Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 11·4 min read
SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Explore SirenFNO for improved PDE solution approximations.
- Utilize sinusoidal representation networks for full frequency learning.
- Achieve significant parameter reductions in neural models.
- Test functional tensor decompositions for enhanced learning efficiency.
How It Works
SirenFNO leverages sinusoidal representation networks (SIRENs) to learn implicit representations that encompass a full frequency spectrum. This approach eliminates the need for frequency truncation, which is a common limitation in traditional FNOs.
By employing mode-wise kernel parameterization, SirenFNO can efficiently manage parameters while maintaining performance across different discretizations.