The paper introduces ForesightFlow, an Information Leakage Score (ILS) framework, to quantify pre-event information leakage in prediction markets, and proposes a necessary extension to analyze empiric…

This study proposes a hybrid Gaussian Process Regression and Holt-Winters smoothing framework to accurately forecast under-five malaria admissions in Ghana, achieving high predictive accuracy and prov…

The paper introduces NumLeak, a framework demonstrating that top-tier LLMs often exhibit high fidelity recall of specific public numeric benchmarks (like financial factors) due to memorization, which…

The paper introduces NumLeak, a framework demonstrating that top-tier LLMs often exhibit high fidelity recall of specific public numeric benchmarks, suggesting that their apparent skill may be due to…

The paper investigates speculative Oracle Extractable Value (OEV) on Layer-2 blockchains, demonstrating that predictable latency differences in cross-chain oracle updates allow for profitable cross-ch…

GS-Fuse is a novel multimodal framework that improves financial forecasting by adaptively fusing event text and price data, achieving state-of-the-art performance by explicitly modeling the directiona…

AdaKoop introduces an efficient streaming algorithm that models complex nonlinear dynamics from nonstationary data streams by leveraging the Koopman operator theory, achieving state-of-the-art accurac…

The paper proposes Under-Cali, an uncertainty-driven dual-expert calibration framework, to achieve stable and efficient online forecasting for irregularly sampled multivariate time series.

The paper argues that long context windows are necessary for time series forecasting not just to capture long-range dependencies, but primarily to reduce uncertainty about the underlying data-generati…

The paper investigates forecasting sparse and bursty vulnerability sightings, concluding that traditional time-series models like SARIMAX are inadequate, and count-based methods like Poisson regressio…

This paper provides the first comprehensive threat model for IoT-enabled Controlled Environment Agriculture (CEA) systems, identifying 123 unique threats and proposing a defense-in-depth framework to…

The paper introduces and analyzes several novel data appraisal metrics, including the Vendi Score and matrix spectral functions, demonstrating that efficient optimization techniques make these metrics…

The paper proposes ReCAP, a novel continual learning framework for portfolio management, which adaptively combines policies from a library based on detected market regimes to achieve superior long-ter…

The paper introduces an explainable risk scoring framework that evaluates tokenized real-world assets (RWAs) based on liquidity, concentration, and market quality, demonstrating that total value locke…

The paper introduces PortBench, a comprehensive benchmark that evaluates LLMs for portfolio management by assessing both correlation awareness and performance across a full, multi-stage decision pipel…

The paper proposes an attention-enhanced deep learning framework using EfficientNet and CBAM to achieve high accuracy (93.3%) in classifying peach leaf damage, demonstrating improved robustness under…

The paper demonstrates that replacing standard pointwise losses (like MSE) with multi-quantile regression significantly improves precipitation nowcasting accuracy and provides valuable risk estimates…

The paper proposes a robust, multi-stage pipeline combining rule-based classification and machine learning to map noisy retail product names to standardized consumption categories, finding that simple…

This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…