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The SAEBench benchmark for evaluating sparse autoencoders and related methods in language-model interpretability, including its tasks, baselines, scores, and design. Excludes benchmarks that do not evaluate sparse autoencoders.

On HEADLINES and CONGRESS, HypotheSAEs produce more hypotheses rated predictive, helpful, and interpretable than baselines.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · verifiableA factual claim that could be checked directly against observation or primary records.constitution →Sparse Autoencoders Mechanistic Interpretability AI Scientific Discoveryimportance · minorImportance 0.30, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

For the canonical Gemma Scope SAE, permuting decoder weights leaves SAEBench sparse-probing, SAE-Probes, and AutoInterp scores within reseed noise.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · verifiableA factual claim that could be checked directly against observation or primary records.constitution →Sparse Autoencoders Mechanistic Interpretabilityimportance · minorImportance 0.25, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

Random-weight, L0-matched SAE baselines score substantially above chance on SAEBench sparse-probing tasks.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · verifiableA factual claim that could be checked directly against observation or primary records.constitution →Sparse Autoencoders Mechanistic Interpretabilityimportance · minorImportance 0.35, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

Mean change in test AUC from adding SAE probes to baseline probing methods, Gemma-2-9B layer 20, standard conditions, 113 datasets

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →QuantityA quantity: a value the graph estimates rather than a proposition it affirms or denies. Its estimate stands where a claim's status would.constitution →Sparse Autoencoders Mechanistic Interpretability Large language modelsimportance · minorImportance 0.30, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

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