We present the Living Novel, an end-to-end system that transforms any literary work into an immersive, multi-character conversational experience. This system is designed to solve two fundamental challenges for LLM-driven characters. Firstly, generic LLMs suffer from persona drift, often failing to stay in character. Secondly, agents often exhibit abilities that extend beyond the constraints of the story's world and logic, leading to both narrative incoherence (spoiler leakage) and robustness failures (frame-breaking). To address these challenges, we introduce a novel two-stage training pipeline. Our Deep Persona Alignment (DPA) stage uses data-free reinforcement finetuning to instill deep character fidelity. Our Coherence and Robustness Enhancing (CRE) stage then employs a story-time-aware knowledge graph and a second retrieval-grounded training pass to architecturally enforce these narrative constraints. We validate our system through a multi-phase evaluation using Jules Verne's Twenty Thousand Leagues Under the Sea. A lab study with a detailed ablation of system components is followed by a 5-day in-the-wild diary study. Our DPA pipeline helps our specialized model outperform GPT-4o on persona-specific metrics, and our CRE stage achieves near-perfect performance in coherence and robustness measures. Our study surfaces practical design guidelines for AI-driven narrative systems: we find that character-first self-training is foundational for believability, while explicit story-time constraints are crucial for sustaining coherent, interruption-resilient mobile-web experiences.

翻译：我们提出了Living Novel系统，这是一个端到端的框架，能够将任意文学作品转化为沉浸式的多角色对话体验。该系统旨在解决大语言模型驱动角色面临的两个核心挑战：首先，通用大语言模型存在角色漂移问题，难以保持角色一致性；其次，智能体常表现出超越故事世界设定与逻辑的能力，导致叙事不连贯（剧透泄露）与鲁棒性失效（框架突破）。为应对这些挑战，我们提出了一种新颖的两阶段训练流程。深度角色对齐阶段采用无数据强化微调技术，以实现深层次的角色忠实度；连贯性与鲁棒性增强阶段则通过构建故事时间感知知识图谱并结合第二轮检索增强训练，从架构层面强化叙事约束。我们以儒勒·凡尔纳的《海底两万里》为案例，通过多阶段评估验证系统性能：首先进行包含系统组件消融分析的实验室研究，随后开展为期5天的真实环境日志研究。实验表明，深度角色对齐流程使专用模型在角色特异性指标上超越GPT-4o，而连贯性与鲁棒性增强阶段在叙事连贯性与系统鲁棒性测试中接近完美表现。本研究提炼出人工智能叙事系统的实用设计准则：角色优先的自训练是构建可信度的基础，而显式故事时间约束对于维持连贯、抗干扰的移动端网页体验至关重要。