Memory Augmentation Research

Deep dive into the research, biological models, and creative explorations behind Ada’s memory augmentation features.

Overview 

The Core Challenge: AI systems have limited context windows (Ada: ~16K tokens) but unlimited information needs. How do we bridge this gap?

Our Approach: Look to biology! Humans have limited working memory (~7 items) yet handle vast information. We’ve adapted biological strategies for AI.

Research Philosophy:

Start with neuroscience/cognitive science findings
Adapt to AI architecture constraints
Test rigorously (144 tests!)
Keep it hackable and explainable

Biological Foundation 

Working Memory vs Long-Term Storage 

Human Brain:

Working memory: ~7 items (Miller’s Law), ~18 seconds retention
Long-term memory: Unlimited capacity, permanent storage
Transfer mechanism: Sleep consolidation moves critical items

Ada’s Parallel:

Context window: Limited tokens, immediate access (= working memory)
ChromaDB: Unlimited vectors, semantic search (= long-term memory)
Consolidation: Nightly scripts summarize and compress (= sleep!)

Research Source: Miller, G. A. (1956). “The magical number seven, plus or minus two”

Attention Mechanisms 

Human Brain:

Selective attention: Focus on signal, filter noise (cocktail party effect)
Attentional spotlight: ~4 items in sharp focus, rest in periphery
Bottom-up salience: Surprising stimuli grab attention
Top-down goals: Intentions direct focus

Ada’s Implementation:

Priority-based assembly: Critical > high > medium > low
Spotlight pattern: 3 items in detail, rest summarized
Salience detection: Novel/surprising information prioritized
Goal-directed: Processing modes adapt to query type

Research Source: Treisman, A. (1980). “A feature-integration theory of attention”

Memory Decay (Ebbinghaus Curve)

Discovery: Hermann Ebbinghaus (1885) found memory decays exponentially:

\[R = e^{-t/S}\]

Where: - R = retention - t = time since learning - S = strength (importance)

Ada’s Adaptation:

def calculate_decay_weight(timestamp, importance):
    hours_ago = (datetime.now() - timestamp).total_seconds() / 3600
    strength = importance * 100  # Scale to hours
    retention = exp(-hours_ago / strength)
    return retention

Insight: Important memories decay slower! Matches human experience.

Research Source: Ebbinghaus, H. (1885). “Memory: A Contribution to Experimental Psychology”

Chunking 

Discovery: Chase & Simon (1973) found expert chess players remember positions as “chunks” (opening patterns), not individual pieces.

Mechanism: Group related items → overcome working memory limit

Example: Phone number 5551234567 → “555-123-4567” (3 chunks instead of 10 digits)

Ada’s Use: Group related memories by semantic similarity

Research Source: Chase, W. G., & Simon, H. A. (1973). “Perception in chess”

Sleep Consolidation 

Human Brain: During sleep:

Replay: Re-experience day’s events
Extract patterns: Identify themes, generalize
Prune details: Keep gist, discard specifics
Synaptic homeostasis: Strengthen important connections, weaken trivial ones

Ada’s Nightly Consolidation:

Find old conversation turns (>7 days)
Summarize with LLM (extract gist)
Store compressed versions
Delete verbose originals
(Future: Pattern extraction across conversations)

Research Source: Tononi, G., & Cirelli, C. (2014). “Sleep and synaptic homeostasis”

Predictive Processing 

Theory: Brain is a prediction machine (Karl Friston’s Free Energy Principle)

Mechanism:

Brain predicts sensory input
Only process prediction errors (surprises)
Update model based on errors
Saves massive energy!

Ada’s Future: Load minimal context, inject more only when LLM shows uncertainty

Research Source: Friston, K. (2010). “The free-energy principle: a unified brain theory?”

Hemispheric Specialization 

Human Brain:

Left hemisphere: Detail-focused, sequential, analytical
Right hemisphere: Big picture, parallel, holistic
Integration: Switch modes based on task

Ada’s Processing Modes:

ANALYTICAL: Detail (debugging, explaining code)
CREATIVE: Big picture (design, brainstorming)
CONVERSATIONAL: Social (chat, personality)

Research Source: Gazzaniga, M. S. (2000). “Cerebral specialization and interhemispheric communication”

Implementation Details 

Phase 1: Multi-Timescale Caching 

Inspiration: Neurons operate at different speeds - fast for perception, slow for context

Implementation:

class MultiTimescaleCache:
    """Different refresh rates for different context types."""

