On the Emergence and Test-Time Use of Structural Information in Large Language Models

Michelle Chao Chen, Moritz Miller, Bernhard Schölkopf, Siyuan Guo
1/25/2026
cs.CLcs.LG

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Abstract

Learning structural information from observational data is central to producing new knowledge outside the training corpus. This holds for mechanistic understanding in scientific discovery as well as flexible test-time compositional generation. We thus study how language models learn abstract structures and utilize the learnt structural information at test-time. To ensure a controlled setup, we design a natural language dataset based on linguistic structural transformations. We empirically show that the emergence of learning structural information correlates with complex reasoning tasks, and that the ability to perform test-time compositional generation remains limited.

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Cite this paper

@article{chen2026emergence,
  title  = {On the Emergence and Test-Time Use of Structural Information in Large Language Models},
  author = {Michelle Chao Chen and Moritz Miller and Bernhard Schölkopf and Siyuan Guo},
  year   = {2026},
  eprint = {2601.17869},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.17869v1}
}

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