Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes

2026-03-11

AI Summary

This work addresses the challenge of predicting irregular event sequences by jointly modeling dependencies among discrete event types and continuous-time dynamics. To this end, the authors propose the NEXTPP framework, which employs self-attention to encode event tokens, neural ordinary differential equations (Neural ODEs) to model continuous latent state evolution, and cross-attention mechanisms to enable bidirectional interaction between the discrete and continuous components. This integrated approach drives the conditional intensity function of a neural Hawkes process and represents the first unified framework for coupling discrete event markers with continuous temporal dynamics. By doing so, NEXTPP overcomes key limitations of existing methods in jointly capturing asynchronous dependencies and temporal evolution. Extensive experiments on five real-world datasets demonstrate that NEXTPP consistently outperforms state-of-the-art models, achieving significant improvements in event prediction performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous-time data streams.Existing sequential approaches capture dependencies among event tokens but ignore the continuous evolution between events, while Neural Ordinary Differential Equation (Neural ODE) methods model smooth dynamics yet fail to account for how event types influence future timing.To overcome these limitations, we propose NEXTPP, a dual-channel framework that unifies discrete and continuous representations via Event-granular Neural Evolution with Cross-Interaction for Marked Temporal Point Processes. Specifically, NEXTPP encodes discrete event marks via a self-attention mechanism, simultaneously evolving a latent continuous-time state using a Neural ODE. These parallel streams are then fused through a crossattention module to enable explicit bidirectional interaction between continuous and discrete representations. The fused representations drive the conditional intensity function of the neural Hawkes process, while an iterative thinning sampler is employed to generate future events. Extensive evaluations on five real-world datasets demonstrate that NEXTPP consistently outperforms state-of-the-art models. The source code can be found at https://github.com/AONE-NLP/NEXTPP.

Problem

• Marked Temporal Point Processes
• Discrete Marks
• Continuous Dynamics
• Irregular Event Sequences
• Neural Hawkes Process

Innovation

• Marked Temporal Point Processes
• Neural ODE
• Cross-Attention
• Neural Hawkes Process
• Event Sequence Modeling