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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.CV English(EN) ·

    CABLE: Cloud-Assisted Bandwidth-efficient LMM-based Encoding for V2X Systems

    Researchers have developed CABLE, a novel framework designed to enhance the efficiency of large multimodal models (LMMs) in vehicle-to-everything (V2X) systems. This system reduces communication overhead and cloud-side latency by uploading only masked regions of interest (ROIs) from edge devices to the cloud, rather than full-resolution frames. CABLE utilizes previous segmentation masks, ego-motion compensation, and residual-motion cues to define these ROIs, creating a feedback loop between the cloud and edge. Experiments on multiple datasets demonstrate significant communication savings and improved LMM prefill speeds with only a minor trade-off in detection quality. AI

    CABLE: Cloud-Assisted Bandwidth-efficient LMM-based Encoding for V2X Systems

    IMPACT This framework could significantly reduce the computational and communication costs for real-time AI perception in autonomous systems.

  2. RESEARCH · arXiv stat.ML English(EN) · · [4 sources]

    Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction

    Researchers have developed new methods for online conformal prediction, a framework for uncertainty quantification in machine learning. The proposed techniques, Online Localized Conformal Prediction (OLCP) and State-Adaptive Bayesian Conformal Prediction (SA-BCP), aim to improve prediction set efficiency and stability, particularly in non-exchangeable data settings like time-series and online learning. These methods address limitations of existing approaches by incorporating covariate-dependent localization and spatio-temporal decoupling, leading to more reliable uncertainty estimates and narrower prediction intervals. AI

    Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction

    IMPACT Introduces advanced techniques for more robust uncertainty quantification in machine learning models, potentially improving reliability in time-series and online learning applications.