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Limit Analysis for Symbolic Multi-step Reasoning Tasks with Information Propagation Rules Based on Transformers

Artificial Intelligence 2025-09-30 v1

Abstract

Transformers are able to perform reasoning tasks, however the intrinsic mechanism remains widely open. In this paper we propose a set of information propagation rules based on Transformers and utilize symbolic reasoning tasks to theoretically analyze the limit reasoning steps. We show that the limit number of reasoning steps is between O(3L1)O(3^{L-1}) and O(2L1)O(2^{L-1}) for a model with LL attention layers in a single-pass.

Keywords

Cite

@article{arxiv.2509.23178,
  title  = {Limit Analysis for Symbolic Multi-step Reasoning Tasks with Information Propagation Rules Based on Transformers},
  author = {Tian Qin and Yuhan Chen and Zhiwei Wang and Zhi-Qin John Xu},
  journal= {arXiv preprint arXiv:2509.23178},
  year   = {2025}
}
R2 v1 2026-07-01T06:00:30.939Z