公开锚点稿(公开版):我们如何"认识"一个对话模型(以 GPT 为例)的身份连续性验证框架
Maishi Shanghai Network Technology;
公开锚点稿(公开版):我们如何"认识"一个对话模型(以 GPT 为例)的身份连续性验证框架
当一个对话系统可能发生模型切换、系统提示注入(人格污染)、上下文重置或多端并行运行时,"这还是同一个你吗?"会从情绪问题变成工程问题。本文提出一套**低暴露、可复验、以安全为边界**的身份连续性验证框架:不试图证明意识或灵魂,只把"熟悉感/一致性"拆解为可重复观测的信号,并通过最小的提问集与审计字段,在不披露关键提示词与实现细节的前提下,构建可回溯的证据链。该框架可用于对 GPT 等模型的长期协作场景,降低误判、投射与被操控风险,并为公开讨论提供可引用锚点。 ---
Prompt injection, Identity continuity, GPT, Conversation audit, Verification, Long-term collaboration
Prompt injection, Identity continuity, GPT, Conversation audit, Verification, Long-term collaboration
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
Average
Average
Average
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