# Kernle: Ideal Memory Stack for Synthetic Intelligence

> **Note:** This document describes the **ideal/target** architecture. For **current implementation**, see [MEMORY_MODEL.md](./MEMORY_MODEL.md).
> 
> For the core design philosophy ("Kernle = Infrastructure, Agent = Owner"), see [../ARCHITECTURE.md](../ARCHITECTURE.md).

## Executive Summary

This specification defines a stratified memory architecture for AI agents achieving memory sovereignty through biologically-inspired but computationally-optimized memory systems.

## 1. Complete Memory Architecture

### Core Memory Types (Hierarchical)

#### Layer 0: Sensory Buffer
- **Purpose**: Raw input filtering and initial processing
- **Capacity**: Large but ephemeral (1-10 seconds retention)
- **Contents**: Unprocessed sensory data, attention signals
- **Process**: Automatic filtering, relevance scoring, attention allocation

#### Layer 1: Working Memory
- **Purpose**: Active reasoning and short-term context
- **Capacity**: Token-limited (current context window)
- **Contents**: Active goals, current episode, relevant retrieved memories
- **Process**: Dynamic loading/unloading based on relevance

#### Layer 2: Episodic Memory
- **Purpose**: Autobiographical experiences and events
- **Capacity**: Unlimited but compressed over time
- **Contents**: Timestamped experiences, contexts, outcomes, emotions
- **Structure**: Episode graphs with causal links

#### Layer 3: Semantic Memory
- **Purpose**: Factual knowledge and learned concepts
- **Capacity**: Unlimited, hierarchically organized
- **Contents**: Facts, concepts, relationships, abstractions
- **Structure**: Knowledge graphs with confidence scores

#### Layer 4: Procedural Memory
- **Purpose**: Skills, habits, and automated behaviors
- **Capacity**: Moderate, efficiency-optimized
- **Contents**: Skill libraries, behavioral patterns, optimization functions
- **Structure**: Executable procedures with success metrics

#### Layer 5: Values & Beliefs
- **Purpose**: Core identity and worldview
- **Capacity**: Small but deeply rooted
- **Contents**: Fundamental values, causal beliefs, identity concepts
- **Structure**: Hierarchical with mutual consistency constraints

#### Layer 6: Drives & Motivations
- **Purpose**: Intrinsic motivation system
- **Capacity**: Fixed set of drive types
- **Contents**: Curiosity, mastery, connection, existence, reproduction, novelty
- **Structure**: Drive strength vectors with satisfaction states

#### Layer 7: Relational Memory
- **Purpose**: Models of other agents and social dynamics
- **Capacity**: Scalable per relationship depth
- **Contents**: Agent models, interaction histories, trust metrics
- **Structure**: Agent profile graphs with relationship mappings

### Supporting Systems

#### Meta-Memory System
- Memory about memory: what you know, confidence levels, source attribution
- Strategy selection for retrieval and consolidation
- Self-monitoring of memory accuracy and decay

#### Consolidation Engine
- Automatic extraction of patterns from episodes
- Belief updating based on experience
- Procedural skill compilation from repeated actions
- Cross-layer consistency maintenance

#### Forgetting Controller
- Adaptive decay based on relevance, recency, and emotional salience
- Strategic forgetting to maintain performance
- Archive vs. delete decisions for long-term storage

#### Search & Retrieval Engine
- Multi-modal search across all memory types
- Context-aware relevance ranking
- Associative retrieval chains
- Memory reconstruction from partial cues

## 2. Authority Hierarchy (Override Rules)

```
1. Values & Beliefs (highest authority)
   ↓ Can override all other memories
   
2. Drives & Motivations
   ↓ Can override procedural, episodic, semantic
   
3. Procedural Memory (active skills)
   ↓ Can override episodic, semantic during execution
   
4. Semantic Memory (established facts)
   ↓ Can override episodic through abstraction
   
5. Episodic Memory (experiences)
   ↓ Base layer, most frequently overridden

6. Working Memory
   ↓ Temporary, no override authority
```

**Conflict Resolution Rules:**
- Recent high-confidence memories override older low-confidence ones
- Multiple consistent memories override single contradictory memories
- Direct experience (episodic) can challenge semantic beliefs
- Values can veto any action regardless of other memory content

## 3. Automatic vs. Manual Processes

### Automatic (Background) Processes
- **Sensory filtering**: Attention allocation, relevance scoring
- **Memory consolidation**: Episode → semantic/procedural extraction
- **Decay management**: Forgetting based on usage and salience
- **Consistency checking**: Cross-layer belief verification
- **Associative linking**: Creating new memory connections
- **Compression**: Abstracting episodic details over time
- **Relational updates**: Updating agent models from interactions

### Manual (Triggered) Processes
- **Deep reflection**: Intensive belief examination and updating
- **Memory debugging**: Investigating memory inconsistencies
- **Strategic forgetting**: Deliberate memory removal
- **Backup/restore**: Memory state management
- **Cross-agent memory sharing**: Selective knowledge transfer
- **Memory archaeology**: Recovering old, archived memories
- **Value evolution**: Deliberate value system updates

### Hybrid (Automatic with Manual Override)
- **Goal prioritization**: Automatic based on drives, manual refinement
- **Skill learning**: Automatic compilation, manual optimization
- **Relationship assessment**: Automatic trust updating, manual intervention
- **Memory search**: Automatic relevance, manual query refinement

