@inproceedings{chen-etal-2026-adaptive,
    title = "Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models",
    author = "Chen, Tianle  and
      Cheng, Pengyu  and
      Zhu, Qiyuan  and
      Wang, Jiacheng  and
      Liu, Bei  and
      Gu, Hao  and
      Shen, Ruijie  and
      Hou, Xiaofeng  and
      Han, Sirui  and
      Liu, Jiacheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1130/",
    pages = "24647--24662",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) have achieved exceptional performance in complex reasoning via Chain-of-Thought (CoT), yet the associated computational costs remain prohibitive. CoT reasoning contains significant untapped efficiency potential across two dimensions: \textit{temporal redundancy}, where reasoning steps may be unnecessary, and \textit{spatial redundancy}, where computations can be performed at reduced precision. While current optimization techniques often necessitate resource-intensive fine-tuning or data curation, we introduce ASTRO (Adaptive Spatial and Temporal Redundancy Optimization), a training-free framework that simultaneously addresses both dimensions. ASTRO leverages Dewey{'}s reflective thinking model to segment reasoning phases, applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination. Empirical results across diverse reasoning benchmarks demonstrate that ASTRO achieves up to an 11.3 $\times$ efficiency gain without compromising accuracy, highlighting the advantages of holistic multi-dimensional redundancy management over isolated optimization methods."
}