@inproceedings{hao-etal-2026-alignment,
    title = "What Does Alignment Cost? The Structural Brittleness of Chain-of-Thought Reasoning",
    author = "Hao, Joanna  and
      Jiang, Shanduojiao  and
      Nakka, Sai Asish",
    editor = "Chen, Canyu  and
      Zhang, Yuji  and
      Li, Zoey Sha  and
      Wang, Zihan  and
      Wang, Qineng  and
      Su, Jinyan  and
      Kargupta, Priyanka  and
      Marjanovi{\'c}, Sara Vera  and
      Pan, Jeff Z.  and
      Bansal, Mohit  and
      Augenstein, Isabelle  and
      Han, Jiawei  and
      Ji, Heng  and
      Li, Manling",
    booktitle = "Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models ({K}now{FM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.knowfm-1.3/",
    pages = "25--33",
    ISBN = "979-8-89176-403-3",
    abstract = "While Chain-of-Thought (CoT) prompting enables Large Language Models to explicitly justify their predictions, the extent to which these textual rationales faithfully reflect internal computation remains unclear. We investigate the circuit-level impact of alignment by performing a strict within-family comparison of the 1B-parameter Llama 3 architecture (Base vs. Instruct). Executing dynamic circuit discovery and dual-direction resample ablation on unconstrained CoT traces across synthetic mathematical primitives and a GSM8K proxy, we find that foundation models possess highly redundant, self-repairing computational networks; completely corrupting their primary reasoning circuits yields a minimal performance drop (2.92{\%}) due to the dynamic compensation of backup heads (the Hydra Effect). In contrast, the instruction-tuned model exhibits reduced structural redundancy, suffering more than double the degradation (6.79{\%}) under identical perturbation. We formalize our observation as an ``Alignment Tax on Redundancy'': optimizing for human-preference compliance repurposes dormant backup circuits, centralizing mathematical routing and rendering the aligned model{'}s reasoning pathways significantly more vulnerable to internal perturbation."
}