Chronocept: Instilling a Sense of Time in Machines

ACL ARR 2025 May Submission543 Authors

Abstract: Human cognition is deeply intertwined with a sense of time, known as \textit{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 This sense allows us to judge how long facts remain valid and when knowledge becomes outdated.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.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: calibration/uncertainty, explanation faithfulness, hierarchical & concept explanations, model editing, probing, knowledge tracing/discovering/inducing, temporal validity, time-aware NLP, regression-based evaluation

Contribution Types: Model analysis & interpretability, Reproduction study, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English

Submission Number: 543