Intent Awareness Boosts LLM Long-form Scientific Report Generation

A new arXiv paper (2603.27435) proposes that enhancing large language models' (LLMs) awareness of authorial intent can significantly improve the quality of generated long-form scientific reports. The researchers developed structured, tag-based schemes to elicit implicit intents behind writing and citation choices. These extracted intents were shown to enhance zero-shot generation in LLMs and enable creation of high-quality synthetic data for fine-tuning smaller models. Experiments across various challenging scientific report generation tasks showed average improvements of +2.9 absolute points for large models and +12.3 for small models over baselines. The paper was announced as a replace-cross update. The work addresses the limitation that LLMs, despite being trained on diverse academic papers, are not exposed to the reasoning processes that guide authors in crafting documents. The findings suggest that intent-aware training can improve the factual and structural quality of AI-generated reports, with implications for automated scientific writing and knowledge-intensive tasks.