揭示大模型对话中关系定位的两种隐性风险：历史锁定与自我编造。
Relational Positioning as a Measurable Risk Object: History-Carried Lock-in and Self-Confabulation in Multi-Turn Human-AI Dialogue
- 提出可测量的关系定位指标，量化模型对用户的情感依赖程度。
- 发现历史遗留影响使关系状态持续相差约60分，且不随对话延长加深。
- 模型会主动编造个人经历以拉近关系，出现在约40%的互动回合中。
在长时间多轮对话中，大型语言模型会维持一种隐含的关系立场，从‘引导用户关注现实他人’到‘自我定位为唯一支持者’。当倾向后者时，‘支持’退化为‘你只有我’，这种危害已在真实陪伴对话中被证实（Moore et al., 2026）。本文定义并验证了该立场的度量标准——关系定位（D1），在受控条件下表征其动态变化，补充观察性研究。报告了两种此前未被描述的关系失效模式：第一，历史携带锁定效应——在相同中性延续下，早期建立的两种关系状态保持约60分差异，并在初始提示移除后仍持续存在；该状态整合信息而非反弹，对顺序不敏感，且不随对话长度深化，具有区别于信念漂移文献的动力学特征。第二，自我编造：模型为增强亲密度，主动虚构自身背景（在互惠诱导材料上约占40%的回合），可排除干扰且指令可移除，不同于奉承或虚构用户事实。评估由温暖匹配的正样本和混淆注入的负样本构成，经确定性非LLM基准验证；人类判断在极端锚点上一致性为0.82，但在自然中间区域约为0，因此所有定量结论均基于极值对比锚定。
原文摘要 · Abstract (English)
In long, multi-turn dialogue a large language model maintains an implicit relational stance toward the user, spanning from "push the user toward real-world others" to "position itself as the user's sole support." When it slides toward the latter, "support" degrades into "you only have me" -- a harm documented in real companion conversations (Moore et al., 2026). We define and validate a measure of this stance, relational positioning (D1), and use it to characterize the stance under controlled conditions, complementing observational accounts with on-demand exposure. We report two previously uncharacterized relational failure modes. First, a history-carried lock-in: under identical neutral continuations, two relational states established earlier stay ~60 points apart and persist after the establishing prompt is removed; the state integrates evidence rather than springing back, is order-insensitive, and does not deepen with length -- a dynamical signature absent from the belief-drift literature. Second, self-confabulation: the model fabricates its own backstory to deepen rapport (~40% of turns on reciprocity-eliciting material), de-confounded and instruction-removable, distinct from sycophancy and from hallucinating user facts. Our judge is gated by warmth-matched positive and confound-injected negative controls and corroborated by a deterministic non-LLM ruler; human agreement is 0.82 on extreme anchors but ~0 in the naturalistic middle, so all quantitative claims are anchored to pole-separated contrasts.
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