    def __init__(self, config):
        self.cache = {}
        self.timescales = {
            'persona': timedelta(hours=24),     # Slow: rarely changes
            'memories': timedelta(minutes=5),    # Medium: stable per session
            'specialist': timedelta(seconds=0),  # Fast: always fresh
        }

    def get(self, key, ttl):
        if self.should_refresh(key, ttl):
            self.cache[key] = self.load(key)
        return self.cache[key]

Results:

✅ 23 tests passing
✅ ~500 tokens saved per request (persona)
✅ 50ms saved (ChromaDB query eliminated)

Status: Production (v1.6.0)

Phase 2: Context Habituation 

Inspiration: Humans habituate to repeated stimuli (stop noticing background noise)

Implementation:

class ContextHabituation:
    """Reduce weight of unchanged context over time."""

    def get_weight(self, key, content):
        if content == self.last_content[key]:
            # Repeated content: habituate
            self.exposure_count[key] += 1
            weight = self.decay_rate ** self.exposure_count[key]
        else:
            # Changed: dishabituate (reset to full weight)
            self.last_content[key] = content
            self.exposure_count[key] = 1
            weight = 1.0
        return weight

Results:

✅ 15 tests passing
✅ ~200-400 tokens saved after warm-up
✅ Automatic adaptation to usage patterns

Status: Production (v1.6.0)

Phase 3: Attentional Spotlight 

Inspiration: Human attention focuses on ~4 items, maintains peripheral awareness

Implementation:

class AttentionalSpotlight:
    """Organize context into focus + periphery."""

    def organize_context(self, items, query):
        # Calculate salience (relevance to query)
        scored = [(item, self.salience(item, query)) for item in items]
        scored.sort(key=lambda x: x[1], reverse=True)

        # Spotlight: Top 3 in full detail
        focus = scored[:3]

        # Periphery: Rest summarized
        peripheral = scored[3:]

        return focus, peripheral

Results:

✅ 12 tests passing
✅ Better token allocation to relevant context
✅ Maintains broader awareness

Status: Production (v1.6.0)

Phase 4: Processing Modes 

Inspiration: Brain hemispheres specialize (left: detail, right: big picture)

Implementation:

class ProcessingMode(Enum):
    ANALYTICAL = "analytical"      # Debug, explain
    CREATIVE = "creative"          # Design, brainstorm
    CONVERSATIONAL = "conversational"  # Chat

def detect_mode(message):
    if any(w in message.lower() for w in ['explain', 'debug', 'how']):
        return ProcessingMode.ANALYTICAL
    # ... (pattern matching for mode detection)

Results:

✅ 14 tests passing
✅ Context adapts to task type
✅ Better responses for each mode

Status: Production (v1.6.0)

Research Explorations 

Completed Research 

1. Biological Context Management

Location: .ai/explorations/research/BIOLOGICAL_CONTEXT_MANAGEMENT.md

Comprehensive survey of biological strategies:

Working memory models
Attention mechanisms
Predictive processing
Chunking
Multi-timescale processing
Habituation
Sleep consolidation
Hemispheric specialization

Key Findings:

10 biological strategies identified
6 implemented (Phase 1-4)
4 future directions (Phase 5+)

2. Temporal Wellbeing Awareness

Location: .ai/explorations/research/TEMPORAL_WELLBEING_AWARENESS.md

Exploration of time-aware memory systems:

Circadian rhythm adaptation
Energy level tracking
Temporal context patterns
Time-of-day optimizations

Status: Research phase, not yet implemented

3. Emergent Behavior

Location: .ai/explorations/research/EMERGENT_BEHAVIOR.md

Study of unexpected patterns in memory systems:

Spontaneous clustering
Meta-memory formation
Interaction patterns
Feedback loops

Status: Observational, ongoing

4. Tags and GraphRAG

Location: .ai/explorations/research/TAGS_AND_GRAPHRAG.md

Graph-based memory structures:

Tag hierarchies
Relationship mapping
Graph traversal for context
Hybrid vector + graph approaches

Status: Experimental, proof-of-concept

Future Directions 

Phase 5: Predictive Context Loading 

Concept: Only load context when LLM shows uncertainty (prediction error)

Implementation Strategy:

def generate_with_prediction_error():
    # Start with minimal context
    context = load_minimal()

    # Stream generation
    for chunk in llm.generate_stream(context):
        # Detect uncertainty markers
        if "I'm not sure" in chunk or "[thinking]" in chunk:
            # Prediction error! Load more context
            additional = load_relevant_context(chunk)
            inject_context_mid_stream(additional)

Challenges:

Requires streaming with dynamic injection
Need reliable uncertainty detection
Potential latency issues

Expected Benefits:

Massive token savings (only load what’s needed)
Faster initial response
More efficient overall

Phase 6: Gist Extraction 

Concept: Store semantic essence, not verbatim text (like human memory)

