[Paper] IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking

Source: arXiv - 2606.09709v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.CL

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CL.

Authors

• Zechen Sun
• Yuyang Sun
• Zecheng Tang
• Juntao Li
• Wenpeng Hu
• Wenliang Chen
• Zhunchen Luo
• Guotong Geng
• Min Zhang

Paper Information

• arXiv ID: 2606.09709v1
• Categories: cs.CL
• Published: June 8, 2026
• PDF: Download PDF