# Personal AI Infrastructure -- Knowledge Distillation Module

**Version**: v1.0.0 | **Status**: Active | **Last Updated**: March 2026

## Overview

Knowledge Distillation provides teacher-student model compression for PAI agents. It implements Hinton et al. (2015) using pure Python + NumPy, enabling agents to understand and evaluate knowledge transfer without requiring PyTorch.

## PAI Algorithm Phase Mapping

| Phase | Activity | Key Functions/Tools |
|-------|----------|-------------------|
| **OBSERVE** | Collect teacher and student logits | `soft_labels` |
| **THINK** | Analyze dark knowledge in teacher soft targets | `soft_labels` at varying temperatures |
| **PLAN** | Configure temperature and alpha for distillation | `DistillationLoss(T, alpha)` |
| **BUILD** | Set up distillation pipeline | `DistillationLoss`, `distillation_loss` |
| **EXECUTE** | Compute distillation loss on batches | `distillation_loss(student, teacher, labels)` |
| **VERIFY** | Validate loss components (KL + CE) and teacher accuracy | `result["distillation_loss"]`, `result["teacher_accuracy"]` |
| **LEARN** | Record compression metrics and loss trends | `distillation_compute_loss` MCP tool |

## MCP Tools Available

| Tool | Description | Trust Level |
|------|-------------|-------------|
| `distillation_compute_loss` | Compute KD loss on synthetic data | SAFE |

## Agent Capabilities

| Agent Type | Primary Use | Key Functions |
|-----------|-------------|--------------|
| **Engineer** | Implement distillation training loops | `distillation_loss`, `soft_labels` |
| **Architect** | Evaluate temperature/alpha trade-offs | `distillation_compute_loss` |
| **QATester** | Verify loss decomposition and soft label properties | `soft_labels`, loss component checks |

## Navigation

- [README](README.md) | [AGENTS](AGENTS.md) | [SPEC](SPEC.md)
