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source_url: https://openreview.net/forum?id=SXgGKkShhT
ingested: 2026-06-16
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# Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches

**Authors:** Feng Zhou (Renmin Univ.), Quyu Kong (Independent), Jie Qiao (Guangdong Univ. of Tech.), Cheng Wan (Renmin Univ.), Yixuan Zhang (Southeast Univ.), Ruichu Cai (Guangdong Univ. of Tech.)

**Venue:** Transactions on Machine Learning Research (TMLR), June 2026

**OpenReview:** https://openreview.net/forum?id=SXgGKkShhT

## Abstract

Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed and successfully applied across diverse domains. In recent years, advances in deep learning have spurred the development of neural TPPs, enabling greater flexibility and expressiveness in capturing complex temporal dynamics. The emergence of large language models (LLMs) has further sparked excitement, offering new possibilities for modeling and analyzing event sequences by leveraging their rich contextual understanding. This survey presents a comprehensive review of recent research on TPPs from three perspectives: Bayesian, deep learning, and LLM approaches. We begin with a review of the fundamental concepts of TPPs, followed by an in-depth discussion of model design and parameter estimation techniques in these three frameworks. We also revisit classic application areas of TPPs to highlight their practical relevance. Finally, we outline challenges and promising directions for future research.

## Taxonomy (Figure 1)

1. **TPP Preliminaries** — Unmarked TPP, Marked TPP, conditional intensity function
2. **Bayesian TPPs** — Parametric Bayesian TPPs, Bayesian Nonparametric Poisson Process, Bayesian Nonparametric Hawkes Process
3. **Neural TPPs** — Recurrent Neural TPPs, Autoregressive (Transformer) TPPs, Diffusion-based TPPs, Parameterization choices
4. **LLM-based TPPs** — LLM-inspired TPPs (PromptTPP, LAMP), Direct LLM-TPP Integration (TPP-LLM, Language-TPP), Multimodal extensions
5. **Datasets & Benchmarks** — EasyTPP, DanmakuTPPBench
6. **Training Methods** — MLE, Wasserstein, NCE, Score Matching, Fisher Divergence
7. **Applications** — Event Prediction (social, epidemiology, finance, recommendation), Causal Discovery (neuroscience, finance, AI ops, cybersecurity)
8. **Challenges** — Data/model heterogeneity, interpretability, scalability, sampling efficiency, multimodal modeling

## Key Contributions

1. First survey to cover TPPs across Bayesian, neural, AND LLM paradigms in a unified framework
2. Emphasis on Bayesian nonparametric TPPs (overlooked in prior surveys)
3. Systematic review of LLM-based TPPs (nascent area, not previously surveyed)
4. Comprehensive taxonomy bridging statistical rigor, neural flexibility, and LLM capabilities
