TimeMark proposes a trustworthy time watermarking framework that uses cryptographic techniques and error-correcting codes to achieve 100% accurate recovery of the generation time from AIGC, resisting…

The paper proposes a novel binomial multibit LLM watermarking scheme that encodes every bit of a payload at every token position, achieving superior message accuracy and robustness compared to existin…

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…

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 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…

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 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…

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…

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 the 'readout-mediator angle' to demonstrate that simple linear probes, while capable of decoding information, often capture directions orthogonal to the model's actual causal comp…

The paper proposes an infrastructure, clawgang and meowtrade, to transform private, non-transferable agent memories into verifiable, tradable economic commodities.

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 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…

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…

This paper proposes Stringology-Based Cryptology (SBC), a novel approach that analyzes the structural properties of cryptographic outputs by treating them as symbolic sequences, offering complementary…

The paper proposes a time-aware self-supervised learning framework using BYOL to improve Android malware detection robustness by accurately accounting for app release times.

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

The paper proposes a novel active learning framework using Linearized Optimal Transport to strategically select measurement timepoints, thereby minimizing uncertainty when inferring continuous probabi…

MemMark introduces a state-evolution attribution watermark that embeds owner-controlled signals into latent memory-write decisions, enabling robust provenance tracking for agent memory even when all t…

The paper proposes TEMG-TTA, a novel framework that combines temporal motif awareness and test-time adaptation to significantly improve Out-of-Distribution (OOD) anomaly detection in complex blockchai…