# Dream-Inspired Memory Consolidation System

## Overview

This document describes an innovative approach to memory consolidation inspired by human cognitive processes during sleep. Originally conceptualized on June 7, 2025, and contributed to [Issue #11](https://github.com/doobidoo/mcp-memory-service/issues/11) on July 28, 2025, this system enhances the proposed multi-layered consolidation architecture with biologically-inspired mechanisms.

> **📚 Implementation Guide**: For a complete guide on implementing this system autonomously without external AI, see the [Autonomous Memory Consolidation Guide](./autonomous-memory-consolidation.md).

## Background

Traditional memory systems face challenges with:
- Unbounded growth leading to performance degradation
- Difficulty surfacing truly important information
- Lack of creative discovery mechanisms
- No natural "forgetting" process

The dream-inspired approach addresses these by mimicking how human brains consolidate memories during sleep cycles.

## Core Concepts

### 1. Exponential Decay Scoring

Memories naturally lose relevance over time unless reinforced by connections or access patterns.

```python
def calculate_memory_relevance(memory, current_time):
    """
    Calculate memory relevance using exponential decay.
    
    Factors:
    - Age of memory
    - Base importance score
    - Retention period (varies by memory type)
    """
    age = current_time - memory.created_at
    base_score = memory.importance_score
    
    # Different memory types have different decay rates
    retention_periods = {
        'critical': 365,      # Critical memories decay slowly
        'reference': 180,     # Reference material moderately
        'temporary': 7,       # Temporary notes decay quickly
        'default': 30        # Standard memories
    }
    
    retention_period = retention_periods.get(memory.memory_type, 30)
    decay_factor = math.exp(-age.days / retention_period)
    
    # Connections boost relevance
    connection_boost = 1 + (0.1 * len(memory.connections))
    
    return base_score * decay_factor * connection_boost
```

### 2. Creative Association System

During consolidation, the system randomly pairs memories to discover non-obvious connections, similar to how dreams create unexpected associations.

```python
async def creative_association_phase(memories):
    """
    Discover creative connections between seemingly unrelated memories.
    
    The "sweet spot" for interesting discoveries is moderate similarity
    (0.3-0.7 range) - not too similar, not completely unrelated.
    """
    # Sample random pairs (limit to prevent combinatorial explosion)
    max_pairs = min(100, len(memories) * (len(memories) - 1) // 2)
    pairs = random.sample(
        list(combinations(memories, 2)), 
        k=min(max_pairs, len(combinations(memories, 2)))
    )
    
    associations = []
    for mem1, mem2 in pairs:
        similarity = calculate_semantic_similarity(mem1, mem2)
        
        # Sweet spot for creative connections
        if 0.3 < similarity < 0.7:
            # Analyze why they might be connected
            connection_reason = analyze_connection(mem1, mem2)
            
            # Create association memory
            association = await create_association_memory(
                source_memories=[mem1.hash, mem2.hash],
                similarity_score=similarity,
                connection_type=connection_reason,
                metadata={
                    "discovery_method": "creative_association",
                    "discovery_date": datetime.now(),
                    "tags": ["association", "discovery"]
                }
            )
            associations.append(association)
    
    return associations
```

### 3. Controlled Forgetting (Memory Pruning)

Not all memories need to be retained forever. The system implements intelligent forgetting to maintain efficiency.

```python
async def memory_pruning_phase(time_horizon):
    """
    Implement controlled forgetting for memory health.
    
    Rather than deleting, we compress and archive low-value memories.
    """
    # Get candidates for forgetting
    candidates = await get_pruning_candidates(time_horizon)
    
    pruned_memories = []
    for memory in candidates:
        if should_forget(memory):
            # Don't delete - compress and archive
            compressed = await compress_memory(memory)
            
            # Create summary if part of a pattern
            if memory.cluster_id:
                await update_cluster_summary(memory.cluster_id, compressed)
            
            # Archive the original
            await archive_memory(memory, compressed)
            pruned_memories.append(memory.hash)
    
    return pruned_memories

def should_forget(memory):
    """
    Determine if a memory should be forgotten.
    
    Factors:
    - Relevance score below threshold
    - No recent access
    - No important connections
    - Not tagged as important
    """
    if memory.tags.intersection({'important', 'critical', 'reference'}):
        return False
    
    if memory.relevance_score < 0.1:
        if not memory.connections:
            if (datetime.now() - memory.last_accessed).days > 90:
                return True
    
    return False
```

### 4. Semantic Compression

Create condensed representations of memory clusters for efficient long-term storage.

```python
async def semantic_compression(memory_cluster):
    """
    Compress a cluster of related memories into a semantic summary.
    
    This creates a higher-level abstraction while preserving key information.
    """
    # Extract key concepts using NLP
    concepts = extract_key_concepts(memory_cluster)
    
    # Identify recurring themes
    themes = identify_themes(concepts)
    
    # Create compressed representation
    compressed = {
        "summary": generate_thematic_summary(themes),
        "key_concepts": concepts[:10],  # Top 10 concepts
        "temporal_range": {
            "start": min(m.created_at for m in memory_cluster),
            "end": max(m.created_at for m in memory_cluster)
        },
        "source_count": len(memory_cluster),
        "aggregate_tags": aggregate_tags(memory_cluster),
        "embedding": generate_concept_embedding(concepts),
        "compression_ratio": calculate_compression_ratio(memory_cluster)
    }
    
    # Store as consolidated memory
    consolidated = await store_consolidated_memory(
        content=compressed["summary"],
        metadata={
            **compressed,
            "type": "semantic_compression",
            "compression_date": datetime.now()
        }
    )
    
    # Link source memories
    for memory in memory_cluster:
        await add_memory_link(memory.hash, consolidated.hash, "compressed_into")
    
    return consolidated
```

## Autonomous Implementation

**🚀 This entire system can run autonomously without external AI!** The key insight is that the MCP Memory Service already generates embeddings when memories are stored. These embeddings enable:

- Mathematical similarity calculations (cosine similarity)
- Clustering algorithms (DBSCAN, hierarchical clustering)
- Statistical summarization (TF-IDF, centroid method)
- Rule-based decision making

For detailed implementation without AI dependencies, see the [Autonomous Memory Consolidation Guide](./autonomous-memory-consolidation.md).

