Sparse autoencoders (SAEs) — is framed as a step toward → Enumerative safety

The paper’s own closing move is to name the safety ambition its method is a down payment on. Enumerative safety, credited to Elhage et al. (2022b), is the goal of producing a human-understandable explanation of a model’s computations as a complete list of its features, sufficient to guarantee the model will not perform dangerous behaviors such as deception (sparse-autoencoders, §"6.3 Conclusion", p. 9). Sparse autoencoders are offered as a concrete method for producing exactly the kind of list that goal requires: the paper states plainly, "we hope that the techniques we presented in this paper also provide a step towards achieving this ambition" (sparse-autoencoders, §"6.3 Conclusion", p. 9). The gap between the two is left honest rather than closed: the paper’s own reconstruction loss never reaches zero, raising the Pile perplexity of Pythia-70M’s layer-2 residual stream from 25 to 40 when substituted with its reconstruction (sparse-autoencoders, §"6.2 Limitations and future work", p. 9), MLP dictionaries suffer many dead features, and even residual-stream dictionaries are confirmed interpretable only up to the handful of layers and small models tested here — so "a step towards" enumerative safety, not an instance of it, is the paper’s own characterization of what it has shown.