Knowledge catalogue
arXiv:2606.04419v1 Announce Type: cross Abstract: MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam
arXiv:2601.21461v3 Announce Type: replace-cross Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP
arXiv:2511.06331v2 Announce Type: replace Abstract: Detailed structural and species information on individual tree level is increasingly important to support precision forestry, biodiversity conservat
arXiv:2606.04302v1 Announce Type: new Abstract: Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes e
arXiv:2606.04597v1 Announce Type: new Abstract: Admissible heuristics are essential for optimal planning, yet learning them remains challenging due to the risk of overestimation. Cost partitioning com
arXiv:2606.05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (
arXiv:2606.04623v1 Announce Type: new Abstract: High-dimensional Hamiltonian systems play a central role in many scientific and engineering disciplines, with dynamics evolving on symplectic manifolds.
arXiv:2606.05087v1 Announce Type: new Abstract: Frequent English verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'mak
arXiv:2405.15454v4 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both
arXiv:2511.12581v2 Announce Type: replace Abstract: Static IR drop analysis is a fundamental and critical task in the field of chip design. Nevertheless, this process can be quite time-consuming, pote
arXiv:2601.03569v3 Announce Type: replace Abstract: Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detectio
arXiv:2606.04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and
arXiv:2606.04405v1 Announce Type: cross Abstract: Modern Transformer architectures frequently employ normalization mechanisms such as RMSNorm and Query-Key Normalization, making parts of the model app
arXiv:2606.05068v1 Announce Type: new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because
arXiv:2602.02405v2 Announce Type: replace-cross Abstract: Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct soluti
arXiv:2606.04880v1 Announce Type: new Abstract: Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection reg
arXiv:2511.00801v4 Announce Type: replace Abstract: Text-guided medical image editing must satisfy the requested pathology while preserving anatomy, modality-specific appearance, and clinical plausibi
arXiv:2606.04621v1 Announce Type: new Abstract: We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction,
arXiv:2601.07036v2 Announce Type: replace-cross Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we
arXiv:2505.22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible. To do thi
arXiv:2602.11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties
arXiv:2606.04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ le
arXiv:2606.04730v1 Announce Type: new Abstract: With the advent of Large Language Models, single-task and token-based multi-task models have evolved into instruction-based systems that infer task and
arXiv:2606.04757v1 Announce Type: cross Abstract: We study decentralized stochastic smooth convex optimization, where M workers minimize an average objective using local stochastic gradients and neigh
arXiv:2506.23546v2 Announce Type: replace-cross Abstract: Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points can be captured by the Boltzm
arXiv:2606.04008v1 Announce Type: cross Abstract: Radiated noise in unmanned underwater vehicles (UUVs) is an important indicator for characterizing acoustic signatures and evaluating platform perform
arXiv:2606.04957v1 Announce Type: cross Abstract: System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We
arXiv:2606.04340v1 Announce Type: new Abstract: A sentence like 'The authors that no critics recommended have ever received acknowledgment for a best-selling novel' is sometimes rated as acceptable ev
arXiv:2606.04265v1 Announce Type: cross Abstract: The Schrodinger Bridge Problem constructs a stochastic process that connects an initial distribution to a terminal distribution with minimum energy. T
arXiv:2606.04028v1 Announce Type: new Abstract: The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating ma
arXiv:2606.04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting. Although this problem arises frequently in practice
arXiv:2601.21868v2 Announce Type: replace-cross Abstract: Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provid
arXiv:2606.04451v1 Announce Type: new Abstract: Neighbor embedding algorithms reveal correlations in high-dimensional data by constructing an equivalent graph representation in a lower-dimensional spa
arXiv:2602.01083v2 Announce Type: replace Abstract: Weight-space learning studies neural architectures that operate directly on the parameters of other neural networks. Motivated by the growing availa
arXiv:2606.04320v1 Announce Type: cross Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pas
arXiv:2509.22454v2 Announce Type: replace Abstract: Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving competitive performance in image synthesis.
arXiv:2606.04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented g
arXiv:2603.09391v2 Announce Type: replace-cross Abstract: Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods ty
arXiv:2606.04392v1 Announce Type: cross Abstract: This study develops a two-domain physics-informed neural network framework for contaminant transport through a GCL/SL composite liner system, in which
arXiv:2606.04133v1 Announce Type: new Abstract: Image geolocation aims to estimate where a photograph was taken from its visual content. At worldwide scale, this remains challenging because visual evi
arXiv:2510.03511v3 Announce Type: replace-cross Abstract: While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant meth
arXiv:2606.04834v1 Announce Type: new Abstract: Minimum Description Length (MDL) formalizes the principle of Occam's razor by optimizing the total description length: L(model)+L(data | model). For seq
arXiv:2606.05129v1 Announce Type: cross Abstract: Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed caus
arXiv:2606.04319v1 Announce Type: cross Abstract: We propose a neural formulation for estimating the appearance of complex luminaires. We focus on challenging luminaires with complex light transport (
arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the S
arXiv:2602.00104v3 Announce Type: replace-cross Abstract: Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. How
arXiv:2606.04453v1 Announce Type: new Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis
arXiv:2606.04396v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate responses by iteratively unmasking and revising many positions in parallel. This process leaves a rich
arXiv:2606.04680v1 Announce Type: cross Abstract: Automatic speech recognition systems commonly rely on reference transcriptions for evaluation, while reference-free approaches often depend on interna
arXiv:2606.04684v1 Announce Type: cross Abstract: The real-time hardships of video processing seriously limit the usage of Automatic License Plate Recognition (ALPR) with application in dynamic traffi
arXiv:2604.01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remar
arXiv:2606.04706v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable
arXiv:2606.04582v1 Announce Type: cross Abstract: Real-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions o
arXiv:2606.04576v1 Announce Type: cross Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited param
arXiv:2606.04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations. We show that this paradigm obscures a fundamental vulnerabi
arXiv:2603.12433v3 Announce Type: replace-cross Abstract: Model stitching, connecting early layers of one model (source) to later layers of another (target) via a light stitch layer, has served as a p
arXiv:2606.04384v1 Announce Type: new Abstract: Machine learning's reliance on sensitive data necessitates privacy-preserving techniques like Differentially Private Stochastic Gradient Descent (DPSGD)
arXiv:2606.04974v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional conte
arXiv:2606.04120v1 Announce Type: cross Abstract: Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context win
arXiv:2606.04500v1 Announce Type: new Abstract: High-throughput microscopy generates large, structured datasets capturing cellular responses to pharmacological perturbations, but accessing these datas