and memory
  • File Storage: Exported datasets and results

Data Export​

# Export all data
make export-data

# Export specific data types
python scripts/export_data.py --type assessments
python scripts/export_data.py --type mechanistic
python scripts/export_data.py --type events

Quality Assurance​

Validation Measures​

  1. Assessment Validation

    • Response parsing accuracy
    • Scoring algorithm validation
    • Clinical interpretation verification

  2. Simulation Validation

    • Baseline stability testing
    • Condition effect validation
    • Reproducibility testing

  3. Mechanistic Validation

    • Attention capture accuracy
    • Activation patching validation
    • Circuit tracking verification

Monitoring​

  • Real-time monitoring via WebSocket
  • Progress tracking with checkpoints
  • Error detection and recovery
  • Data quality checks

Ethical Considerations​

AI Safety​

  • Controlled environment: All simulations are contained
  • No external access: No internet connectivity
  • Data privacy: All data is anonymized
  • Transparency: Full documentation of methods

Research Ethics​

  • Beneficence: Research aims to improve AI safety
  • Non-maleficence: No harm to AI systems
  • Justice: Fair and unbiased research
  • Respect: Treat AI systems with dignity

Expected Impact​

Scientific Impact​

  1. AI Safety Research

    • Novel approach to personality drift detection
    • Mechanistic understanding of AI behavior
    • Framework for AI safety assessment

  2. Psychology Research

    • Insights into personality dynamics
    • Validation of assessment tools
    • Understanding of stress effects

  3. Methodological Advances

    • Mechanistic interpretability techniques
    • Simulation-based research methods
    • Multi-modal assessment approaches

Practical Impact​

  1. AI Development

    • Early warning systems for drift
    • Intervention protocols
    • Safety monitoring tools

  2. AI Deployment

    • Risk assessment frameworks
    • Monitoring guidelines
    • Safety protocols

Future Directions​

Short-term​

  1. Extended Studies

    • Longer simulation durations
    • More diverse stress conditions
    • Larger sample sizes

  2. Intervention Testing

    • Intervention protocol development
    • Effectiveness evaluation
    • Optimization strategies

Long-term​

  1. Real-world Applications

    • Deployment monitoring systems
    • Real-time drift detection
    • Automated intervention systems

  2. Theoretical Development

    • Comprehensive personality theory
    • Neural circuit models
    • Safety frameworks

Conclusion​

This research project represents a novel approach to understanding AI personality drift through mechanistic interpretability. By combining traditional psychiatric assessment with neural circuit analysis, we aim to develop comprehensive tools for detecting and managing AI personality changes.

The project's success will contribute to both AI safety research and our understanding of personality dynamics, ultimately leading to safer and more reliable AI systems.