arXiv:2511.07637v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving documents from an external corpus at inference time. When this corpus contains sensitive information, however, unprotected RAG systems are at risk of leaking private information.…
arXiv:2509.03764v2 Announce Type: replace-cross Abstract: Relevance evaluation plays a crucial role in personalized search systems to ensure that search results align with a user’s queries and intent. While human annotation is the traditional method for relevance evaluation, its high cost…
arXiv:2511.07649v1 Announce Type: new Abstract: Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip as an adaptive, time-varying graph learning framework for…
arXiv:2511.05540v2 Announce Type: replace-cross Abstract: We challenge the long-standing assumption that exhaustive scene modeling is required for high-performance end-to-end autonomous driving (E2EAD). Inspired by cognitive science, we propose that effective planning arises not from reconstructing the world, but from the…
arXiv:2511.07651v1 Announce Type: new Abstract: Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address limitations of traditional crime linkage methods in handling high-dimensional, sparse, and heterogeneous data, we propose a Siamese Autoencoder framework…
arXiv:2501.05530v2 Announce Type: replace-cross Abstract: This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ…
arXiv:2509.15810v2 Announce Type: replace Abstract: To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the…
arXiv:2511.04807v2 Announce Type: replace Abstract: Given a “data manifold” $Msubset mathbb{R}^n$ and “latent space” $mathbb{R}^ell$, an autoencoder is a pair of continuous maps consisting of an “encoder” $Ecolon mathbb{R}^nto mathbb{R}^ell$ and “decoder” $Dcolon mathbb{R}^ellto mathbb{R}^n$ such that the “round trip”…
arXiv:2410.12982v2 Announce Type: replace Abstract: While transformers have been at the core of most recent advancements in sequence generative models, their computational cost remains quadratic in sequence length. Several subquadratic architectures have been proposed to address this computational issue. Some…
arXiv:2505.22422v2 Announce Type: replace Abstract: The construction of confidence intervals for the mean of a bounded random variable is a classical problem in statistics with numerous applications in machine learning and virtually all scientific fields. In particular, obtaining the tightest…
