⏳ TIME

[NeurIPS'25 Spotlight] TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios

🎉🎉 Congratulations! This paper has been accepted as NeurIPS 2025 Spotlight 🌟🔥 at D&B track.

🌟 If you found this work helpful, please consider giving us a ⭐ on GitHub!

📋 Project Information

Authors: Shaohang Wei, Wei Li, Feifan Song, Wen Luo, Tianyi Zhuang, Haochen Tan, Zhijiang Guo, Houfeng Wang
Affiliation: Peking University, Noah's Ark Lab
Contact: shaohang@stu.pku.edu.cn

📖 Abstract

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:

• Intensive temporal information
• Fast-changing event dynamics
• 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 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.

🚀 Get Started

📥 Step 1: Install Dependencies

# Install git-lfs
pip install git-lfs

📊 Step 2: Download Dataset

We provide two datasets. Choose according to your needs:

⚠️ Option 1: Complete TIME Dataset (Large dataset - may be too large for quick evaluation)

# Navigate to the working directory and download the benchmark dataset TIME
chmod +x scripts/download_data_time.sh

# Download the data
./scripts/download_data_time.sh

✅ Option 2: TIME-Lite Dataset (Recommended - High-quality subset)

# Navigate to the working directory and download the benchmark dataset TIME-Lite
chmod +x scripts/download_data_time_lite.sh

# Download the data
./scripts/download_data_time_lite.sh

🔧 Step 3: Install Evaluation Dependencies

pip install -r evaluation/requirements.txt

▶️ Step 4: Run Evaluation

Option A: Evaluate TIME dataset

./scripts/eval_time.sh

Option B: Evaluate TIME-Lite dataset (Recommended)

./scripts/eval_timelite.sh

🧠 Construction Pipeline

📊 Data Quantity

📈 Dataset Statistics:

• TIME: 38,522 QA pairs (Complete benchmark)
• TIME-Lite: 943 QA pairs (High-quality subset)

Here is a detailed breakdown of the dataset statistics:

Dataset	All Tasks	Ext.	Loc.	Comp.	D.C.	O.C.	E.R.	O.R.	R.R.	C.T.	T.L.	C.F.
TIME	38522	1480	3546	3376	3401	3549	3537	3538	3537	3513	5508	3537
TIME-Wiki	13848	1261	1299	1126	1151	1299	1287	1288	1287	1263	1300	1287
TIME-News	19958	0	1800	1800	1800	1800	1800	1800	1800	1800	3758	1800
TIME-Dial	4716	219	447	450	450	450	450	450	450	450	450	450
TIME-Lite	943	60	90	78	86	90	90	90	90	90	89	90
TIME-Lite-Wiki	322	30	30	24	28	30	30	30	30	30	30	30
TIME-Lite-News	299	0	30	30	30	30	30	30	30	30	29	30
TIME-Lite-Dial	322	30	30	24	28	30	30	30	30	30	30	30

Task abbreviations: Ext. (Extract), Loc. (Localization), Comp. (Computation), D.C. (Duration Compare), O.C. (Order Compare); E.R. (Explicit Reasoning), O.R. (Order Reasoning), R.R. (Relative Reasoning); C.T. (Co-temporality), T.L. (Timeline), C.F. (Counterfactual).

💪🏻 Evaluation Results

📊 TIME-Lite Results Radar Charts

Here are the detailed evaluation results for the TIME-Lite dataset on different sub-datasets:

🗄️ TIME-Lite-Wiki

📰 TIME-Lite-News

💬 TIME-Lite-Dial

💬 Citation

If you find our work interesting and meaningful, welcome to star this repo, give an upvote to our HF repo TIME and cite our paper as follows.

@article{wei2025time,
  title={TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios},
  author={Wei, Shaohang and Li, Wei and Song, Feifan and Luo, Wen and Zhuang, Tianyi and Tan, Haochen and Guo, Zhijiang and Wang, Houfeng},
  journal={arXiv preprint arXiv:2505.12891},
  year={2025}
}

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