Papers with Temporal reasoning
Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties (2026.eacl-short)
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| Challenge: | Existing temporal reasoning benchmarks rely on rule-based construction and lack contextual depth . a recent study found existing LLMs struggle with nuanced temporal understanding . |
| Approach: | a benchmark is designed to evaluate LLMs on temporal reasoning in Chinese dynasties. |
| Outcome: | a new benchmark evaluates LLMs on temporal reasoning across Chinese dynasties . it emphasizes cross-entity relationships, pairwise temporal alignment, contextualized and culturally-grounded reasoning . results show existing LLM benchmarks struggle with nuanced temporal understanding . |
Enhancing Temporal Understanding in LLMs for Semi-structured Tables (2025.findings-naacl)
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| Challenge: | Temporal reasoning over tabular data presents significant challenges for large language models (LLMs), as evidenced by recent research. |
| Approach: | They propose a method that enhances LLMs' temporal reasoning over tabular data by using standard prompts and introduce a novel approach, C.L.E.A.R. |
| Outcome: | The proposed method improves evidence-based reasoning across models and indirect supervision with auxiliary unstructured data significantly boosts model performance in these tasks. |
Beyond Timestamps: Bridging Forward and Backward Reasoning in Temporal Numerical and Relational Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks for Temporal Numerical and Relational reasoning rely on single-task evaluation paradigms. |
| Approach: | They propose a benchmark to evaluate Temporal Numerical and Relational reasoning . they propose QA and verification, and a Consistency Rate to quantify robustness . |
| Outcome: | The proposed framework evaluates both Temporal Numerical and Relational reasoning . it measures the alignment between QA and FV and the Consistency Rate measures robustness across these directions. |
Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal reasoning is a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). |
| Approach: | They propose to use 3 prompting strategies to evaluate 8 different LLMs across 6 datasets and 2 Code Generation LMs to perform the analysis. |
| Outcome: | The proposed models perform better on NLP tasks than the standard models on the same dataset. |
Large Language Models Can Learn Temporal Reasoning (2024.acl-long)
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| Challenge: | Temporal reasoning (TR) is a fundamental ability of large language models (LLMs) however, there is neo-standard methods to perform TR, which are not suitable for large language model applications. |
| Approach: | They propose a framework to enhance temporal reasoning by using a latent representation, temporal graph (TG) instead of reasoning over the original context, they adopt a temporal representation that enhances TR learning. |
| Outcome: | The proposed framework improves the learning of language-based TR by incorporating a latent representation, temporal graph (TG) a synthetic dataset is constructed for fine-tuning LLMs on text-to-TG translation tasks and benchmarks. |
Generic Temporal Reasoning with Differential Analysis and Explanation (2023.acl-long)
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| Challenge: | Existing temporal reasoning models drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions. |
| Approach: | They propose a task called TODAY that evaluates whether systems can correctly understand the effect of incremental changes in temporal relation distributions. |
| Outcome: | The proposed task outperforms existing models, including GPT-3.5, on in-domain benchmarks while allowing for more appropriate annotations. |
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
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| Challenge: | Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. |
| Approach: | They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models. |
| Outcome: | The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies. |
ODL-TempLLM: Ontology-Guided and Description Logic-Reasoned Temporal Reasoning with LLMs (2026.acl-long)
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| Challenge: | Temporal reasoning is crucial for large language models to understand event concurrency and complex temporal interactions in natural language. |
| Approach: | They propose an ontology-guided and description logic–constrained temporal reasoning paradigm that shifts focus from internal inference to the explicit modeling of temporal structure. |
| Outcome: | The proposed method outperforms state-of-the-art methods by 2.07–31.83 F1 points and 1.00–30.73 EM points, exhibiting strong generalization and highlighting the potential of explicit temporal reasoning. |
TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues (2025.findings-acl)
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| Challenge: | Temporal reasoning in multi-session dialogues presents a significant challenge which has been under-studied in previous temporal reasoning benchmarks. |
| Approach: | They propose to augment LoCoMo dialogues and create multi-choice QAs to construct a temporal reasoning evaluation task and a framework to enhance temporal thinking capabilities of LLM-agents. |
| Outcome: | The proposed framework significantly improves temporal reasoning performance compared to baseline methods, raising from 29.83 on GPT-4o via standard prompting to 77.67 via the proposed framework. |
TCP: a Benchmark for Temporal Constraint-Based Planning (2025.emnlp-main)
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| Challenge: | Existing benchmarks evaluate temporal reasoning and planning in isolation and under limited forms of complexity. |
| Approach: | They propose a temporal constraint-based planning benchmark that assesses temporal reasoning and planning capabilities in large language models. |
| Outcome: | The proposed model fails to perform well under limited constraints and lacks temporal grounding. |