[2606.12689] Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models
[Submitted on 10 Jun 2026]
Title:Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models
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Abstract:Latent reasoning models (LRMs) replace explicit chain-of-thought with continuous thoughts. Recent work treats observable latent-state patterns, such as BFS-like frontiers and decodable arithmetic computation, as evidence for internal reasoning mechanisms. Evaluating two LRMs (Coconut and CODI) against controls lacking the proposed recurrence or curriculum, we find these patterns also appear in the controls and do not always causally affect behavior. Causal interventions reveal that latent-thought utilization is not binary but graded, scaling with a thought's causal effect on model behavior. Geometric analyses reveal this effect concentrates in low-rank directions whose step-to-step geometry grows more structured as their behavioral influence increases. Latent thoughts should therefore be treated as hidden computation, not hidden explanation: decodability, attention, or static structure alone cannot establish mechanism. LRM interpretability thus requires matched controls and causal tests.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.12689 [cs.CL]
(or arXiv:2606.12689v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.12689
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Darpan Aswal [view email] [v1] Wed, 10 Jun 2026 21:23:22 UTC (318 KB)
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