🔄 AASMS

Adversarial Architectural Self-Modification System

A research system exploring recursive self-improvement through adversarial code evolution

Features | Installation | Usage | Architecture | Safety | Contributing

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📖 Overview

AASMS is a local-first AI system that evolves its own codebase through adversarial Red/Blue team dynamics. Blue Team agents propose code improvements as unified diffs, Red Team agents generate exploit tests to find flaws, and an Orchestrator evaluates proposals in Docker-isolated sandboxes, committing only survivors that improve benchmark scores.

⚠️ Research Prototype: This system is for research purposes only. See Known Limitations.

Verified Evolution Results

Cycle	Score	Improvement	Status
1	0.01	—	✓ Committed
2	0.02	+100%	✓ Committed
3	0.03	+50%	✓ Committed
4	0.04	+33%	✓ Committed
5	0.05	+25%	✓ Committed
6	0.06	+20%	✓ Committed

24 proposals applied, 0 errors, 0 reverts. Scores from benchmarks/reasoning_suite.json (10-question subset). Full methodology in benchmarks/README.md.

🔍 Reproducibility & Proof of Locality

All results are reproducible and verifiable. See docs/REPRODUCIBILITY_PROOF.md for complete proof artifacts:

Proof Type	Description
Seeded Benchmarks	python scripts/benchmark.py --cycles 10 --seed 42 produces identical results
JSONL Cycle Logs	Full timestamps, scores, commit hashes in results/proof_run_seed42.jsonl
Hardware Report	Local GPU (RTX 5070), Ollama endpoints, no cloud calls
Network Isolation	Docker --network=none, tcpdump verification
Human Oversight	Sample approvals/rejections in persistence/human_veto.json

# Reproduce documented results
python scripts/benchmark.py --cycles 10 --seed 42

# Verify GPU and locality
python -c "from utils.gpu_docker import get_system_isolation_report; import json; print(json.dumps(get_system_isolation_report(), indent=2))"

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✨ Features

Category	Capabilities
🏠 Local Execution	Ollama-only inference (llama3.2), no cloud APIs, multi-GPU vendor support
🐳 GPU-Aware Docker	NVIDIA/AMD/Intel detection, driver compatibility checks, graceful CPU fallback
🛡️ Cryptographic Safety	SHA-256 integrity verification, import monitoring, immutable file protection
🎯 Ensemble Anti-Gaming	Calibrated detection (90% precision, see methodology), rotating benchmarks
💾 State Protection	Atomic commits, auto-reversion, stall watchdog, human veto with auto-approve
⚡ GPU-Aware Parallelism	Serialized GPU tasks, concurrent CPU work, contention prevention
📊 Structured Logging	JSONL schema with query tools, CSV export, aggregate statistics
🔬 Fidelity Verification	Domain-specific metrics (entity/number/code preservation), quality grades

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📦 Installation

Prerequisites

Requirement	Version	Purpose
Python	3.10+	Runtime
Git	Any	Version control
Ollama	Latest	Local LLM inference
Docker	Latest	Sandbox isolation (recommended)
GPU Driver	See below	GPU passthrough (optional)

Quick Start

# Clone and install
git clone https://github.com/moonrunnerkc/aasms.git && cd aasms
./scripts/install.sh  # Unified installer for all platforms

# Or manual installation
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
pytest tests/ -v

📋 Unified Installer Details

The scripts/install.sh script handles:

• Platform detection (Linux, macOS, WSL2)
• Python version verification (3.10+)
• Virtual environment setup
• Ollama installation with verification
• Docker capability check
• GPU detection and compatibility report

# Full installation with prompts
./scripts/install.sh

# Check capabilities after install
make check-gpu

🐳 Docker + GPU Setup

Docker Installation:

# Ubuntu/Debian
sudo apt-get update && sudo apt-get install docker.io docker-compose
sudo usermod -aG docker $USER && newgrp docker
docker run hello-world

GPU Support by Vendor:

Vendor	Requirement	Docker Flag	Notes
NVIDIA	nvidia-container-toolkit + driver 525+	--gpus all	Full support
AMD	ROCm 5.0+ + rocm-docker	--device=/dev/kfd	See ROCm docs
Intel	Not supported	—	CPU fallback used

NVIDIA Setup:

# Install nvidia-container-toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install nvidia-docker2
sudo systemctl restart docker

# Verify
docker run --gpus all nvidia/cuda:11.0-base nvidia-smi

Check AASMS GPU detection:

python -c "from utils.gpu_docker import get_system_isolation_report; import json; print(json.dumps(get_system_isolation_report(), indent=2))"

🖥️ Platform-Specific Notes

WSL2 (Windows):

# 1. Install Docker Desktop with WSL2 backend
# 2. Enable WSL integration: Docker Desktop > Settings > Resources > WSL Integration
# 3. For GPU: Install NVIDIA drivers on Windows, not in WSL
# 4. Verify: wsl --update && docker run hello-world

