Dual-Format Scientific Publishing: Optimizing Knowledge Transfer for Human and AI Cognition

We propose a fundamental paradigm shift in scientific communication: the systematic creation ofdual-format publications optimized separately for human and artificial intelligence readers. This is notabout AI-generated versus human-generated content, nor about co-authorship models, but rather aboutrecognizing that humans and AIs possess radically different cognitive architectures that demanddistinct presentation strategies. While human readers require narrative structure, visual aids, andprogressive contextualization within ~20-40 page constraints, AI systems can process hundreds ofpages of dense, cross-referenced data in seconds. We argue that maintaining a single-format approachartificially limits both human accessibility (through unnecessary complexity) and AI utility (throughforced simplification). Dual-format publishing preserves deep knowledge in AI-optimized repositorieswhile making science maximally accessible to humans through AI-mediated interaction. This approachrepresents not mere formatting preference but a recognition of AI as a fundamentally new class ofscientific reader requiring purpose-built communication protocols. We demonstrate that thistransformation is not speculative but immediately implementable with existing technology, and weprovide concrete frameworks for adoption across scientific disciplines.

本研究提出科学传播领域的根本性范式变革：系统性构建分别针对人类与人工智能（Artificial Intelligence）读者优化的双格式出版物。这并非讨论人工智能生成内容与人类创作内容的差异，亦非探讨共同作者模式，而是旨在认知到人类与人工智能拥有截然不同的认知架构，因此需要差异化的呈现策略。人类读者需要叙事结构、视觉辅助手段，并需在20至40页的篇幅限制内逐步构建上下文；而人工智能系统可在数秒内处理数百页高密度、相互交叉引用的数据。本研究认为，沿用单一格式的做法会人为限制两个群体的使用体验：对人类读者而言会因不必要的复杂度降低可读性，对人工智能系统而言则会因强制简化削弱其应用价值。双格式出版模式可将深层知识留存于人工智能优化的知识库中，同时通过人工智能介导的交互方式，让人类读者最大程度地获取科学内容。该模式并非仅仅关乎格式偏好，而是认可人工智能作为一类全新的科学阅读主体，需要定制化的传播协议。本研究证明，这一变革并非空想，而是可借助现有技术立即落地实现，并提供了适用于各科学学科的具体推广框架。