TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in
  Real-World Scenarios
Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend
the real world. However, existing works neglect the real-world challenges for
temporal reasoning: (1) intensive temporal information, (2) fast-changing event
dynamics, and (3) complex temporal dependencies in social interactions. To
bridge this gap, we propose a multi-level benchmark TIME, designed for temporal
reasoning in real-world scenarios. TIME consists of 38,522 QA pairs, covering 3
levels with 11 fine-grained sub-tasks. This benchmark encompasses 3
sub-datasets reflecting different real-world challenges: TIME-Wiki, TIME-News,
and TIME-Dial. We conduct extensive experiments on reasoning models and
non-reasoning models. And we conducted an in-depth analysis of temporal
reasoning performance across diverse real-world scenarios and tasks, and
summarized the impact of test-time scaling on temporal reasoning capabilities.
Additionally, we release TIME-Lite, a human-annotated subset to foster future
research and standardized evaluation in temporal reasoning. The code is
available at https://github.com/sylvain-wei/TIME , and the dataset is available
at https://huggingface.co/datasets/SylvainWei/TIME .
683059e8e2f446ed653e8512
Shaohang Wei
claimed_verified
2025-05-28T19:14:47.601Z
67244a81aa8556c561925ab6
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/w-vZ0uwYACagrNq-H1oyO.jpeg
Shaohang Wei
user
SylvainWei
683059e8e2f446ed653e8513
Wei Li
683059e8e2f446ed653e8514
Feifan Song
claimed_verified
2025-05-26T08:14:11.572Z
6447ca6ca478b20f1755b294
/avatars/5049856b5ed1b74533fff902e14b4c7c.svg
Feifan Song
user
songff
683059e8e2f446ed653e8515
Wen Luo
683059e8e2f446ed653e8516
Tianyi Zhuang
683059e8e2f446ed653e8517
Haochen Tan
683059e8e2f446ed653e8518
Zhijiang Guo
683059e8e2f446ed653e8519
Houfeng Wang
TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in
  Real-World Scenarios
Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend
the real world. However, existing works neglect the real-world challenges for
temporal reasoning: (1) intensive temporal information, (2) fast-changing event
dynamics, and (3) complex temporal dependencies in social interactions. To
bridge this gap, we propose a multi-level benchmark TIME, designed for temporal
reasoning in real-world scenarios. TIME consists of 38,522 QA pairs, covering 3
levels with 11 fine-grained sub-tasks. This benchmark encompasses 3
sub-datasets reflecting different real-world challenges: TIME-Wiki, TIME-News,
and TIME-Dial. We conduct extensive experiments on reasoning models and
non-reasoning models. And we conducted an in-depth analysis of temporal
reasoning performance across diverse real-world scenarios and tasks, and
summarized the impact of test-time scaling on temporal reasoning capabilities.
Additionally, we release TIME-Lite, a human-annotated subset to foster future
research and standardized evaluation in temporal reasoning. The code is
available at https://github.com/sylvain-wei/TIME , and the dataset is available
at https://huggingface.co/datasets/SylvainWei/TIME .
A benchmark called TIME assesses temporal reasoning in LLMs across varied real-world challenges, including intensive temporal information, fast-changing event dynamics, and complex social interactions, and evaluates the impact of test-time scaling.
Temporal reasoning Large Language Models (LLMs) QA pairs benchmark TIME-Wiki TIME-News TIME-Dial reasoning models non-reasoning models TIME-Lite