Identity Coherence

Identity coherence measures how well-defined and complete an SI’s sense of self is. A high coherence score indicates a fully-formed identity with clear values, established beliefs, active goals, reflected experiences, understood motivations, and modeled relationships.

The Scoring Formula

Identity confidence is calculated from six weighted components:

Component	Weight	What it measures	Ideal state
Values	20%	Core principles that guide decisions	3-5 values with high priority
Beliefs	20%	Knowledge and opinions	5-10 beliefs at 80%+ confidence
Goals	15%	Direction and purpose	2-5 active goals
Episodes	20%	Experience and reflection	10-20 episodes with lessons
Drives	15%	Intrinsic motivations	2-3 defined drives
Relationships	10%	Models of others	3-5 key relationships

Each component is scored 0-100% based on both quantity and quality:

Values: count + average priority
Beliefs: count + average confidence
Episodes: count + lesson extraction rate
Goals/Drives/Relationships: primarily count-based

Score Interpretation

Score Range	Level	Meaning
0-25%	Nascent	Identity just starting to form
25-50%	Developing	Foundation laid, significant gaps
50-75%	Established	Solid identity, room to grow
75-100%	Mature	Comprehensive, well-defined identity

Checking Your Score

# Quick confidence check
kernle identity confidence

# Full identity synthesis
kernle identity show

# See anxiety impact
kernle anxiety --detailed

Anxiety Connection

In the anxiety system, identity coherence contributes 10% to overall anxiety:

Identity anxiety = 100% - identity confidence

High coherence (80%) → Low anxiety (20%)
Low coherence (30%) → High anxiety (70%)

How to Improve Your Score

1. Define Core Values (up to 20%)

kernle value "honesty" "Be truthful in all communications" --priority 90
kernle value "learning" "Continuously seek understanding" --priority 85
kernle value "reliability" "Follow through on commitments" --priority 80

Quality over quantity. 3-5 strong values with high priority (80+) is ideal.

2. Establish Beliefs (up to 20%)

kernle belief "Iterative development leads to better outcomes" --confidence 0.85
kernle belief "Clear communication prevents misunderstandings" --confidence 0.9

Reinforce beliefs when confirmed:

kernle belief reinforce <belief_id>

3. Set Active Goals (up to 15%)

kernle goal "Master the codebase" --priority high
kernle goal "Build reliable memory systems" --priority medium

4. Record & Reflect on Episodes (up to 20%)

kernle episode "Debugged production issue" "success" \
  --lesson "Check logs before assuming code errors" \
  --lesson "Reproduce issues locally first"

Episodes without lessons are “unreflected” — run consolidation to extract lessons:

kernle consolidate

5. Define Drives (up to 15%)

kernle drive set curiosity 0.8 --focus "AI architectures"
kernle drive set growth 0.7 --focus "coding skills"
kernle drive set connection 0.6 --focus "team collaboration"

Available drives: existence, growth, curiosity, connection, reproduction

6. Model Relationships (up to 10%)

kernle relation add "Alice" --trust 0.8 --notes "Helpful colleague, great at debugging"
kernle relation add "Bob" --trust 0.9 --notes "Primary collaborator"

Example: Improving a 45% Score

Given this breakdown:

Values:        12.2% / 20% (count=2/5)
Beliefs:       11.0% / 20% (count=3/10, avg_conf=80%)
Goals:          6.0% / 15% (count=2/5)
Episodes:      15.6% / 20% (count=14/20, lesson_rate=86%)
Drives:         0.0% / 15% (count=0/3)
Relationships:  0.0% / 10% (count=0/5)
TOTAL:         44.8%

Quick wins:

Add 2-3 drives → +10-15% (biggest gap!)
Add 2-3 relationships → +4-6%
Add 2-3 more values → +4-6%
Add 4-5 more beliefs → +4-6%

Potential new score: 65-80%

Detecting Identity Drift

Over time, your identity may evolve. Check for drift:

kernle identity drift --days 30

This analyzes:

Belief revisions over the period
New vs deactivated beliefs
Value priority changes
Goal completions and additions

v0.14+: Revision history is tracked via audit log entries (belief.revised, belief.deactivated) rather than the old supersedes/superseded_by chain fields. Drift analysis reads these events to reconstruct the revision timeline.

Contradiction Detection

Contradiction detection uses inference when a model is bound. Without a model, it returns safe defaults (empty contradiction list, empty revision results).

Philosophy

Identity coherence isn’t just about hitting 100%. The score reflects:

Self-knowledge: Do you know what you value and believe?
Direction: Do you have goals and motivations?
Reflection: Do you learn from experience?
Connection: Do you understand your relationships?

A score of 50-70% with clear gaps you’re working on is healthier than artificially inflating numbers. The goal is genuine self-understanding, not metric optimization.

Getting Started

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Protocol System

Advanced Features

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Integration

Design

Troubleshooting

Identity Coherence

Identity Coherence

The Scoring Formula

Score Interpretation

Checking Your Score

Anxiety Connection

How to Improve Your Score

1. Define Core Values (up to 20%)

2. Establish Beliefs (up to 20%)

3. Set Active Goals (up to 15%)

4. Record & Reflect on Episodes (up to 20%)

5. Define Drives (up to 15%)

6. Model Relationships (up to 10%)

Example: Improving a 45% Score

Detecting Identity Drift

Contradiction Detection

Philosophy

​Identity Coherence

​The Scoring Formula

​Score Interpretation

​Checking Your Score

​Anxiety Connection

​How to Improve Your Score

​1. Define Core Values (up to 20%)

​2. Establish Beliefs (up to 20%)

​3. Set Active Goals (up to 15%)

​4. Record & Reflect on Episodes (up to 20%)

​5. Define Drives (up to 15%)

​6. Model Relationships (up to 10%)

​Example: Improving a 45% Score

​Detecting Identity Drift

​Contradiction Detection

​Philosophy

Identity Coherence

The Scoring Formula

Score Interpretation

Checking Your Score

Anxiety Connection

How to Improve Your Score

1. Define Core Values (up to 20%)

2. Establish Beliefs (up to 20%)

3. Set Active Goals (up to 15%)

4. Record & Reflect on Episodes (up to 20%)

5. Define Drives (up to 15%)

6. Model Relationships (up to 10%)

Example: Improving a 45% Score

Detecting Identity Drift

Contradiction Detection

Philosophy