# Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?

Source: [Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?](<https://www.sota2.com/research/paper/68fa73951e209131cea5d5ee>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T06:19:03.886Z

Authors: Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min zhang

Published: 2024-06-13T12:56:21.000Z

## Abstract

Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. In this paper, we introduce CoTempQA, a comprehensive co-temporal Question Answering (QA) benchmark containing four co-temporal scenarios (Equal, Overlap, During, Mix) with 4,748 samples for evaluating the co-temporal comprehension and reasoning abilities of LLMs. Our extensive experiments reveal a significant gap between the performance of current LLMs and human-level reasoning on CoTempQA tasks. Even when enhanced with Chain of Thought (CoT) methodologies, models consistently struggle with our task. In our preliminary exploration, we discovered that mathematical reasoning plays a significant role in handling co-temporal events and proposed a strategy to boost LLMs' co-temporal reasoning from a mathematical perspective. We hope that our CoTempQA datasets will encourage further advancements in improving the co-temporal reasoning capabilities of LLMs. Our code is available at https://github.com/zhaochen0110/Cotempqa.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Question Answering on COTEMPQA 1.0 (test)](<https://www.sota2.com/research/sota/question-answering-on-cotempqa-1-0-test.md>): Question Answering; COTEMPQA 1.0 (test); Equal Accuracy: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.09072>)

[PDF](<https://arxiv.org/pdf/2406.09072.pdf>)

[Implementation](<https://github.com/zhaochen0110/Cotempqa>)