AI RESEARCH

Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure

arXiv CS.AI

ArXi:2602.08783v2 Announce Type: replace Latent or continuous chain-of-thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate computations are difficult to evaluate beyond correlation-based probes. In this paper, we view latent chain-of-thought as a manipulable causal process in representation space by modeling latent steps as variables in a structural causal model (SCM) and analyzing their effects through step-wise $\mathrm{do}$-interventions.