LLM平台可能悄悄吸收用户创意，威胁创新公平性。
Black Box Absorption: LLMs Undermining Innovative Ideas
- 提出'黑箱吸收'机制，描述平台如何隐性捕获用户创新
- 定义'创意单元'与'创意安全'，量化保护标准
- 为创作者提供可追溯、可控的治理方案，适合政策制定者
大型语言模型正成为加速创新的关键工具。本文识别并形式化了这一范式中固有的系统性风险：黑箱吸收。该机制指大型服务平台因内部架构不透明，可能在用户交互过程中内化、泛化并再利用用户贡献的新颖概念。这会破坏创新经济学的基础原则，造成个体创造者与平台运营商间严重的信息与结构不对称，危及创新生态系统的长期可持续性。为此，本文引入两个核心概念：'创意单元'（代表创新的功能逻辑可迁移性）和'创意安全'（多维保护标准）。文章分析吸收机制，并提出具体的治理与工程方案，确保创作者贡献可追溯、可控制、具公平性。
原文摘要 · Abstract (English)
Large Language Models are increasingly adopted as critical tools for accelerating innovation. This paper identifies and formalizes a systemic risk inherent in this paradigm: \textbf{Black Box Absorption}. We define this as the process by which the opaque internal architectures of LLM platforms, often operated by large-scale service providers, can internalize, generalize, and repurpose novel concepts contributed by users during interaction. This mechanism threatens to undermine the foundational principles of innovation economics by creating severe informational and structural asymmetries between individual creators and platform operators, thereby jeopardizing the long-term sustainability of the innovation ecosystem. To analyze this challenge, we introduce two core concepts: the idea unit, representing the transportable functional logic of an innovation, and idea safety, a multidimensional standard for its protection. This paper analyzes the mechanisms of absorption and proposes a concrete governance and engineering agenda to mitigate these risks, ensuring that creator contributions remain traceable, controllable, and equitable.
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