Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 25·4 min read
On the Infinite Width and Depth Limits of Predictive Coding Networks
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 predictive coding as an alternative to backpropagation.
- Utilize findings to improve training stability in deep networks.
- Implement local updates in wider networks for efficiency.
- Reach activity equilibrium to ensure convergence to backpropagation.
How It Works
The study shows that in linear residual networks, the parameterizations that stabilize predictive coding are the same as those for backpropagation. When the network width is much larger than its depth, the predictive coding energy function converges to the quadratic loss of backpropagation, allowing for equivalent gradient calculations.