[2605.00847] H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models
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arXiv:2605.00847 (cs)
[Submitted on 15 Apr 2026]
Title:H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models
Authors:Cutter Dawes, Aryan Sharma, Angelos Ioannis Lagos, Shivam Raval
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Abstract:Representing and navigating hierarchy is a fundamental primitive of reasoning. Large language models have demonstrated proficiency in a wide variety of tasks requiring hierarchical reasoning, but there exists limited analysis on how the models geometrically represent the necessary latent constructions for such thinking. To this end, we develop \textit{H-probes}, a collection of linear probes that extract hierarchical structure, specifically depth and pairwise distance, from latent representations. In synthetic tree traversal tasks, the H-probes robustly find the subspaces containing hierarchical structure necessary to complete the tasks; furthermore, in comprehensive ablation experiments, we show that these hierarchy-containing subspaces are low-dimensional, causally important for high task performance, and generalize within- and out-of-domain. Furthermore, we find analogous, though weaker, hierarchical structure in real-world hierarchical contexts such as mathematical reasoning traces. These results demonstrate that models represent hierarchy not only at the level of syntax and concepts, but at deeper levels of abstraction -- including the reasoning process itself.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.00847 [cs.CL]
(or arXiv:2605.00847v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.00847
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arXiv-issued DOI via DataCite
Submission history
From: Aryan Sharma [view email] [v1] Wed, 15 Apr 2026 00:59:17 UTC (8,535 KB)
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