## Integration with Time-Based Layers

The dream-inspired mechanisms enhance each consolidation layer:

### Daily Processing (Light Touch)
- Calculate initial relevance scores
- Identify highly-connected memory clusters
- Flag memories showing rapid decay

### Weekly Processing (Active Consolidation)
- Run creative association discovery
- Begin semantic compression of stable clusters
- Apply light pruning to obvious temporary content

### Monthly Processing (Deep Integration)
- Comprehensive relevance recalculation
- Controlled forgetting with archival
- Create month-level semantic summaries

### Quarterly Processing (Pattern Extraction)
- Deep creative association analysis
- Major semantic compression operations
- Identify long-term knowledge structures

### Yearly Processing (Knowledge Crystallization)
- Final compression of historical data
- Archive rarely-accessed memories
- Create year-level knowledge maps

## Implementation Architecture

```python
class DreamInspiredConsolidator:
    """
    Main consolidation engine with biologically-inspired processing.
    """
    
    def __init__(self, storage, config):
        self.storage = storage
        self.config = config
        self.decay_calculator = ExponentialDecayCalculator(config)
        self.association_engine = CreativeAssociationEngine(storage)
        self.compression_engine = SemanticCompressionEngine()
        self.pruning_engine = ControlledForgettingEngine(storage)
    
    async def consolidate(self, time_horizon: str):
        """
        Run full consolidation pipeline for given time horizon.
        """
        # 1. Retrieve memories for processing
        memories = await self.storage.get_memories_for_horizon(time_horizon)
        
        # 2. Calculate/update relevance scores
        await self.update_relevance_scores(memories)
        
        # 3. Cluster by semantic similarity
        clusters = await self.cluster_memories(memories)
        
        # 4. Run creative associations (if appropriate for horizon)
        if time_horizon in ['weekly', 'monthly']:
            associations = await self.association_engine.discover(memories)
            await self.storage.store_associations(associations)
        
        # 5. Compress clusters
        for cluster in clusters:
            if len(cluster) >= self.config.min_cluster_size:
                await self.compression_engine.compress(cluster)
        
        # 6. Controlled forgetting (if appropriate)
        if time_horizon in ['monthly', 'quarterly', 'yearly']:
            await self.pruning_engine.prune(memories)
        
        # 7. Generate consolidation report
        return await self.generate_report(time_horizon, memories, clusters)
```

## Configuration Options

```yaml
dream_consolidation:
  # Decay settings
  decay:
    enabled: true
    retention_periods:
      critical: 365
      reference: 180
      standard: 30
      temporary: 7
    
  # Creative association settings
  associations:
    enabled: true
    min_similarity: 0.3
    max_similarity: 0.7
    max_pairs_per_run: 100
    
  # Forgetting settings
  forgetting:
    enabled: true
    relevance_threshold: 0.1
    access_threshold_days: 90
    archive_location: "./memory_archive"
    
  # Compression settings
  compression:
    enabled: true
    min_cluster_size: 5
    max_summary_length: 500
    preserve_originals: true
```

## Benefits

1. **Natural Scalability**: System automatically manages growth through decay and forgetting
2. **Serendipitous Discovery**: Creative associations reveal unexpected insights
3. **Cognitive Realism**: Mirrors human memory, making it more intuitive
4. **Performance Optimization**: Compression and archival maintain speed
5. **Adaptive Importance**: Memory relevance evolves based on usage and connections

## Safety Considerations

- Never auto-delete memories marked as 'critical' or 'important'
- Always compress before archiving
- Maintain audit trail of all consolidation operations
- Allow users to recover archived memories
- Provide options to disable forgetting for specific memory types

## Future Enhancements

1. **Sleep Cycle Simulation**: Run different consolidation phases at different "depths"
2. **Dream Journal**: Track and analyze creative associations over time
3. **Memory Replay**: Periodically resurface old memories for potential new connections
4. **Adaptive Decay Rates**: Learn optimal retention periods from user behavior
5. **Emotional Tagging**: Consider emotional context in consolidation decisions

## Related Resources

- 📋 [Issue #11: Multi-Layered Memory Consolidation System](https://github.com/doobidoo/mcp-memory-service/issues/11)
- 🤖 [Autonomous Memory Consolidation Guide](./autonomous-memory-consolidation.md) - Complete implementation without external AI
- 📚 [MCP Memory Service Documentation](https://github.com/doobidoo/mcp-memory-service)

## Conclusion

The dream-inspired memory consolidation system brings biological realism to digital memory management. By implementing natural processes like decay, creative association, and controlled forgetting, we create a system that not only scales efficiently but also surfaces truly meaningful information while discovering unexpected connections.

This approach transforms memory management from a storage problem into a living, breathing system that evolves with its user's needs.

---

*Original concept: June 7, 2025*  
*Contributed to Issue #11: July 28, 2025*  
*Documentation created: July 28, 2025*
