@conference{CheMilSchGuo26,
  title = {On the Emergence and Test-Time Use of Structural Information in Large Language Models},
  booktitle = {Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)},
  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.},
  pages = {1449--1465},
  editors = {Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David},
  publisher = {Association for Computational Linguistics},
  month = jul,
  year = {2026},
  author = {Chen, M. C. and Miller, M. and Sch{\"o}lkopf, B. and Guo, S.},
  url = {https://aclanthology.org/2026.acl-long.65/},
  month_numeric = {7}
}