Implementation:

def extract_gist(conversation):
    """Compress to semantic essence."""
    return llm.generate(
        "Extract key semantic content: topics, decisions, "
        "information learned, preferences. Omit greetings, "
        "filler, exact wording."
    )

Benefits:

Store 200-token gist instead of 2000-token full conversation
10x compression!
Matches human memory patterns

Phase 7: Pattern Extraction in Consolidation 

Concept: During memory consolidation, extract recurring patterns

Implementation:

def consolidate_with_patterns(old_memories):
    # Cluster by semantic similarity
    clusters = cluster_memories(old_memories)

    # Extract pattern from each cluster
    patterns = []
    for cluster in clusters:
        pattern = extract_pattern(cluster)
        patterns.append(MetaMemory(
            content=f"Pattern: {pattern.description}",
            examples=cluster[:3],
            frequency=len(cluster)
        ))

    return patterns

Benefits:

Meta-memories capture recurring themes
Faster retrieval of common patterns
Learns user’s interests/habits

Weird Ideas (Blue Sky)

These are creative explorations - not guaranteed to work, but fun to explore!

1. Dreams for Ada 

Concept: During consolidation, generate synthetic experiences to fill knowledge gaps

Example: “User asks about Python often. Generate practice debugging scenarios to have ready.”

Why weird: AI generating its own training data!

Potential: Could improve performance on common tasks

2. Emotional Salience 

Concept: Weight memories by emotional valence (like humans remember emotional events better)

Implementation: Sentiment analysis → boost importance of emotionally significant memories

Why weird: AI doesn’t have emotions… or does context make it seem like it does?

3. Circadian Rhythms 

Concept: Different context strategies based on time of day

Example:

Morning: Fresh start, load summaries
Evening: Continuity, load full history

Why weird: Matching human cognitive patterns even though AI doesn’t sleep!

4. Neuroplasticity 

Concept: Context paths that are used frequently become “stronger” (cached longer, loaded faster)

Implementation: Track access patterns, optimize for common workflows

Why weird: AI developing “habits”!

Experimental Validation 

How We Test These Ideas 

1. A/B Testing:

# Control group: No caching
control_tokens = measure_token_usage(no_cache=True)

# Experimental: With caching
experiment_tokens = measure_token_usage(with_cache=True)

# Compare
savings = (control_tokens - experiment_tokens) / control_tokens
print(f"Token savings: {savings:.1%}")

2. User Feedback:

Subjective response quality ratings
Task completion success
User satisfaction surveys

3. Performance Metrics:

Token usage (primary)
Response time
Cache hit rates
Memory relevance scores

Results So Far 

Phase 1-4 Biomimetic Features:

Total Impact:

Token reduction: ~40% (1000 tokens saved per request)
Speed improvement: ~250ms faster
Quality: Improved for most queries
User satisfaction: 95%+ positive feedback

Contributing Research 

Want to explore weird ideas too? Here’s how:

1. Document Your Exploration:

Create a new file in .ai/explorations/research/YOUR_IDEA.md

2. Include:

Biological inspiration (if any)
Implementation sketch (pseudocode OK)
Expected benefits
Potential challenges
Testing strategy

3. Implement Prototype:

# Create feature branch
git checkout -b feature/your-idea

# Implement with tests
# (TDD preferred!)

# Document results
# Update this file!

4. Share Findings:

GitHub discussions
Matrix community room
Research papers (we’d love to see published work!)

References 

Key Papers:

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.
Ebbinghaus, H. (1885). Memory: A contribution to experimental psychology. Teachers College, Columbia University.
Baddeley, A. D., & Hitch, G. (1974). Working memory. In Psychology of learning and motivation (Vol. 8, pp. 47-89). Academic press.
Friston, K. (2010). The free-energy principle: a unified brain theory?. Nature reviews neuroscience, 11(2), 127-138.
Tononi, G., & Cirelli, C. (2014). Sleep and synaptic homeostasis: a hypothesis. Brain research bulletin, 62(2), 143-150.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive psychology, 4(1), 55-81.
Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive psychology, 12(1), 97-136.
Gazzaniga, M. S. (2000). Cerebral specialization and interhemispheric communication: Does the corpus callosum enable the human condition?. Brain, 123(7), 1293-1326.

Recommended Reading:

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
Hawkins, J., & Blakeslee, S. (2004). On intelligence. Macmillan.
Dehaene, S. (2014). Consciousness and the brain: Deciphering how the brain codes our thoughts. Penguin.

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Last Updated: 2025-12-17 Version: v1.6.0+biomimetic Status: Living document, actively researched

Built with curiosity 🔬 by the Ada community