Can activation verbalizers surface an internal chain of thought?
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We introduce an evaluation for activation verbalizers: can they surface a target model's reasoning as it solves a math problem in a single forward pass? For open-weight NLAs, the answer seems to be: "possibly, but definitely not reliably".Lots of important capabilities currently require AI models to reason "out loud" in a natural-language chain of thought, which means that we can monitor important parts of their thinking. It would be nice to have this same affordance for the reasoning that model
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