arXiv:2606.07157v1 Announce Type: new Abstract: Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind, and strategic reasoning. To compare models against humans, we estimate the $50\%$-task-completion time hor
The accelerating pace of AI development necessitates proactive measures to maintain oversight and control as models gain more sophisticated internal reasoning capabilities.
The ability of frontier AI models to reason internally without explicit chain-of-thought tokens directly impacts the effectiveness of current safety and monitoring protocols, posing risks to responsible AI development.
Traditional methods for monitoring AI safety that rely on observable reasoning steps may become insufficient, requiring new approaches to understand and verify model behavior.
- · AI safety researchers
- · Developers of new AI interpretability tools
- · Organizations prioritizing AI governance
- · Regulation relying solely on CoT monitoring
- · AI models lacking internal transparency
- · Human oversight without advanced tools
Reduced transparency in frontier AI model decision-making processes.
Increased difficulty in auditing and ensuring the safety and alignment of advanced AI systems.
Potential for unexpected and unexplainable AI behaviors to emerge in critical applications, leading to societal distrust.
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Read at arXiv cs.AI