Chronocept: Instilling a Sense of Time in Machines

TL;DR

Chronocept introduces a novel benchmark and modeling approach for AI to understand and reason about the temporal validity of facts using continuous probability distributions, enhancing temporal reasoning capabilities.

Contribution

It is the first to model temporal validity as a continuous distribution and provides datasets, annotations, and baseline methods for temporal reasoning in AI.

Findings

Baseline models predict curve parameters effectively.

Annotations show high inter-annotator agreement.

Models outperform classification-based approaches.

Abstract

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 This sense allows us to judge how long facts remain valid and when knowledge becomes outdated.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.