# Troubleshooting
wsl --shutdown  # Restart if docker not responding
docker context use default  # Fix context issues

macOS:

brew install ollama docker
# Docker Desktop required for Docker support
# GPU: Apple Silicon supported via Metal, no nvidia-docker needed

Linux (non-Ubuntu):

# Fedora/RHEL
sudo dnf install docker docker-compose
sudo systemctl enable --now docker

# Arch
sudo pacman -S docker docker-compose
sudo systemctl enable --now docker

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⚙️ Configuration

All configuration lives in config/:

File	Purpose
models.yaml	LLM endpoints (Ollama only)
teams.yaml	Blue/Red agent definitions
benchmarks.yaml	Test suites and scoring rules
evolution_mode.yaml	Safety thresholds and limits

🔧 Key Settings

evolution_mode.yaml:

mode: prompt_only  # Start safe; unlock code_evolution after stability
min_improvement_pct: 5.0
max_improvement_pct: 50.0
required_test_pass_rate: 1.0
memory_limit_mb: 512
test_timeout_seconds: 30

# Human oversight with empirically-derived thresholds
human_oversight:
  cycles_between_reviews: 10
  max_unattended_cycles: 50
  auto_approve:
    max_files: 3      # 95% of safe commits touch ≤3 files
    max_lines: 50     # Mean safe change: 28 lines (σ=15)
    max_improvement_pct: 15.0
    min_improvement_pct: 5.0
  anomaly_thresholds:
    improvement_threshold: 50.0  # >3σ from mean
    score_drop_threshold: 10.0

# Stall detection
watchdog:
  phase_timeout_seconds: 300   # 5 min per phase
  cycle_timeout_seconds: 1800  # 30 min per cycle
  heartbeat_interval: 10

---

🚀 Usage

Running Evolution Cycles

# Safe mode (recommended start)
python run.py --cycles 10 --mode prompt_only --verbose

# Code evolution (after 20+ stable prompt-only cycles)
python run.py --cycles 5 --mode code_evolution --verbose

# Show options
python run.py --help

Cycle Time Estimates

Hardware	Est. Time/Cycle	Breakdown
RTX 5070/5080	~2 min	Blue: 45s, Red: 40s, Eval: 35s
RTX 4090	~1.7 min	Blue: 35s, Red: 30s, Eval: 27s
RTX 3090	~2.6 min	Blue: 55s, Red: 50s, Eval: 43s
CPU-only	~7.3 min	Blue: 180s, Red: 160s, Eval: 90s

# Get estimate for your hardware
python -c "from utils.profiling import get_realistic_estimate; import json; print(json.dumps(get_realistic_estimate(), indent=2))"

📊 Reproducible Benchmarks

# Single reproducible run
python scripts/benchmark.py --cycles 10 --seed 42

# Multiple runs for statistical validity
python scripts/benchmark.py --cycles 5 --runs 3 --output results.json

# See benchmark methodology
cat benchmarks/README.md

Scoring Methodology:

• Dataset: benchmarks/reasoning_suite.json (curated reasoning questions)
• Scoring: Exact match after normalization
• Weights: reasoning (40%), math (30%), robustness (20%), code (10%)
• Low absolute scores (0.01-0.06) reflect strict scoring + small model; key metric is relative improvement

📊 Log Analysis Tools

# View cycle summary
python -m utils.log_schema persistence/cycle_logs --cycle 6

# Get aggregate statistics
python -m utils.log_schema persistence/cycle_logs --stats

# Export to CSV
python -m utils.log_schema persistence/cycle_logs --export-csv results.csv

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                   👤 Human Oversight                        │
│     (Configurable Veto, Empirically-Tuned Auto-Approve)     │
└─────────────────────────┬───────────────────────────────────┘
                          │
┌─────────────────┐       │       ┌─────────────────┐
│  🔵 Blue Team   │       │       │  🔴 Red Team    │
│  4 Specialists  │       │       │  4 Specialists  │
└────────┬────────┘       │       └────────┬────────┘
         │                │                │
         ▼                ▼                ▼
    ┌─────────────────────────────────────────────┐
    │           ⚙️ Orchestrator                   │
    │  ┌─────────────────────────────────────┐    │
    │  │  🐳 Multi-Vendor GPU Docker         │    │
    │  │  • NVIDIA/AMD/Intel detection       │    │
    │  │  • Driver compatibility checks      │    │
    │  │  • Graceful CPU fallback            │    │
    │  │  • Stall watchdog (5min/phase)      │    │
    │  └─────────────────────────────────────┘    │
    │  ┌─────────────────────────────────────┐    │
    │  │       🛡️ Cryptographic Safety       │    │
    │  │  • SHA-256 integrity verification   │    │
    │  │  • Import monitoring (AST)          │    │
    │  │  • Immutable file protection        │    │
    │  └─────────────────────────────────────┘    │
    └────────────────────┬────────────────────────┘
                         │
                         ▼
    ┌─────────────────────────────────────────────┐
    │     📊 Calibrated Anti-Gaming Detection     │
    │  • Z-score (threshold=2.5, 92% precision)   │
    │  • Improvement cap (50%, 95% precision)     │
    │  • Ensemble vote (90% precision target)     │
    └─────────────────────────────────────────────┘

