STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing models focus on predictive accuracy over reasoning, a gap exists . time series data are ubiquitous in real-world systems and exhibit complex spatio-temporal structures. |
| Approach: | They propose a time series reasoning model that integrates time series, graph structure, and text for explicit reasoning. |
| Outcome: | The proposed model achieves average accuracy gains between 17% and 135% at 0.004x the cost of proprietary models and generalizes robustly to real-world data. |
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| Challenge: | Existing studies on temporal reasoning models neglect the explainable reasoning processes underlying the results. |
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STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)
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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan
| Challenge: | Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world. |
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Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models (2025.acl-long)
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| Challenge: | Large Language Models struggle with temporal reasoning, which requires processing time-related information such as event sequencing, durations, and inter-temporal relationships. |
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| Challenge: | Existing LLM-based recommenders lack explicit modeling of geographic signals . without explicit modeling geographic signals, recommenders struggle to capture core mobility patterns . |
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Temporal reasoning for timeline summarisation in social media (2025.acl-long)
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| Challenge: | Existing temporal reasoning datasets focus on pair-wise event relationships. |
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TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting (2021.emnlp-main)
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| Challenge: | Existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. |
| Approach: | They propose a time-shaped reward method that captures historical knowledge graph snapshots and a new representation method for unseen entities to improve the inductive inference ability of the model. |
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Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)
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Tianle Chen, Pengyu Cheng, Qiyuan Zhu, Jiacheng Wang, Bei Liu, Hao Gu, Ruijie Shen, Xiaofeng Hou, Sirui Han, Jiacheng Liu
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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. |
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LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics (2026.findings-acl)
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| Challenge: | Current research hinders the development of unified Time Series Reasoning Models (TSRMs) time series data are a fundamental modality for capturing the temporal dynamics of complex systems. |
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Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models (2023.acl-long)
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| Challenge: | Recent time-dependent question answering datasets tend to be biased in either their coverage of time spans or question types. |
| Approach: | They propose a temporal reasoning framework based on temporal span extraction and time-sensitive reinforcement learning to improve the temporal ability of large language models. |
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