This paper presents Vivace, a serverless system for exact temporal OLAP over interval histories, which addresses the issues of incomplete data and incorrect answers in serverless functions.

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…

The paper introduces TimeSage-MT, a comprehensive multi-turn benchmark designed to rigorously test an LLM agent's ability to perform complex, evolving time series analysis, revealing critical gaps in…

The paper introduces QuITE, a plug-and-play embedding module that uses learnable query tokens to effectively embed irregular multivariate time series data into latent representations compatible with e…

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

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…

The paper proposes MERIT, a dual-level, multi-horizon memory retrieval framework that significantly improves the performance of interactive text-to-SQL agents by providing both global and local memory…

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 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…

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…

MORPHOS is a novel autoregressive framework that generates dynamic 3D assets (like meshes and radiance fields) from videos by using a unified 4D representation to ensure temporal consistency and handl…

The paper analyzes a fragment of Higher-Order Datalog, showing that restricting recursion to a linear form shifts its expressive power from time complexity to space complexity, specifically capturing…

The paper introduces an LLM-agent framework to solve the 'last-mile forecasting' problem, bridging the gap between raw statistical predictions and business-ready forecasts by incorporating weakly stru…

The paper introduces Open-Domain Tabular Question Answering for Future Data Forecasting and Reasoning, a new dataset and framework that enables LLMs to perform time-series forecasting and reasoning on…

The paper proposes a flexible meta-programming framework to declaratively operationalize and explore varied temporal logics, such as TEL, MEL, and DEL, within standard Answer Set Programming systems.

The paper introduces Momento, a new benchmark that evaluates agentic AI's ability to maintain state and reason across multiple, disconnected sessions, revealing that current agents struggle with integ…

TabChange proposes a novel framework to generate natural and minimally altered counterfactual instances in tabular data by precisely controlling attribute modifications based on their relationship str…

The paper introduces a new anytime-valid inference method to correct split selection in online decision trees, providing robust statistical guarantees for streaming data that existing methods lack.

This paper establishes the identifiability of latent regimes and regime-dependent causal structures in complex non-stationary time series modeled by Markov Switching Models, even with instantaneous ef…

The paper introduces Sophrosyne, a system that moderates LLM agent exploration in relational data systems, significantly reducing over-exploration and boosting SQL generation accuracy by guiding the a…