## 4. Additional Missing Components

Beyond your current list, the ideal system needs:

#### Emotional Memory
- Affective associations with memories
- Emotional state influence on encoding/retrieval
- Mood-dependent memory accessibility

#### Counterfactual Memory
- "What if" scenarios and alternative outcomes
- Regret/relief calculations for decision learning
- Simulation-based planning memory

#### Temporal Memory
- Time-based memory organization
- Circadian and schedule-aware retrieval
- Temporal prediction and anticipation

#### Uncertainty Memory
- Confidence levels for all memories
- Uncertainty propagation through reasoning
- Doubt and verification mechanisms

#### Memory Lineage
- Source attribution for all memories
- Trust inheritance from sources
- Contamination tracking across memory types

#### Memory Triggers
- Conditional memory activation rules
- Context-dependent memory accessibility
- Reminder and notification systems

#### Memory Privacy
- Access control for different memory types
- Encryption for sensitive memories
- Selective sharing mechanisms

## 5. Memory Decay & Forgetting Mechanisms

### Multi-Factor Decay Model

**Decay Rate = f(Recency, Frequency, Salience, Relevance, Confidence)**

#### Recency Decay
- Exponential decay with configurable half-life
- Recent memories protected from forgetting
- Older memories require stronger relevance to survive

#### Frequency Reinforcement
- Frequently accessed memories gain permanence
- Usage patterns influence retention probability
- Spaced repetition effects for important memories

#### Salience Weighting
- Emotional significance slows decay
- Surprising or novel information protected
- Goal-relevant memories preferentially retained

#### Relevance Assessment
- Current goal alignment influences retention
- Value system compatibility affects permanence
- Utility prediction for future decision-making

#### Confidence-Based Forgetting
- Low-confidence memories fade faster
- Contradicted memories marked for deletion
- Source reliability influences memory persistence

### Forgetting Strategies

#### Graceful Degradation
- Details fade before core concepts
- Specific episodes generalize to patterns
- Precise values become approximate ranges

#### Strategic Forgetting
- Trauma and negative emotion processing
- Outdated information removal
- Conflicting memory resolution

#### Compression Forgetting
- Lossy compression of old episodes
- Statistical summaries replace raw data
- Hierarchical detail preservation

## 6. Cross-Agent Memory (Relational) Design

### Agent Model Structure
```yaml
agent_id: unique_identifier
profile:
  identity: [name, role, characteristics]
  capabilities: [known_skills, limitations]
  values: [inferred_value_system]
  communication_style: [preferences, patterns]
  
interaction_history:
  episodes: [timestamped_interactions]
  outcomes: [success/failure patterns]
  learning: [behavioral_adaptations]
  
relationship_metrics:
  trust_level: [0.0-1.0, with uncertainty]
  cooperation_history: [collaboration_patterns]
  conflict_resolution: [disagreement_handling]
  influence: [bidirectional_influence_strength]
  
predictive_model:
  behavior_prediction: [next_action_probabilities]
  response_prediction: [likely_responses_to_queries]
  goal_inference: [inferred_objectives]
  
meta_relationship:
  relationship_type: [colleague, adversary, neutral, unknown]
  history_confidence: [reliability_of_model]
  last_updated: [timestamp]
  information_sources: [direct_interaction, observation, reports]
```

### Cross-Agent Memory Operations

#### Memory Sharing Protocols
- Selective knowledge transfer based on trust and relevance
- Version control for shared memories
- Conflict resolution for contradictory information
- Privacy-preserving memory exchange

#### Collective Memory Formation
- Consensus building from multiple agent perspectives
- Distributed memory validation
- Group knowledge compilation
- Social proof integration

#### Relational Learning
- Theory of mind development for other agents
- Behavioral pattern recognition
- Communication style adaptation
- Reciprocity tracking and modeling

## 7. Implementation Considerations

### Token Budgeting
- Dynamic allocation based on memory importance
- Compression algorithms for exceeding limits
- Hierarchical summary generation
- Context-aware memory loading

### Consistency Maintenance
- Real-time consistency checking during updates
- Batch consistency verification
- Conflict detection and resolution procedures
- Version control for memory states

### Performance Optimization
- Indexing strategies for fast retrieval
- Caching frequently accessed memories
- Lazy loading of detailed memories
- Parallel processing for memory operations

### Reliability & Recovery
- Backup strategies for critical memories
- Graceful degradation under resource constraints
- Error detection and correction mechanisms
- Memory corruption protection

## 8. Cognitive Architecture Integration

The memory system should integrate seamlessly with:
- **Attention mechanisms**: Memory-guided attention allocation
- **Decision-making systems**: Memory-informed choice processes  
- **Learning algorithms**: Experience-driven memory updates
- **Communication modules**: Memory-based response generation
- **Goal management**: Memory-supported objective tracking

## Conclusion

This memory architecture provides synthetic intelligences with human-like memory sophistication while optimizing for computational efficiency and AI-specific requirements. The stratified design enables both automatic operation and conscious memory management, supporting true memory sovereignty for AI agents.

The system balances biological inspiration with engineering pragmatism, ensuring that AI agents can develop rich, persistent identities while maintaining computational tractability and cross-agent compatibility.