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Interpretability for Turing Machines

This story is from 2026-09-07. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.04661v1 Announce Type: cross Abstract: We show that susceptibilities, an interpretability technique developed for neural networks, can identify the presence of algorithmic structure in Turing machines by probing the local loss landscape of a learning problem for noisy Turing machines int…

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  1. 2026-09-07 04:00 · arXiv stat.ML
    Interpretability for Turing Machines

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