发表机构
Southern University of Science and Technology（南方科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
VeriForge是混合式主动写作系统，通过主动内联高亮、双流查询、空间知识画布三种机制，帮助作者识别叙事创作中的潜在知识缺口，提升领域知识整合能力，获用户与专家认可。
AI 中文摘要
优秀的小说凭借精确细节获得逼真度，从如何握持长剑刺穿盔甲缝隙，到为何流血的尸体尚未散发出腐臭气味，将领域专业知识融入虚构世界的架构中。当前的AI写作工具在发现和整合陌生领域知识至叙事中的支持有限：它们要求作者无法提出的明确查询，生成可能使文风同质化的成品散文，或仅在作者已知的范围内提供协助。我们认为，AI应向作者揭示潜在知识缺口，同时保留作者将发现的知识转化为真实散文的自主权。基于对9位小说作家的形成性访谈，我们提出VeriForge，这是一个混合式主动写作系统，通过划分认知劳动实现：系统承担领域发现的主动任务，作者保留叙事合成的全部主动控制权。VeriForge通过三种互补机制实现这一点：主动内联高亮在作者创作时标记潜在知识缺口；双流查询将对话式响应与锚定源的知识卡片配对，用于直接事实提取；空间知识画布允许作者在写作过程中组织和连接发现的知识。这些机制由基于领域特定源材料的图增强检索生成式管道提供支持。一项被试内用户研究（N=12）提供初步证据，该范式帮助作者识别此前被忽视的知识缺口，支持创造性探索，且在受控冷启动写作任务中，被专家评分者认为能产生领域根基更强的段落。
英文摘要
Great fiction earns its verisimilitude through precise details, from how a longsword is gripped to pierce armor gaps to why a bleeding corpse cannot yet smell of decay, weaving domain expertise into the fabric of invented worlds. Current AI writing tools offer limited support for discovering and integrating unfamiliar domain knowledge into narrative. They require explicit queries that authors cannot formulate, generate finished prose that risks homogenizing voice, or assist only within the boundaries of what authors already know. We argue that AI should reveal latent knowledge gaps to writers while preserving their agency to transform discovered knowledge into authentic prose. Grounded in formative interviews with 9 fiction writers, we present VeriForge, a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis. VeriForge realizes this through three complementary mechanisms. Proactive inline highlighting flags potential knowledge gaps as authors draft. Dual-stream querying pairs conversational responses with source-anchored Knowledge Cards for direct fact extraction. A spatial Knowledge Canvas allows authors to organize and connect discovered knowledge across their writing. These mechanisms are powered by a graph-based retrieval-augmented generation pipeline grounded in domain-specific source materials. A within-subjects user study (N=12) provides preliminary evidence that this paradigm helps authors recognize previously overlooked knowledge gaps, supports creative exploration, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.
Comments14 pages, 5 figures, Accepted by UIST'26
