This paper investigates the tradeoff between parameter estimation and predictive accuracy in probabilistic temporal graphs, proposing a causal framework for evaluation.

The paper proposes Dual-Scale Retentive Dynamics (DSRD), a unified framework that improves representation learning on dynamic graphs by jointly modeling evolving temporal and structural dependencies.

This paper proposes ChronoID, a framework for time-aware semantic ID learning in generative recommendation.

The paper introduces and analyzes 'temporal framing,' defining it as the persuasive use of time-related language in news, and demonstrates that this framing can be effectively detected using supervise…

CHRONOS is a novel three-layer architecture designed to address coupled failures in temporal data marketplaces by integrating temporal decay, changepoint-aware pricing, and differential privacy for ro…

This paper proposes a Meta-Role Temporal Graph Network (MR-TGN) framework for collective intent prediction in multi-agent systems, modeling agents as dynamically evolving graph entities and employing…

This paper proposes a hierarchical analytical framework to characterize region-level latency differences in Low-Earth orbit satellite Internet using Starlink RTT measurements.

The paper proposes TDPM, a time-aware diffusion model for generative recommendation, which significantly improves recommendation accuracy by explicitly modeling the non-stationary, time-evolving natur…

The paper proposes a novel nonparametric mutual information estimator to robustly quantify dependence between heterogeneous temporal data, specifically continuous time series and discrete event sequen…

ChronosAD introduces a novel architecture that uses time series foundation models and a custom Temporal Block to achieve robust and highly accurate anomaly detection across diverse domains.

The paper proposes VISTA, a multi-level event semantics mining framework, to accurately predict complex events in long videos, addressing the limitations of current LLMs in this domain.

A new reliability metric is proposed for DTW alignment paths based on agreement between DTW and FlexDTW.

This paper introduces the Group Attention Neural Hawkes Process (GAttNHP) framework to improve forecasting of future events on Temporal Knowledge Graphs (TKGs) by addressing long-range temporal depend…

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…

This paper proposes a taxonomy-guided evaluation protocol for temporal fidelity in synthetic sequential tabular data, measuring timestamp validity, cross-sectional structure, within-entity dynamics, a…

The paper introduces WebKnoGraph, an open-source framework for systematically evaluating internal linking strategies on websites by modeling the site as a graph and assessing trade-offs between author…

This paper introduces PULS, a continuous semantic world-model pipeline for video anomaly detection, which includes a KSD Bridge and an Anticipatory State Predictor. The KSD Bridge maps physical tensor…

This paper investigates the effects of structural and temporal inhomogeneities in decentralised federated learning and shows that they significantly slow down the convergence process.