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arXiv Machine Learning By Abdullah X

Markovian Circuit Tracing: Ground-Truth Tests of Transformer State Abstractions

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Markovian Circuit Tracing (MCT) is a benchmark that tests whether transformer activations encode a compact state abstraction with recoverable transition structure. In experiments with small causal transformers trained on sequences from four‑state hidden Markov models, models achieve near‑optimal validation loss and recover transition matrices accurately under easy and medium observability, but performance collapses to control levels under hard observability. Various controls—including shuffled data, untrained activations, and sparse autoencoders—demonstrate that the recovered transition structure is genuinely causally useful rather than a trivial artifact.

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