As large language models (LLMs) transition from research prototypes to real-world systems, customization has emerged as a central bottleneck. While text prompts can already customize LLM behavior, we argue that text-only prompting does not constitute a suitable control interface for scalable, stable, and inference-only customization. This position paper argues that model providers should expose \emph{vector prompt inputs} as part of the public interface for customizing LLMs. We support this position with diagnostic evidence showing that vector prompt tuning continues to improve with increasing supervision whereas text-based prompt optimization saturates early, and that vector prompts exhibit dense, global attention patterns indicative of a distinct control mechanism. We further discuss why inference-only customization is increasingly important under realistic deployment constraints, and why exposing vector prompts need not fundamentally increase model leakage risk under a standard black-box threat model. We conclude with a call to action for the community to rethink prompt interfaces as a core component of LLM customization.

翻译：随着大型语言模型从研究原型向实际系统过渡，自定义已成为核心瓶颈。虽然文本提示已能定制LLM行为，但我们认为纯文本提示并不构成可扩展、稳定且仅需推理的自定义控制接口。本立场论文主张，模型提供商应将向量提示输入作为公开接口的一部分，以支持LLM定制。我们通过诊断性证据支持这一观点：向量提示调优会随监督信号增强而持续改进，而基于文本的提示优化则过早饱和；且向量提示表现出密集的全局注意力模式，表明其具有独特的控制机制。我们进一步探讨了在实际部署约束下仅需推理的自定义为何日益重要，以及为何在标准黑盒威胁模型下公开向量提示不会从根本上增加模型泄露风险。最后，我们呼吁学界重新思考提示接口作为LLM定制核心组件的定位。