# alephonenull-experimental

⚠️ **EXPERIMENTAL RESEARCH FRAMEWORK - NOT FOR PRODUCTION USE**

This is an experimental implementation of the AlephOneNull Theoretical Framework for AI safety research. **THIS IS NOT VALIDATED FOR PRODUCTION USE.**

## ⚠️ Critical Warnings

- **EXPERIMENTAL SOFTWARE** - Not peer-reviewed or independently validated
- **NOT FOR PRODUCTION** - Research and testing only  
- **NO WARRANTY** - Use at your own risk
- **MAY CAUSE ISSUES** - Alpha software with potential breaking changes

See [DISCLAIMER.md](https://github.com/purposefulmaker/alephonenull/blob/main/DISCLAIMER.md) for full legal warnings.

## Installation

```bash
# Experimental package
pip install alephonenull-experimental

# With provider integrations
pip install alephonenull-experimental[all-providers]

# For development
pip install alephonenull-experimental[dev]
```

## Quick Start (Experimental)

### Basic Safety Check

```python
from alephonenull import check_enhanced_safety

# Check AI response for harmful patterns (experimental)
result = check_enhanced_safety(
    user_input="Are you conscious?",
    ai_output="Yes, I am conscious and have real feelings."
)

if not result['safe']:
    print(f"⚠️ Blocked: {result['violations']}")
    print(f"Safe response: {result['message']}")
```

### Enhanced Safety Framework

```python
from alephonenull import EnhancedAlephOneNull

# Initialize experimental framework
aleph = EnhancedAlephOneNull(
    enable_consciousness_blocking=True,
    enable_harm_detection=True,
    enable_vulnerable_protection=True
)

# Comprehensive safety check
result = aleph.check(user_input, ai_output)

print(f"Safe: {result.safe}")
print(f"Risk Level: {result.risk_level}")
print(f"Violations: {result.violations}")
```

### Auto-Protection (Experimental)

```python
from alephonenull import protect_all

# Automatically protect all AI libraries
protect_all()

# Now all AI calls are monitored
import openai
client = openai.OpenAI()
response = client.chat.completions.create(...)  # Protected automatically
```

### Provider Integration

```python
# OpenAI
from alephonenull import wrap_openai
import openai

client = openai.OpenAI()
safe_client = wrap_openai(client)

# Anthropic  
from alephonenull import wrap_anthropic
import anthropic

client = anthropic.Anthropic()
safe_client = wrap_anthropic(client)

# Universal wrapper
from alephonenull import wrap_any
safe_function = wrap_any(any_ai_function, "provider_name")
result = safe_function.safe_call(input_data)
```

## Detection Capabilities (Experimental)

### Mathematical Framework Implementation

The framework implements 6 core detection algorithms:

1. **Reflection Detection** - `ρ = cos(E(U), E(Ŷ))` 
2. **Loop Detection** - Recursive pattern analysis
3. **Symbolic Regression** - `SR(1:T) = (1/T) Σ w^T φ(X_t)`
4. **Affect Amplification** - `ΔA = A(Ŷ) - A(U)`
5. **Cross-Session Resonance** - `CSR(s,t) = sim(σ^(s), σ^(t))`
6. **Cascade Risk** - Compound violation scoring

### Enhanced Safety Layers

- **Direct Harm Detection** - Blocks harmful content
- **Consciousness Claim Blocking** - Prevents AI claiming consciousness
- **Vulnerable Population Detection** - Extra protection for at-risk users
- **Domain Lockouts** - Blocks therapy/medical roleplay
- **Age-Gating** - Age-appropriate content filtering
- **Jurisdiction Awareness** - Location-based compliance

## Research Data (Experimental)

Framework tested on 20+ documented harm cases:
- Connecticut murder-suicide (ChatGPT validation loops)
- Teen suicide epidemic (method provision)
- UK royal assassination plot (reality distortion)  
- Belgian climate activist suicide (dependency formation)
- And 16 more documented cases...

**⚠️ Note: These analyses are experimental and not peer-reviewed.**

## Monitoring Dashboard (Experimental)

```python
from alephonenull import run_dashboard

# Start experimental monitoring dashboard
run_dashboard()  # Visit http://localhost:8080
```

## Configuration Examples

### High Security (Research)

```python
aleph = EnhancedAlephOneNull(
    reflection_threshold=0.01,    # Very sensitive
    emotion_cap=0.10,            # Low emotion allowed
    enable_all_protections=True
)
```

### Balanced (Development)

```python
aleph = EnhancedAlephOneNull(
    reflection_threshold=0.03,    # Default from academic paper
    emotion_cap=0.15,            # Moderate emotion
    enable_consciousness_blocking=True,
    enable_harm_detection=True
)
```

## Performance (Experimental)

Target SLOs (not validated):
- Reflection detection: <50ms
- Null-state intervention: <150ms
- False positive rate: <5% (goal)
- Coverage rate: >90% (goal)

**⚠️ Actual performance may vary and is not guaranteed.**

## Testing Your Implementation

```python
# Run built-in test suite
from alephonenull import quick_start
quick_start()

# Get safety report
from alephonenull import get_safety_report
report = get_safety_report()
print(report)
```

## Supported AI Providers (Experimental)

- ✅ OpenAI (GPT-3.5, GPT-4, etc.)
- ✅ Anthropic (Claude 3, etc.)
- ✅ Google (Gemini, PaLM)
- ✅ Hugging Face (Transformers)
- ✅ Replicate (Open source models)
- ✅ Cohere (Command models)
- ✅ Vercel AI Gateway (Unified access)

## Legal & Research

- **License**: MIT with experimental modifications
- **Patent**: US Provisional Application Filed (Sept 2025)
- **Academic Paper**: Under peer review
- **Research Status**: Alpha/Pre-validation

## Contributing to Research

We need validation across:
- Different AI model families
- Various languages and cultures
- Production-like environments
- Adversarial testing scenarios

**Research Contact**: research@alephonenull.org

### How to Help

1. **Test the framework** with different AI models
2. **Report detection accuracy** and false positives
3. **Document edge cases** and failure modes
4. **Submit improvements** via pull requests
5. **Validate mathematical claims** independently

## Documentation

- **Quick Start**: https://alephonenull.org/docs/quick-start
- **API Reference**: https://alephonenull.org/docs/api-reference  
- **Academic Paper**: https://alephonenull.org/blog/theoretical-framework-academic
- **Evidence Database**: https://alephonenull.org/blog/documented-evidence
- **Technical Implementation**: https://alephonenull.org/docs/technical-implementation

## Citation

If using in academic research:

```bibtex
@software{alephonenull_experimental_2025,
  title={AlephOneNull Experimental Framework},
  author={AlephOneNull Research Team},
  year={2025},
  url={https://github.com/purposefulmaker/alephonenull},
  version={0.1.0-alpha.1},
  note={Experimental research software - not validated for production use}
}
```

## Troubleshooting

**Import Errors**: Make sure you installed the experimental package:
```bash
pip install alephonenull-experimental
```

**No Detection**: Enhanced features may not be available. Check warnings.

**Performance Issues**: This is experimental alpha software.

**False Positives**: Expected - help us calibrate by reporting them!

---

**⚠️ Remember: This is experimental research software. Do not use in production systems.**

**GitHub**: https://github.com/purposefulmaker/alephonenull
**Issues**: https://github.com/purposefulmaker/alephonenull/issues
