Papers with temporal understanding
ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) still face significant challenges in reasoning and arithmetic. |
| Approach: | They propose a new benchmark to evaluate LLMs' temporal understanding that includes 16 tasks identifying the Allen relation between two temporal events and temporal arithmetic. |
| Outcome: | The proposed model handles Allen relations, even symmetrical ones, quite differently. |
TimeRes: A Turkish Benchmark For Evaluating Temporal Understanding of Large Language Models (2026.eacl-srw)
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| Challenge: | Existing benchmarks focus on English and underexplore how linguistic structure contributes to temporal meaning. |
| Approach: | They propose a Turkish benchmark to evaluate temporal understanding of Large Language Models (LLMs) their benchmark examines Reichenbach’s temporal points and reported speech through date arithmetic . |
| Outcome: | The proposed model fails to resolve reported speech and fails to generalize across word order variations. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)
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Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
| Challenge: | Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks . |
| Approach: | They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps. |
| Outcome: | The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios. |