Abstract
Central to long-form text generation in vertical domains is the "impossible trinity" confronting current large language models (LLMs): the simultaneous achievement of low hallucination, deep logical coherence, and personalized expression. This study establishes that this bottleneck arises from existing generative paradigms succumbing to the Statistical Smoothing Trap, a phenomenon that overlooks the high-entropy information acquisition and structured cognitive processes integral to expert-level writing. To address this limitation, we propose the DeepNews Framework, an agentic workflow that explicitly models the implicit cognitive processes of seasoned financial journalists. The framework integrates three core modules: first, a dual-granularity retrieval mechanism grounded in information foraging theory, which enforces a 10:1 saturated information input ratio to mitigate hallucinatory outputs; second, schema-guided strategic planning, a process leveraging domain expert knowledge bases (narrative schemas) and Atomic Blocks to forge a robust logical skeleton; third, adversarial constraint prompting, a technique deploying tactics including Rhythm Break and Logic Fog to disrupt the probabilistic smoothness inherent in model-generated text. Experiments delineate a salient Knowledge Cliff in deep financial reporting: content truthfulness collapses when retrieved context falls below 15,000 characters, while a high-redundancy input exceeding 30,000 characters stabilizes the Hallucination-Free Rate (HFR) above 85%. In an ecological validity blind test conducted with a top-tier Chinese technology media outlet, the DeepNews system--built on a previous-generation model (DeepSeek-V3-0324)-achieved a 25% submission acceptance rate, significantly outperforming the 0% acceptance rate of zero-shot generation by a state-of-the-art (SOTA) model (GPT-5).
Abstract (translated)
在垂直领域中长篇文本生成的核心问题，是当前大型语言模型（LLMs）面临的“不可能的三难困境”：即无法同时实现低虚假信息率、深刻的逻辑连贯性和个性化表达。本研究确立了这一瓶颈源自现有生成范式陷入统计平滑陷阱的现象，该现象忽略了获取高熵信息和结构化认知过程对于专家级写作的重要性。为解决此限制，我们提出了DeepNews框架，这是一个代理工作流，明确地模拟资深财经记者的隐性认知过程。该框架集成了三个核心模块：首先是一个基于觅食理论的双粒度检索机制，它规定了一个10:1的信息饱和输入比例以减少虚假信息输出；其次是一套方案引导的战略规划流程，利用领域专家知识库（叙事模式）和原子块构建一个坚固的逻辑骨架；第三是使用包括节奏打断和逻辑迷雾等战术的对抗性约束提示技术，以破坏模型生成文本中的概率平滑性。实验揭示了深度财经报道中显著的知识断崖：当检索到的内容少于15,000字符时，内容真实性会崩溃，而超过30,000字符的高冗余输入可以将无虚假信息率（HFR）稳定在85%以上。在一个顶级中国科技媒体进行的生态有效盲测中，基于前一代模型（DeepSeek-V3-0324）构建的DeepNews系统实现了25%的提交接受率，显著优于零样本生成状态下最先进（SOTA）模型（GPT-5）的0%接受率。
URL
https://arxiv.org/abs/2512.10121
