We introduce White-Basilisk, a novel vulnerability detection model that demonstrates superior performance while challenging prevailing assumptions in AI model scaling.
Key Innovation
Utilizing an innovative architecture integrating Mamba layers, linear self-attention, and Mixture of Experts, White-Basilisk achieves state-of-the-art results with only 200M parameters and can process sequences up to 128,000 tokens for comprehensive codebase analysis.
Technical Highlights
- Efficient Architecture: Combines cutting-edge techniques for optimal performance
- Scalable Processing: Handles large codebases with extended context windows
- Superior Results: Outperforms larger models while using fewer parameters
- Real-world Application: Designed for practical vulnerability detection scenarios
Impact
This research challenges conventional wisdom about model scaling and demonstrates that architectural innovation can achieve better results than simply increasing model size. The work has significant implications for both cybersecurity applications and efficient AI model design.