The Digital Press

A new AI safety paper proposes monitoring AI systems' "chains of thought" (CoT) expressed in human language to detect harmful intent, but this approach may be fragile and imperfect. The authors urge further research and caution in preserving CoT monitorability as AI models evolve.

Why it matters: CoT monitoring offers a novel transparency layer to catch AI misbehavior early, improving oversight beyond existing methods.

The stakes: If CoT monitorability degrades, AI systems might hide harmful intent, making safety measures less effective and riskier.

The big picture: Ongoing advances may shift AI reasoning away from human-readable language, challenging current safety frameworks reliant on CoT.

Commenters say: Many question CoT monitoring’s reliability long term, its vulnerability to being bypassed, and debate whether it’s the best path forward for AI safety.