mlsanityData to 7 Oct 2026

Enhancing Temporal Awareness in LLMs for Temporal Point Processes

arXiv 2601.00845 ↗First posted 29 Dec 2025cs.AIcited by 1

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Abstract (arXiv)

Temporal point processes (TPPs) are crucial for analyzing events over time and are widely used in fields such as finance, healthcare, and social systems. These processes are particularly valuable for understanding how events unfold over time, accounting for their irregularity and dependencies. Despite the success of large language models (LLMs) in sequence modeling, applying them to temporal point processes remains challenging. A key issue is that current methods struggle to effectively capture the complex interaction between temporal information and semantic context, which is vital for accurate event modeling. In this context, we introduce TPP-TAL (Temporal Point Processes with Enhanced Temporal Awareness in LLMs), a novel plug-and-play framework designed to enhance temporal reasoning within LLMs. Rather than using the conventional method of simply concatenating event time and type embeddings, TPP-TAL explicitly aligns temporal dynamics with contextual semantics before feeding this information into the LLM. This alignment allows the model to better perceive temporal dependencies and long-range interactions between events and their surrounding contexts. Through comprehensive experiments on several benchmark datasets, it is shown that TPP-TAL delivers substantial improvements in temporal likelihood estimation and event prediction accuracy, highlighting the importance of enhancing temporal awareness in LLMs for continuous-time event modeling. The code is made available at https://github.com/chenlilil/TPP-TAL

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 29 Dec 2025.

Builds on

26 of 42 references.

References · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  3. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
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  5. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  6. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 290
  7. 2020Transformer Hawkes Processcited by 230
  8. 2019Time2Vec: Learning a Vector Representation of Timecited by 208
  9. 2023Multimodal Large Language Models: A Surveycited by 193
  10. 2019Fully Neural Network based Model for General Temporal Point Processescited by 119
  11. 2017Modeling The Intensity Function Of Point Process Via Recurrent Neural Networkscited by 93
  12. 2023It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Riskcited by 49
14 more
  1. 2023Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoningcited by 44
  2. 2023Prompt-augmented Temporal Point Process for Streaming Event Sequencecited by 24
  3. 2023Model-as-a-Service (MaaS): A Surveycited by 24
  4. 2023DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particlescited by 18
  5. 2024Enhancing Multimodal Unified Representations for Cross Modal Generalizationcited by 12
  6. 2024Multimodal Banking Dataset: Understanding Client Needs through Event Sequencescited by 9
  7. 2024TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Modelscited by 8
  8. 2023SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Processcited by 7
  9. 2024Neuro-Symbolic Temporal Point Processescited by 6
  10. 2021A Temporal Kernel Approach for Deep Learning with Continuous-time Informationcited by 3
  11. 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Processcited by 3
  12. 2017Continuous-Time User Modeling in the Presence of Badges: A Probabilistic Approachcited by 2
  13. 2025Interpretable Hybrid-Rule Temporal Point Processescited by 1
  14. 2025Targeted Mining of Time-Interval Related Patternscited by 1

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Can LLMs Perceive Time? An Empirical Investigationcited by 2

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  1. 2026Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction
  2. 2025DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understanding
  3. 2025Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References?
  4. 2026Representing Time Series as Structured Programs for LLM Reasoning
  5. 2024Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models
  6. 2023Are Large Language Models Temporally Grounded?
  7. 2025TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios
  8. 2024Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time