Personal Baseline is more suitable for a long-term Human Model than population averages. Individual behavior should be modeled relative to a person's own historical baseline, not a generic mean.
个人基线比群体平均值更适合长期 Human Model。个体行为应相对于其自身历史基线建模，而非通用均值。
The speed at which an AI adapts to a user may affect trust formation, independent of absolute answer quality.
AI 适应用户的速度可能影响信任形成，与绝对回答质量无关。
A Human Model should jointly model Trait + State, not personality alone. Static trait profiles miss context-driven behavioral variation.
Human Model 应同时建模 Trait（特质）+ State（状态），而非仅人格。静态人格画像会遗漏由情境驱动的行为变化。
Relationship prediction requires a Dyadic Model, not two independent personality profiles. Compatibility emerges from interaction, not from summing two individuals.
关系预测需要 Dyadic（二元）模型，而非两个独立的人格画像。契合度产生于互动，而非两个个体的简单相加。
The core of AI personalization is not 'knowing more' but 'changing interaction strategy' in response to the user.
AI 个性化的核心不是“知道更多”，而是“根据用户改变交互策略”。
Long-term human-AI relationship quality depends on Interaction Adaptation, not single-turn answer quality.
人与 AI 的长期关系质量取决于 Interaction Adaptation（交互适应），而非单次回答质量。
What matters is not Absolute Pause but Personal Pause Baseline. Individual Pause Deviation = current pause − personal historical baseline. The same 2s pause means different things for a 0.5s vs 1.5s baseline person.
关键不是绝对停顿（Absolute Pause），而是个人停顿基线（Personal Pause Baseline）。个人停顿偏差 = 当前停顿 − 个人历史基线。同样的 2 秒停顿，对基线 0.5 秒与 1.5 秒的人意义完全不同。
In long-term human-AI interaction, measured adaptation speed — the turn at which an AI first changes its interaction strategy to match a user's revealed preferences — predicts sustained trust and retention better than first-session answer quality. We predict users whose first strategy-change occurs within the first 3 turns show materially higher 30-day retention.
在长期的人机交互中，可测量的适应速度——AI 首次改变交互策略以匹配用户显现偏好的轮次——比首轮回答质量更能预测持续的信任与留存。我们预测：首次策略改变发生在前 3 轮内的用户，其 30 天留存显著更高。
Dyadic Communication Rhythm — the compatibility of two individuals' personal pause/turn-taking baselines (not absolute pause length) predicts felt rapport and willingness to re-engage, independent of topic content. We predict pairs whose personal inter-turn gap baselines are closely matched (deviation within a tolerance band) report higher relationship satisfaction than pairs with shared interests but mismatched rhythm. This extends KH-007 (Personal Pause Baseline) from the individual to the dyad and operationalizes KH-004 (Dyadic Model) at the behavioral-timing level.
二元沟通节奏——两个人个人停顿/话轮基线的兼容性（而非绝对停顿长度）预测感受到的融洽度与再次互动意愿，与话题内容无关。我们预测：个人话轮基线相近（偏差在容差带内）的两人，其关系满意度高于兴趣相投但节奏错配的两人。这把 KH-007（个人停顿基线）从个体层面扩展到二元层面，并在行为时序层面将 KH-004（二元模型）操作化。
A portable, user-owned Human Model — a structured, evidenced 'Human Passport' carrying a person's Trait+State baseline (KH-003) and personal preferences — that travels with the individual across AI systems improves cross-app personalization and human-AI trust more than per-app isolated memory, because it preserves Personal Baseline (KH-001) and Trait+State continuity that isolated systems keep re-deriving from scratch. We predict users who carry an established model into a new AI context reach personalized, trusted interaction in fewer turns than cold-start users, and report higher calibrated trust.
一个可携带、用户拥有的 Human Model——即携带个人 Trait+State 基线（KH-003）与个人偏好的结构化、有证据支撑的“人类护照”——随个人跨越不同 AI 系统，比每个应用各自孤立的记忆更能提升跨应用个性化与人对 AI 的信任，因为它保留了孤立系统不断从零重新推导的“个人基线”（KH-001）与“Trait+State”连续性。我们预测：把已有模型带入新 AI 场景的用户，比冷启动用户用更少轮次达到个性化、可信的互动，并报告更高的校准信任。
In decoding a person's state (arousal, valence, stress) from voice, the acoustic signal must be measured relative to that person's own Prosodic Baseline — their typical pitch range, speaking rate, intensity contour, and rhythm — not a population norm. The same absolute pitch-rise or speaking-rate drop means different internal states for a naturally high-pitched vs low-pitched person, or a fast vs slow talker. We predict a personal-prosodic-baseline model beats a population-norm model on state-classification accuracy and on felt 'being understood', and that mismatched prosodic baselines between two people predict lower rapport (extending KH-009's rhythm-mismatch logic from pause timing into the acoustic domain). This operationalizes KH-003 (Trait+State) at the prosodic level and extends KH-001 (Personal Baseline) and KH-007 (Personal Pause Baseline) from timing into voice.
在从声音解码一个人的状态（唤醒度、效价、压力）时，声学信号必须相对于该人自身的“韵律基线”——其典型音高范围、语速、强度轮廓与节奏——而非群体常模来测量。同样的绝对音高升高或语速下降，对天生高音与低音的人、快语速与慢语速的人，意味着不同的内部状态。我们预测：个人韵律基线模型在状态分类准确率与“被理解感”上优于群体常模模型，且两人韵律基线错配会预测更低的融洽度（把 KH-009 的节奏错配逻辑从停顿时序扩展到声学域）。这在韵律层面将 KH-003（Trait+State）操作化，并把 KH-001（个人基线）与 KH-007（个人停顿基线）从时序扩展到声音。
Dyadic Synchrony Compatibility — a person's felt rapport and sustainable compatibility with another is predictable from the real-time coordination between them (movement, pause/turn rhythm, prosody matching, physiological coupling), beyond shared traits or interests. This coordination should be modeled relative to each person's own baseline (extending KH-001, KH-007, KH-009), so that 'matched baseline + matched rhythm' predicts durable rapport better than trait similarity alone. We predict a matching system that aligns two users' communication rhythms and state baselines outperforms one built on static trait/interest similarity on re-engagement and reported rapport. This operationalizes KH-004 (Dyadic Model) at the behavioral-coordination level.
二元同步兼容性——一个人对他人的融洽感与可持续兼容性，可由两人之间的实时协调（动作、停顿/话轮节奏、韵律匹配、生理耦合）预测，超出共享特质或兴趣的范畴。这种协调应相对于每个人自身基线建模（扩展 KH-001、KH-007、KH-009），从而“匹配基线 + 匹配节奏”比单纯特质相似更能预测持久融洽。我们预测：让两位用户的沟通节奏与状态基线对齐的匹配系统，在再互动意愿与自报融洽度上，优于基于静态特质/兴趣相似的系统。这把 KH-004（二元模型）在行为协调层面操作化。