Skip to content
#generative ai Open access

鬼债范式V13.5:下一代幻想创作公理范式——人机共演化生成式叙事元理论

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

鬼债范式V13.5:下一代幻想创作公理范式——人机共演化生成式叙事元理论 Ghost‑Debt Paradigm V13.5: A Next‑Generation Axiomatic Paradigm for Fantasy Creation — Meta‑Theory of Human‑AI Co‑Evolutionary Generative Narrative 中文摘要 面向幻想文艺工业化创作需求,现有叙事方法论缺少从底层世界观、力量体系、叙事驱动力到人文主题与人机协同的全链路一体化框架,幻想创作正经历由作者中心再现范式向人机共演化生成范式的代际转型。本文提出鬼债范式与MCCE三公理体系,经过多轮迭代与六次范式升级,构建一套AI可执行的幻想创作公理化元理论。该体系采用硬核公理层、血肉工程层、纯艺术创作层三层功能分工架构,由世界观、力量、叙事等四引擎协同运行;通过吸纳桑德森魔法体系、麦基‑坎贝尔冲突本体论、世界层系演化史观、分布式网文叙事、量子叙事本体等前沿理论,新增债量全局守恒律、叙事价值本体律、LBPA‑T时序推演算法、多节点并行债压传播协议与M1‑Quantum量子观测公理,解决力量通胀、主题‑结构割裂、静态债网局限、群像叙事适配不足等问题。体系定义DU苦痛单位作为统一度量,建立S值叙事健康度体检机制,配套完整自检清单与AI协作协议。经《锤声》生成实验、《诡秘之主》反向推演、《百年孤独》压力测试证实该范式在故事生成、文本解析、跨类型泛化层面具备有效性。本范式属于启发式叙事工程提案,部分时序与分布式模块尚待长篇叙事验证,不覆盖纯文学、实验文学,不替代创作者的审美与价值裁决,旨在为人机协同幻想IP创作提供可证伪、可推演的公理基座。 中文关键词:幻想创作方法论;鬼债范式;MCCE三公理;人机共演化生成叙事;DU统一度量;叙事工程;范式跃迁;量子叙事本体 English Abstract Against the industrialized creation of fantasy literature and art, existing narrative methodologies lack an end‑to‑end integrated framework covering fundamental worldbuilding, power‑system design, narrative driving‑force, humanistic themes and human‑AI collaboration. Fantasy creation is undergoing a generational shift from author‑centered representational paradigm toward human‑AI co‑evolutionary generative paradigm. This paper proposes the Ghost‑Debt Paradigm and MCCE Three‑Axiom System, an AI‑executable axiomatic meta‑theory for fantasy creation developed through multiple iterations and six paradigm‑level upgrades. Adopting a three‑tier functional architecture including hard‑core axiom layer, humanistic‑engineering layer and pure‑art creation layer, the framework operates collaboratively with four engines: worldbuilding engine, power‑system engine, narrative engine and ontology engine. By integrating cutting‑edge theories such as Sanderson’s magic system, McKee‑Campbell conflict ontology, world‑system evolutionary historiography, distributed web‑novel narrative and quantum‑narrative ontology, this work introduces the global debt‑quantity conservation law, narrative value ontology law, LBPA‑T temporal deduction algorithm, multi‑node parallel debt‑pressure propagation protocol and M1‑Quantum observational axiom, mitigating pain‑points including power inflation, disjunction between theme and structure, static debt‑network limitations and poor compatibility with ensemble‑cast narratives. The system defines Distress Unit (DU) as a unified metric and establishes the S‑score health‑inspection mechanism for narrative quality, alongside complete self‑check checklists and human‑AI collaboration protocols. Validated by the Hammer Sound generative experiment, reverse deduction on Lord of Mysteries and stress test upon One Hundred Years of Solitude, the paradigm demonstrates feasibility in story generation, textual interpretation and cross‑genre generalization. As a heuristic narrative‑engineering proposal, several temporal‑evolution and distributed‑narrative modules await long‑form verification. It excludes pure literature and experimental writing and cannot replace creators’ aesthetic judgement and value adjudication. It intends to deliver a falsifiable, inferable axiomatic foundation for human‑AI co‑creative fantasy IP development. English Keywords: fantasy creation methodology; Ghost‑Debt Paradigm; MCCE Three‑Axiom System; human‑AI co‑evolutionary generative narrative; DU (Distress Unit) unified metric; narrative engineering; paradigm shift; quantum‑narrative ontology 预印本,没有同行评审。 文件列表:1.【主要】鬼债范式_V7.2-Final基于公理化体系的幻想世界观生成模型研究.PDF-主预打印原稿(默认预览)2.[Addlementary-1]鬼债范式_终极合一方法论_纯引擎V2.1_四皮肤验证版.PDF-纯引擎补充文档3.[Addlementary-2]鬼债尽一身轻_设定集_完整版.PDF-完整世界建筑设置集4.[Addlementary-3]鬼债尽一身轻_设定建设手册_v7.1-Final.PDF-归档历史版本V7.1-最终版5.[Addlementary-4]小说动漫标准化体系基础原创.pdf-用于构建世界的标准化思维导图模板

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.