# L7a: Autoresearch Loop

Build a self-improving agent with integrity guards.

**Module**: M7 Advanced Topics  
**Est. Time**: 75 min  
**Files**: `starter.py`, `solution.py`

## Objective

Create an agent that runs experiments, measures its own performance, logs results, and decides whether to keep or discard each change.

## Concepts

- Autoresearch: run → measure → log → decide
- Code hashing to detect grinding
- Median vs best comparison
- Reward hack defense

## Starter

```bash
cd course/labs/L7-autoresearch/
python starter.py
```

## Solution

```bash
python solution.py
```

## Experiment Log Format

```jsonl
{"run": 1, "status": "baseline", "metric": {"name": "latency", "value": 52, "unit": "ms"}}
{"run": 2, "status": "keep", "metric": {"name": "latency", "value": 46}, "deltaPct": -11.5}
{"run": 3, "status": "discard", "metric": {"name": "latency", "value": 53}, "deltaPct": +1.9}
```

## Integrity Guards

| Threat | Detection | Prevention |
|--------|-----------|------------|
| Grinding (same code re-run) | Code hash comparison | Skip run |
| Noise-chasing | Median vs best comparison | Use median, not best |
| Reward hacking | Timing function isolation | Verify computation not shortcut |
| Test set leakage | Test data hash verification | Assert data unchanged |