A new benchmark called SAEScientist-Bench evaluates whether AI agents can conduct autonomous sparse autoencoder (SAE) interpretability research, addressing a gap in recursive self-improvement work that has focused mainly on automating model training pipelines. The benchmark targets post-hoc monitoring and auditing of what models learn, which the source frames as necessary for reliable autonomous development and safe alignment.
While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essent
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