title: TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in
    Real-World Scenarios
summary: 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 .
paper_authors._id: 683059e8e2f446ed653e8512
paper_authors.name: Shaohang Wei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T19:14:47.601Z
paper_authors._id: 67244a81aa8556c561925ab6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/w-vZ0uwYACagrNq-H1oyO.jpeg
paper_authors.fullname: Shaohang Wei
paper_authors.type: user
paper_authors.user: SylvainWei
paper_authors._id: 683059e8e2f446ed653e8513
paper_authors.name: Wei Li
paper_authors._id: 683059e8e2f446ed653e8514
paper_authors.name: Feifan Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:14:11.572Z
paper_authors._id: 6447ca6ca478b20f1755b294
paper_authors.avatarUrl: /avatars/5049856b5ed1b74533fff902e14b4c7c.svg
paper_authors.fullname: Feifan Song
paper_authors.type: user
paper_authors.user: songff
paper_authors._id: 683059e8e2f446ed653e8515
paper_authors.name: Wen Luo
paper_authors._id: 683059e8e2f446ed653e8516
paper_authors.name: Tianyi Zhuang
paper_authors._id: 683059e8e2f446ed653e8517
paper_authors.name: Haochen Tan
paper_authors._id: 683059e8e2f446ed653e8518
paper_authors.name: Zhijiang Guo
paper_authors._id: 683059e8e2f446ed653e8519
paper_authors.name: Houfeng Wang
paper_title: TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in
    Real-World Scenarios
paper_summary: 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 .
paper_ai_summary: 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.
paper_ai_keywords: Temporal reasoning Large Language Models (LLMs) QA pairs benchmark TIME-Wiki TIME-News TIME-Dial reasoning models non-reasoning models TIME-Lite