Automatic feature circuit detection — is offered as a concrete step toward → Enumerative safety

The paper places feature-circuit-detection’s payoff directly next to its most ambitious stated goal. Its discussion of future work frames tracing causal dependencies between features across layers as building toward "a lens for viewing language models under which causal dependencies are sparse," named explicitly as "a step towards the eventual goal of building an end-to-end understanding of how a model computes its outputs" (sparse-autoencoders, §"6.2 Limitations and future work", p. 9). The very next paragraph names that eventual goal: enumerative safety, Elhage et al.’s (2022b) dream of a complete, human-understandable list of a model’s features sufficient to guarantee it will not perform dangerous behaviors such as deception (sparse-autoencoders, §"6.3 Conclusion", p. 9). Feature-circuit-detection, demonstrated concretely on a single closing-parenthesis feature and its upstream causes, is the paper’s only worked example of what tracing those causal dependencies actually looks like in practice — a small, real instance of the "sparse causal dependencies" the future-work paragraph gestures at, sitting, by the paper’s own structure, as the nearest concrete precedent to the safety guarantee enumerative safety describes.