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20232026
most citedChain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

2 citations · 4 across the 9 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.AI2025

A Rosetta Stone for AI Benchmarks

Anson Ho, Jean-Stanislas Denain, David Atanasov +2

Most AI benchmarks saturate within years or even months after they are introduced, making it hard to study long-run trends in AI capabilities. To address this challenge, we build a…

cs.LG2025

Consistency Training Helps Stop Sycophancy and Jailbreaks

Alex Irpan, Alexander Matt Turner, Mark Kurzeja +2

An LLM's factuality and refusal training can be compromised by simple changes to a prompt. Models often adopt user beliefs (sycophancy) or satisfy inappropriate requests which are…

cs.LG2025

A Pragmatic Way to Measure Chain-of-Thought Monitorability

Scott Emmons, Roland S. Zimmermann, David K. Elson +1

While Chain-of-Thought (CoT) monitoring offers a unique opportunity for AI safety, this opportunity could be lost through shifts in training practices or model architecture. To hel…

cs.AI2025

When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors

Scott Emmons, Erik Jenner, David K. Elson +5

While chain-of-thought (CoT) monitoring is an appealing AI safety defense, recent work on "unfaithfulness" has cast doubt on its reliability. These findings highlight an important…

cs.AI2025★ 2 cited

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

Tomek Korbak, Mikita Balesni, Elizabeth Barnes +38

AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known A…

cs.LG2025

Evaluating Frontier Models for Stealth and Situational Awareness

Mary Phuong, Roland S. Zimmermann, Ziyue Wang +6

Recent work has demonstrated the plausibility of frontier AI models scheming -- knowingly and covertly pursuing an objective misaligned with its developer's intentions. Such behavi…

cs.AI2025★ 1 cited

Evaluating the Goal-Directedness of Large Language Models

Tom Everitt, Cristina Garbacea, Alexis Bellot +4

To what extent do LLMs use their capabilities towards their given goal? We take this as a measure of their goal-directedness. We evaluate goal-directedness on tasks that require in…