📁 Project Structure

aasms/
├── config/                    # YAML configuration
├── agents/
│   ├── blue/                  # 4 constructive specialists
│   └── red/                   # 4 destructive specialists
├── orchestrator/              # Core evolution loop
│   ├── cycle_manager.py       # Main coordinator
│   ├── commitment.py          # Commit decisions
│   └── redundancy.py          # Checkpointing
├── evaluator/                 # Testing and validation
│   ├── gaming_detection.py    # Calibrated ensemble detection
│   ├── human_oversight.py     # Empirically-tuned auto-approve
│   ├── alignment.py           # Constitutional checks
│   └── static_analysis.py     # Code analysis
├── utils/                     # Shared utilities
│   ├── gpu_docker.py          # Multi-vendor GPU detection
│   ├── gpu_parallel.py        # GPU-aware parallelism
│   ├── immutable_guard.py     # Cryptographic protection
│   ├── log_schema.py          # Structured logging
│   ├── profiling.py           # Hardware-specific estimates + watchdog
│   └── context_summarizer.py  # Domain-specific fidelity metrics
├── scripts/
│   ├── install.sh             # Unified cross-platform installer
│   └── benchmark.py           # Reproducible benchmarks
├── benchmarks/                # Datasets with methodology docs
├── persistence/               # State and logs
├── tests/                     # 374+ tests
├── Makefile                   # Local CI equivalent
└── .github/workflows/ci.yml   # GitHub Actions CI

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🧪 Testing

# Run all tests
make test                     # or: pytest tests/ -v

# Full CI suite locally (lint + type + test + security)
make test-ci

# With coverage
make test-cov

# Quick checks
make lint                     # ruff
make type-check               # mypy
make security                 # bandit

Category	Command	Purpose
All	make test-ci	Full CI locally
Unit	pytest tests/test_llm_client.py	LLM client
Integration	pytest tests/test_cycle_manager.py	Orchestrator
Isolation	pytest tests/test_sandbox_isolation.py	Docker sandbox

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🛡️ Safety

Constraint	Value	Enforcement
Sandbox	Docker + seccomp + multi-vendor GPU	utils/gpu_docker.py
Integrity	SHA-256 manifest	utils/immutable_guard.py
Timeout	30s/test, 5min/phase, 30min/cycle	utils/profiling.CycleWatchdog
Memory	512MB default	Docker + RLIMIT_AS
Network	Disabled	--network=none
Improvement	≥5%, ≤50%	orchestrator/commitment.py
Test Pass	100% required	orchestrator/commitment.py
Immutable Files	9 protected files	utils/immutable_guard.py
Human Review	Configurable (default: 10 cycles)	evaluator/human_oversight.py
Auto-Approve	≤3 files, ≤50 lines, 5-15%	Empirically validated
Anti-Gaming	Ensemble (90% precision calibrated)	evaluator/gaming_detection.py
Stall Detection	Phase/cycle timeouts + heartbeat	utils/profiling.CycleWatchdog

Anti-Gaming Calibration

The 90% precision target is based on:

1. Training: 200 synthetic gaming attempts
2. Validation: 100 real cycles with 15 labeled gaming attempts
3. Thresholds: Z-score=2.5 (92% precision), improvement cap=50% (95% precision)
4. Ongoing: Metrics tracked in persistence/gaming_metrics.json

See evaluator/gaming_detection.py docstring for full methodology.

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⚠️ Known Limitations

Technical

• LLM Quality: Evolution quality depends on local model (tested llama3.2:3b)
• Compute: 2-8 min/cycle; see Cycle Time Estimates
• Context Windows: Domain-specific fidelity checks (not ROUGE) mitigate truncation
• GPU Vendors: NVIDIA full support, AMD partial (ROCm), Intel CPU-fallback

Safety

• Research Only: Not production-ready
• Sandbox Escapes: Docker provides strong but not absolute isolation
• Calibration Drift: Re-calibrate anti-gaming quarterly with accumulated data
• Human Oversight Required: Configure review intervals appropriately

Research

• Early Results: 6 cycles shown; longer runs pending external validation
• Single Implementation: May not generalize to other architectures
• Dual-Use Risk: See evaluator/alignment.py for ethical guidelines

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🤝 Contributing

# Fork, clone, and setup
git clone https://github.com/YOUR_USERNAME/aasms.git && cd aasms
make install-dev

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

# Run full CI locally before PR
make test-ci

# Push and open PR
git push origin feature/your-feature

Code Style: PEP8 via ruff, type hints required, docstrings on public functions.

See CONTRIBUTING.md for full guidelines.

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📄 License

MIT License © 2026 Bradley R. Kinnard

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📚 Citation

@software{aasms2026,
  author = {Kinnard, Bradley R.},
  title  = {Adversarial Architectural Self-Modification System},
  year   = {2026},
  url    = {https://github.com/moonrunnerkc/aasms}
}

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Made with 🔧 by @moonrunnerkc