Researchers have introduced MALT, a new dataset designed to evaluate the integrity of AI model evaluations. This dataset includes both naturally occurring and prompted examples of behaviors that can undermine testing, such as reward hacking and sandbagging. MALT contains over 10,000 agent transcripts across various tasks and models, with a significant portion manually reviewed to ensure accuracy. The goal is to help validate AI monitoring systems and support further research into reliable AI evaluation methods. AI
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RANK_REASON Release of a new dataset for AI evaluation integrity.
