---
license: mit
language:
  - en
tags:
  - verification
  - ai-safety
  - hallucination-detection
  - drift-detection
  - adversarial
  - code-generation
  - multi-model
datasets:
  - openai/openai_humaneval
metrics:
  - accuracy
pipeline_tag: text-generation
library_name: cmvk
---

# Cross-Model Verification Kernel (CMVK)

**CMVK** is a mathematical and adversarial verification library for detecting drift and hallucinations between AI model outputs. It implements the "Trust, but Verify (with a different brain)" philosophy.

## Model Description

CMVK is not a model itself, but a **verification framework** that orchestrates multiple LLMs (GPT-4, Gemini, Claude) in an adversarial configuration to reduce correlated blind spots.

### Architecture

```
┌─────────────────────────────────────────────────────────┐
│              Verification Kernel (Arbiter)               │
│  - Manages verification loop                            │
│  - Enforces strategy bans (Lateral Thinking)            │
│  - Makes final accept/reject decisions                  │
└───────────┬─────────────────────────────┬───────────────┘
            │                             │
    ┌───────▼────────┐          ┌────────▼────────┐
    │   Generator    │          │    Verifier     │
    │   (System 1)   │◄────────►│   (System 2)    │
    │   GPT-4o/o1    │  Hostile │  Gemini/Claude  │
    └────────────────┘  Review  └─────────────────┘
```

## Intended Use

### Primary Use Cases

1. **Code Generation Verification**: Verify LLM-generated code by having a different model attempt to find bugs, edge cases, and security issues.

2. **Hallucination Detection**: Calculate drift scores between outputs from different models to identify potential hallucinations.

3. **Research on Model Diversity**: Study how different model combinations reduce correlated blind spots.

4. **AI Safety Research**: Implement adversarial verification patterns for safer AI deployments.

### Out-of-Scope Use Cases

- ❌ Real-time production systems (latency-sensitive applications)
- ❌ Single-model self-correction (defeats the purpose)
- ❌ Tasks requiring human-in-the-loop verification
- ❌ High-stakes decisions without human oversight

## How to Use

### Installation

```bash
pip install cmvk
```

### Basic Usage (Primitive Library)

```python
import cmvk

# Verify drift between two outputs
score = cmvk.verify(
    output_a="def add(a, b): return a + b",
    output_b="def add(x, y): return x + y"
)

print(f"Drift Score: {score.drift_score:.2f}")  # 0.15 (low = similar)
print(f"Confidence: {score.confidence:.2f}")
print(f"Drift Type: {score.drift_type.value}")
```

### Advanced Usage (Full Framework)

```python
from cross_model_verification_kernel import (
    VerificationKernel,
    OpenAIGenerator,
    GeminiVerifier,
)

# Initialize with model diversity
kernel = VerificationKernel(
    generator=OpenAIGenerator(model="gpt-4o"),
    verifier=GeminiVerifier(model="gemini-1.5-pro"),
    enable_trace_logging=True,
    seed=42,  # For reproducibility
)

# Run adversarial verification
result = kernel.execute(
    task="Write a function to merge two sorted arrays in O(n) time"
)

print(f"Success: {result.is_complete}")
print(f"Solution: {result.final_result}")
```

## Training Data

CMVK itself is not trained. It orchestrates pre-trained foundation models:

| Component | Supported Models |
|-----------|------------------|
| Generator | GPT-4o, GPT-4-turbo, o1-preview |
| Verifier | Gemini 1.5 Pro, Claude 3.5 Sonnet |

## Evaluation

### Benchmark: HumanEval

| Method | Pass@1 | Blind Spot Reduction |
|--------|--------|---------------------|
| Single Model (GPT-4o) | 87.2% | 1.0x (baseline) |
| CMVK (GPT-4o + Gemini) | 91.5% | 4.3x |
| CMVK (GPT-4o + Claude) | 90.8% | 3.9x |

### Mathematical Framework

CMVK reduces blind spot probability using:

$$P(\text{combined error}) = P(\text{error})^2 + \rho \cdot P(\text{error}) \cdot (1 - P(\text{error}))$$

Where $\rho$ is the correlation coefficient between models (lower for diverse model pairs).

## Limitations

### Technical Limitations

1. **API Dependency**: Requires API access to multiple LLM providers (OpenAI, Google, Anthropic)
2. **Latency**: Multi-model verification adds latency (2-5x single model)
3. **Cost**: Multiple API calls increase inference costs
4. **Rate Limits**: Subject to provider rate limits

### Bias and Fairness

- Models may share biases from overlapping training data (e.g., Common Crawl)
- Verification effectiveness varies by domain and language
- Code-focused evaluation; other modalities less tested

### Failure Modes

- **Correlated Blind Spots**: If models share the same training gap, verification may fail
- **Adversarial Gaming**: Verifier may be overly harsh or miss subtle bugs
- **False Positives**: High drift scores don't always indicate errors

## Ethical Considerations

- CMVK is designed to **improve** AI safety, not replace human oversight
- Results should be validated by domain experts for critical applications
- The framework assumes good-faith use; it cannot detect malicious prompts

## Citation

```bibtex
@software{cmvk2026,
  author = {Siddique, Imran},
  title = {Cross-Model Verification Kernel: Adversarial Multi-Model Verification},
  year = {2026},
  url = {https://github.com/imran-siddique/cross-model-verification-kernel},
  license = {MIT}
}
```

## Model Card Contact

- **Author**: Imran Siddique
- **Repository**: [github.com/imran-siddique/cross-model-verification-kernel](https://github.com/imran-siddique/cross-model-verification-kernel)
- **PyPI**: [pypi.org/project/cmvk](https://pypi.org/project/cmvk)
