Monitoring latent reasoning in looped language models

Abstract:Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage cost. Despite their rising popularity, the analysis of looped LMs is still in its infancy: little is known about whether the iterative transformations actually improve their capabilities, or whether these models remain stable under prompt perturbation. We construct an inspection protocol rooted in Mechanistic Interpretability comprising five diverse lenses. We apply it to examine eight looped LMs from four families. Models differ in many aspects but the key one is whether the prompt is transmitted only within the looped state (carry loops: Ouro, LoopUS) or re-supplied at every iteration (re-read loops: Parcae, and Rec-OLMo & Huginn from the Raven family). The results of our study provide valuable insights concerning safety and efficient use of looped language models.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.18127 [cs.AI]

(or arXiv:2610.18127v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.18127 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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