@inproceedings{sun-etal-2026-cot,
    title = "{IS}-{C}o{T}: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking",
    author = "Sun, Zechen  and
      Sun, Yuyang  and
      Tang, Zecheng  and
      Li, Juntao  and
      Hu, Wenpeng  and
      Chen, Wenliang  and
      Luo, Zhunchen  and
      Geng, Guotong  and
      Zhang, Min",
    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.911/",
    pages = "19874--19887",
    ISBN = "979-8-89176-390-6",
    abstract = "Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in logic-intensive domains, our evaluation reveals that they suffer from a severe length collapse in open-ended writing, where performance degrades sharply as target lengths exceed 2,000 words. We attribute this failure to the limitation of static hierarchical planning, which struggles to provide dynamic guidance over extended contexts. To bridge this gap, we introduce the **Interleaved Structural Chain-of-Thought (IS-CoT)** framework. Unlike external agentic workflows, **IS-CoT** embeds a dynamic Plan-Write-Reflect cycle into the generation process, enabling continuous strategy adaptation and global alignment without additional assistance. Based on this framework, we construct a high-quality dataset of interleaved reasoning traces via a multi-teacher pipeline and train **IS-Writer-8B**. Experiments demonstrate that IS-Writer-8B achieves state-of-the-art performance on challenging long-form benchmarks (e.g., +3.08 vs. DeepSeek-V3.2 on LongBench-Write), exhibiting robust length compliance and coherence competitive with significantly larger proprietary models."
}