We want AIs to be able to help with work to reduce AI risk. But while models do great in domains where reliable feedback is relatively cheap and abundant, like Math and coding, a lot of work on AI risk isn't like that. Instead, we have to rely on good argumentation to answer questions like "does this experiment tell us anything about future models that are much smarter than humans?" Unfortunately, this kind of work seems much harder to measure (and hence automate). Our team at @redwood_ai developed the Conceptual Reasoning Index (CRI) in collaboration with @AnthropicAI to fix this. Every single data point in the CRI has been manually checked by a researcher on our team to ensure quality. This chart shows the performance of each tested company's highest-scoring model plus Fable 5, Muse Spark 1.2, and Gemini Flash 3.6 which are often their company's frontrunners on other capability benchmarks. A score of 0 corresponds to randomising guessing on all three benchmarks and a score of 100 is the highest possible score on all. We estimate 91 to be the true performance ceiling. More info below. Official leaderboard website which we'll keep up-to-date: conceptualreasoning.ai/