arXiv:2511.16822v1 Announce Type: new Abstract: In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model…
arXiv:2511.16786v1 Announce Type: new Abstract: Multimodal large language models suffer from substantial inference overhead since multimodal KV Cache grows proportionally with the visual input length. Existing multimodal KV Cache compression methods mostly rely on attention score to reduce cache size,…
arXiv:2511.16778v1 Announce Type: new Abstract: Recently, structure-text contrastive learning has shown promising performance on text-attributed graphs by leveraging the complementary strengths of graph neural networks and language models. However, existing methods typically rely on homophily assumptions in similarity estimation and…
arXiv:2511.16767v1 Announce Type: new Abstract: Graphs provide a unified representation of semantic content and relational structure, making them a natural fit for domains such as molecular modeling, citation networks, and social graphs. Meanwhile, large language models (LLMs) have excelled at…
arXiv:2511.02969v2 Announce Type: replace Abstract: Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $epsilon$-greedy and Boltzmann…
arXiv:2511.16828v1 Announce Type: new Abstract: Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains brain activity to low-dimensional manifolds. This fundamental mismatch between model…
arXiv:2207.07250v3 Announce Type: replace-cross Abstract: We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU$(d)$ symmetries. It allows us to enhance a natural model of…
arXiv:2511.16839v1 Announce Type: new Abstract: Transformers have defined the state-of-the-art for clinical prediction tasks involving electronic health records (EHRs). The recently introduced Mamba architecture outperformed an advanced Transformer (Transformer++) based on Llama in handling long context lengths, while using fewer…
arXiv:2508.13663v2 Announce Type: replace-cross Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are emph{likely} to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However,…
arXiv:2511.16845v1 Announce Type: new Abstract: Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential for decision making. Conformal prediction (CP) is a general UQ framework that provides…
