个人显著性：高亮是社交性的，但个性存在于选择中
Personal Salience: Highlighting Is Social, but Individuality Lives in Selection
AI总结：
通过共同阅读身份控制实验，发现高亮行为主要受群体影响（群体模型预测显著优于个人模型），但个人历史在选择已显著段落时表现出强预测力（差距+0.14），揭示个性更多体现在选择而非显著性上。
摘要：
社交高亮工具让用户标记对他们重要的段落。我们通过共同阅读身份控制（同一文档被多个用户高亮）来探究从这些自然痕迹中能恢复多少个体信息，该方法固定文档和主题，询问一个人的历史是否比另一个读者的历史更能预测其标记。我们区分了通用显著性（结构）、群体显著性（他人标记的内容）和个人显著性（个体残差）。首先，高亮是社交性的：你标记的句子被群体预测得远好于结构或个人模型，甚至一个估计良好的群体（信息特权基线，能看到同一文档上他人的标记）也胜过基于你其他文档历史构建的前沿LLM孪生模型；文档内的个人信号最多是低语（嵌入评分器上的自己与他人差距+0.017，虽小但显著）。其次，与此形成鲜明对比的是，个性存在于选择中：当问及哪些已显著的段落是你的时，你自己的历史是一个强且无泄漏的预测器（差距+0.14）。主题分解显示这主要是稳定的主题偏好：与主题匹配的同伴相比，它缩小约6-8倍，且薄残差无法与更细的主题区分。非显而易见的部分是不对称性：在相同评分器下，个人信号在显著性中比在选择中弱约6-8倍。方法上，朴素的历史条件评估存在泄漏（目标自己的标记在约42%的配对中进入档案，使个人得分最多增加+0.15 AP），且小群体夸大个性化；我们的结果无泄漏，使用密集群体和模型匹配控制。高亮携带真实的个人签名，但只是强共享签名上的薄层，更多地体现在一个人选择哪些显著段落而非什么显著上。
英文摘要：
Social highlighters let people mark passages that matter to them. We ask how much of an individual is recoverable from these naturalistic traces, using a co-readership identity control (the same document highlighted by many users) that holds document and topic fixed and asks whether a person's own history predicts their marks better than another reader's does. We separate generic salience (structure), crowd salience (what others marked), and personal salience (the individual residual). First, highlighting is social: which sentences you mark is predicted far better by the crowd than by structure or by a personal model, and even a well-estimated crowd, an information-privileged baseline that sees others' marks on the same document, beats a frontier LLM twin built from your other-document history; the within-document personal signal is at most a whisper (own-vs-other gap +0.017 by an embedding scorer, small but significant). Second, in sharp contrast, individuality lives in selection: asked which of the already-salient passages are yours, your own history is a strong, leakage-free predictor (gap +0.14). A topic decomposition shows this is largely stable thematic preference: it shrinks ~6-8x against a topically-matched peer, and a thin residual cannot be separated from finer topic. The non-obvious part is an asymmetry: under the same scorer the individual signal is ~6-8x weaker in salience than in selection. Methodologically, naive history-conditioning evaluations leak (the target's own marks enter the profile in ~42% of pairs, inflating personal scores by up to +0.15 AP) and small crowds overstate personalization; our results are leakage-free, use a dense crowd, and a model-matched control. Highlights carry a genuine individual signature, but a thin layer over a strong shared one, surfacing far more in which salient things a person selects than in what is salient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
