arXiv:2605.22769v1 Announce Type: new Abstract: Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact of pre-training dynamics on the acquisition of time-sensitive factual knowledge, focusing specifically on data ordering. Our main contributions are twofold. First, we introduce a comprehensive benchmark of over 7,000 temporally grounded questions and an evaluation protocol that enables analysis of whether models correctly associate
The rapid advancement and deployment of LLMs necessitate a deeper understanding of their fundamental limitations, particularly regarding temporal reasoning as they are integrated into more dynamic applications.
Improving LLM temporal grounding is critical for their reliability in real-world scenarios, influencing fields from finance to scientific research where up-to-date and contextually accurate information is paramount.
This research shifts the focus from simply pre-training on vast datasets to strategically organizing data to imbue LLMs with a dynamic understanding of time, potentially leading to more robust and less 'frozen' models.
- · AI researchers and developers
- · Companies building knowledge-intensive AI applications
- · Users of LLMs requiring up-to-date factual information
- · Developers of 'frozen' knowledge-based LLM applications
- · Organizations relying on static knowledge bases for critical decision making
More accurate and contextually aware LLMs will emerge, reducing the need for constant fine-tuning for temporal accuracy.
This improved temporal reasoning could accelerate the development of more sophisticated AI agents capable of operating in dynamic, real-time environments.
Enhanced temporal understanding in AI could lead to breakthroughs in areas like predictive analytics, historical analysis, and even scientific discovery by better connecting disparate temporal data points.
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