# Enhancing Temporal Awareness in LLMs for Temporal Point Processes

arXiv: https://arxiv.org/abs/2601.00845 · HTML: https://mlsanity.com/paper/2601.00845/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2311.13165/) (2023)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks](https://mlsanity.com/paper/1705.08982/) (2017)
- [It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk](https://mlsanity.com/paper/2310.01387/) (2023)
- [Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning](https://mlsanity.com/paper/2305.16646/) (2023)
- [Model-as-a-Service (MaaS): A Survey](https://mlsanity.com/paper/2311.05804/) (2023)
- [Prompt-augmented Temporal Point Process for Streaming Event Sequence](https://mlsanity.com/paper/2310.04993/) (2023)
- [DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles](https://mlsanity.com/paper/2306.05957/) (2023)
- [Enhancing Multimodal Unified Representations for Cross Modal Generalization](https://mlsanity.com/paper/2403.05168/) (2024)
- [Multimodal Banking Dataset: Understanding Client Needs through Event Sequences](https://mlsanity.com/paper/2409.17587/) (2024)
- [TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models](https://mlsanity.com/paper/2410.02062/) (2024)
- [SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process](https://mlsanity.com/paper/2310.16336/) (2023)
- [Neuro-Symbolic Temporal Point Processes](https://mlsanity.com/paper/2406.03914/) (2024)
- [A Temporal Kernel Approach for Deep Learning with Continuous-time Information](https://mlsanity.com/paper/2103.15213/) (2021)
- [RoTHP: Rotary Position Embedding-based Transformer Hawkes Process](https://mlsanity.com/paper/2405.06985/) (2024)
- [Continuous-Time User Modeling in the Presence of Badges: A Probabilistic Approach](https://mlsanity.com/paper/1702.01948/) (2017)
- [Interpretable Hybrid-Rule Temporal Point Processes](https://mlsanity.com/paper/2504.11344/) (2025)
- [Targeted Mining of Time-Interval Related Patterns](https://mlsanity.com/paper/2507.12668/) (2025)

## Built on by (most cited first, 1 of 1)

- [Can LLMs Perceive Time? An Empirical Investigation](https://mlsanity.com/paper/2604.00010/) (2026)

## 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