Chronocept：向机器灌输时间感
Chronocept: Instilling a Sense of Time in Machines
AI总结：
本文提出 Chronocept 基准，用偏态正态连续概率分布建模事实的时间有效性，并通过预测曲线参数提升 AI 的时间推理能力。
摘要：
人类认知与被称为 Chronoception（时间知觉）的时间感深度交织。这种感觉使我们能够判断事实保持有效的时长，以及知识何时过时。尽管在视觉、语言和运动控制方面取得了进展，AI 仍难以对时间有效性进行推理。我们推出 Chronocept，这是首个将时间有效性建模为随时间变化的连续概率分布的基准。Chronocept 使用沿语义分解的时间轴拟合的偏态正态曲线，捕捉信息出现、衰减和相关性峰值的细微模式。它包含两个数据集：Benchmark I（原子事实）和 Benchmark II（多句段落）。标注显示出很高的标注者间一致性（84% 和 89%）。我们的基线预测曲线参数——位置、尺度和偏度——实现可解释、可泛化的学习，并优于基于分类的方法。Chronocept 填补了 AI 时间推理中的一个基础性空白，支持知识接地、事实核查、检索增强生成（RAG）和主动智能体等应用。代码和数据公开发布。
英文摘要：
Human cognition is deeply intertwined with a sense of time, known as Chronoception. This sense allows us to judge how long facts remain valid and when knowledge becomes outdated. Despite progress in vision, language, and motor control, AI still struggles to reason about temporal validity. We introduce Chronocept, the first benchmark to model temporal validity as a continuous probability distribution over time. Using skew-normal curves fitted along semantically decomposed temporal axes, Chronocept captures nuanced patterns of emergence, decay, and peak relevance. It includes two datasets: Benchmark I (atomic facts) and Benchmark II (multi-sentence passages). Annotations show strong inter-annotator agreement (84% and 89%). Our baselines predict curve parameters - location, scale, and skewness - enabling interpretable, generalizable learning and outperforming classification-based approaches. Chronocept fills a foundational gap in AI's temporal reasoning, supporting applications in knowledge grounding, fact-checking, retrieval-augmented generation (RAG), and proactive agents. Code and data are publicly available.
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。
