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

The paper establishes a unified framework for timed opacity by introducing a universal observation model and defining evolution-based timed opacity, proving its relationship to existing opacity defini…

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

FlowTime proposes a novel Continuous Generative Regression framework using a Flow-based Personalized Prior to accurately model the multimodal and heterogeneous nature of user watch time prediction, si…

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…

This paper proves that a strongly consistent solution to the Causal Observability Problem is unachievable at the observable boundary and explores the impact of instrumentation placement on monitor gua…

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.

PoisonCap introduces a new 'poison' capability format for CHERI systems to provide efficient, strict use-after-free and initialization safety, surpassing existing temporal safety solutions.

The paper proposes a novel method to automatically enforce differential privacy in stream-based runtime monitoring specifications by analyzing temporal dependencies and injecting calibrated noise.

The paper establishes the first theoretical framework for analyzing the learnability of Test-Time Adaptation (TTA) under non-stationary data streams by introducing Recovery Complexity, which quantifie…

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

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.

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.

This paper introduces the concept of Budget-Aware Agents (BAGEN), showing that current LLM agents often fail to manage resources proactively, and proposes that incorporating early stop and interval es…

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 proposes a Secure-driven time synchronization mechanism to resolve the conflict between RTOS timekeeping (which requires periodic interrupts) and the atomicity requirements of trusted comput…

This paper introduces personalized mechanisms for estimating streaming statistics under $w$-event personalized differential privacy, significantly improving accuracy compared to existing methods.

The paper introduces a non-intrusive variant of index-aware learning for solving differential-algebraic equations (DAEs), ensuring that learned solutions maintain physical consistency like Kirchhoff's…

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…