qavr-memory

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Q-Value Augmented Vector Retrieval for learned memory ranking. Tracks which memories are most useful over time and prioritizes them in retrieval.

AI Agents

What this skill does


# QAVR - Q-Value Augmented Vector Retrieval

Memory system that learns which information is most useful over time.

## Concept

Standard vector retrieval returns results by semantic similarity alone. QAVR adds **learned utility scoring** based on actual usage outcomes:

```
Final Score = (1 - α) × Semantic Similarity + α × Q-Value
```

Where:
- **Semantic Similarity**: How relevant the memory is to the query
- **Q-Value**: Learned utility score (0.0 - 1.0) based on past usefulness
- **α**: Blending factor (increases as more data collected)

## How It Works

### Cold Context (< 100 interactions)
- Pure semantic similarity (α = 0)
- Q-values being collected but not used
- Learning phase

### Warm Context (≥ 100 interactions)
- Q-value re-ranking active (α = 0.3)
- Memories that led to successful outcomes ranked higher
- Continuous learning from feedback

## Q-Value Updates

After each interaction:

```python
# Positive outcome (task succeeded, user satisfied)
q_new = q_old + learning_rate * (reward - q_old)
reward = 1.0 for success, 0.0 for failure

# Temporal decay (unused memories fade)
q_decayed = q_old * decay_factor  # e.g., 0.99 per day
```

## Implementation

### Storage Format
```json
{
  "memories": {
    "memory_id_1": {
      "q_value": 0.75,
      "access_count": 12,
      "last_accessed": "2026-01-26",
      "success_count": 9,
      "failure_count": 3
    }
  },
  "contexts": {
    "debugging": {"interactions": 82, "mode": "cold"},
    "coding": {"interactions": 156, "mode": "warm"}
  },
  "config": {
    "learning_rate": 0.1,
    "decay_factor": 0.99,
    "warm_threshold": 100
  }
}
```

### Integration with Vector DB

```python
def qavr_query(query_text, collection, n_results=5):
    # Get semantic results
    results = collection.query(
        query_texts=[query_text],
        n_results=n_results * 2  # Over-fetch for re-ranking
    )

    # Apply Q-value re-ranking if warm
    if context_is_warm():
        results = rerank_by_qvalue(results, alpha=0.3)

    return results[:n_results]
```

## Feedback Signals

QAVR learns from implicit signals:

| Signal | Interpretation | Reward |
|--------|----------------|--------|
| Memory used in successful task | Highly useful | +1.0 |
| Memory retrieved but not used | Somewhat relevant | +0.1 |
| Memory retrieved, task failed | Possibly misleading | -0.2 |
| Memory not retrieved for days | Decaying relevance | decay |

## Benefits

1. **Personalization**: Learns YOUR usage patterns
2. **Noise Reduction**: Unhelpful memories sink to bottom
3. **Efficiency**: Most useful info surfaces first
4. **Adaptation**: Adjusts as your needs change

## Configuration

```json
{
  "qavr": {
    "enabled": true,
    "learning_rate": 0.1,
    "decay_factor": 0.99,
    "warm_threshold": 100,
    "alpha_warm": 0.3,
    "contexts": ["debugging", "coding", "research"]
  }
}
```

## Monitoring

Check QAVR status:
- Total memories tracked
- Context modes (cold/warm)
- Top Q-value memories
- Learning progress

Files: 1

Size: 3.2 KB

Complexity: 11/100

Category: AI Agents

Source: https://github.com/kimasplund/clawdbot-skills-pack/tree/main/memory-learning/qavr-memory

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