Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 11·4 min read
Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models
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
- Monitor reasoning budgets to avoid overconfidence in LLMs.
- Implement CABStop to halt reasoning when confidence diverges.
- Understand non-monotonic calibration behavior in LLMs.
- Evaluate model performance across different reasoning depths.
How It Works
The study introduces a Hypothesis Lock-In model that explains how longer reasoning can lead to consistent but incorrect outputs. As reasoning depth increases, models may generate explanations that seem valid internally but are ultimately wrong, leading to overconfidence.