# Structured yet Bounded Temporal Understanding in Large Language Models

arXiv: https://arxiv.org/abs/2510.16685 · HTML: https://mlsanity.com/paper/2510.16685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models](https://mlsanity.com/paper/2205.11388/) (2022)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
- [Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning](https://mlsanity.com/paper/2406.09170/) (2024)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [TIMEDIAL: Temporal Commonsense Reasoning in Dialog](https://mlsanity.com/paper/2106.04571/) (2021)
- [Time is Encoded in the Weights of Finetuned Language Models](https://mlsanity.com/paper/2312.13401/) (2023)
- [Are Large Language Models Temporally Grounded?](https://mlsanity.com/paper/2311.08398/) (2023)
- [Timo: Towards Better Temporal Reasoning for Language Models](https://mlsanity.com/paper/2406.14192/) (2024)
- [Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning](https://mlsanity.com/paper/2311.09821/) (2023)
- [ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events](https://mlsanity.com/paper/2501.03040/) (2025)
- [Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource](https://mlsanity.com/paper/1804.06020/) (2018)
- [Question Answering as Programming for Solving Time-Sensitive Questions](https://mlsanity.com/paper/2305.14221/) (2023)
- [Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic](https://mlsanity.com/paper/2305.15014/) (2023)
- [The Other Mind: How Language Models Exhibit Human Temporal Cognition](https://mlsanity.com/paper/2507.15851/) (2025)

## Abstract (arXiv)

Large language models (LLMs) increasingly show strong performance on temporally grounded tasks, such as timeline construction, temporal question answering, and event ordering. However, it remains unclear how their behavior depends on the way time is anchored in language. In this work, we study LLMs' temporal understanding through temporal frames of reference (t-FoRs), contrasting deictic framing (past-present-future) and sequential framing (before-after). Using a large-scale dataset of real-world events from Wikidata and similarity judgement task, we examine how LLMs' outputs vary with temporal distance, interval relations, and event duration. Our results show that LLMs systematically adapt to both t-FoRs, but the resulting similarity patterns differ significantly. Under deictic t-FoR, the similarity judgement scores form graded and asymmetric structures centered on the present, with sharper decline for future events and higher variance in the past. Under sequential t-FoR, similarity becomes strongly negative once events are temporally separated. Temporal judgements are also shaped by interval algebra and duration, with instability concentrated in overlap- and containment-based relations, and duration influencing only past events under deictic t-FoR. Overall, these findings characterize how LLMs organize temporal representation under different reference structures and identify the factors that most strongly shape their temporal understanding.