Knowledge catalogue
arXiv:2608.18727v1 Announce Type: new Abstract: Applying machine learning to web pages is challenging due to the need to interpret HTML together with associated resources and perform rendering to obta
arXiv:2608.18399v1 Announce Type: new Abstract: Vision transformers (ViTs) trained to copy a pretrained teacher's attention maps recover most of fine-tuning's in-distribution accuracy yet fall measura
arXiv:2608.18330v1 Announce Type: new Abstract: Whether input-dependent ('dynamic') combination of a regression model pool beats the best static blend depends on the shift and is rarely known before d
arXiv:2608.19063v1 Announce Type: new Abstract: PSMA and FDG PET/CT visualise complementary biological information in prostate cancer. Combining both tracers could capture heterogeneous tumour phenoty
arXiv:2608.18521v1 Announce Type: new Abstract: Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules into contrasti
arXiv:2608.18486v1 Announce Type: new Abstract: In a Transformer, each layer attends to past tokens only through KV produced at its own depth, despite the presence of deeper representations during aut
arXiv:2608.18661v1 Announce Type: new Abstract: Streaming text-to-speech is essential for low-latency spoken dialogue systems, yet many systems wait for sentence-level text and are therefore only pseu
arXiv:2608.17298v1 Announce Type: cross Abstract: 3D Gaussian Splatting has made Gaussian primitives a highly efficient representation for real-time novel view synthesis, but its rasterisation-based f
arXiv:2608.16947v1 Announce Type: cross Abstract: Huang, Lou, and Xiao introduced Dynamic Mixture-of-Experts Serving and gave an O(sqrt(log k))-competitive randomized algorithm for its integral primal
arXiv:2608.17124v1 Announce Type: new Abstract: Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by
arXiv:2608.17096v1 Announce Type: new Abstract: The Voynich manuscript (Beinecke MS 408) is usually analysed on three unstated assumptions: that its glyphs are letters, that the strings between blanks
arXiv:2608.17711v1 Announce Type: new Abstract: Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive performance. The id
arXiv:2608.17250v1 Announce Type: cross Abstract: This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temp
arXiv:2608.17856v1 Announce Type: new Abstract: Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapt
arXiv:2512.06040v2 Announce Type: replace-cross Abstract: Voice authentication systems deployed at the network edge face dual threats: a) sophisticated deepfake synthesis attacks and b) control-plane
arXiv:2608.16916v1 Announce Type: cross Abstract: Calculating average distances in large-scale networks is computationally intensive and constrained by limited main memory, posing a significant challe
arXiv:2608.17087v1 Announce Type: new Abstract: Linear temporal logic is a modal extension of propositional logic that allows one to state how a system should behave over time. Its canonical domain is
arXiv:2608.17060v1 Announce Type: new Abstract: Distinguishing a genuine foul from a simulated dive in football remains one of the sport's most contested fine-grained recognition problems, especially
arXiv:2608.17132v1 Announce Type: new Abstract: Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The i
arXiv:2607.09812v2 Announce Type: replace-cross Abstract: Microbial density is clinically important for tumor assessment and treatment decision-making, and recent advances in deep learning suggest tha
arXiv:2608.17592v1 Announce Type: cross Abstract: The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents mus
arXiv:2602.12250v2 Announce Type: replace Abstract: Graph neural networks (GNNs) enable powerful unsupervised learning of communities. However, such inference may inadvertently expose sensitive group
arXiv:2608.18004v1 Announce Type: new Abstract: Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-ba
arXiv:2311.09614v5 Announce Type: replace-cross Abstract: This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing lite
arXiv:2608.17366v1 Announce Type: new Abstract: Merging finetuned models combines specialized capabilities without joint training or access to the original data. Most methods operate by linear arithme
arXiv:2608.17427v1 Announce Type: new Abstract: Medical vision-language models (Med-VLMs) have demonstrated strong performance on medical visual question answering, yet they remain prone to hallucinat
arXiv:2602.23088v2 Announce Type: replace Abstract: Vision foundation models increasingly support interactive scientific workflows, but natural-language interaction requires coupling visual representa
arXiv:2608.16968v1 Announce Type: cross Abstract: In this paper, we consider methods for the diagonal multi-omics integration of heterogeneous datasets. Several approaches to the nature of biological
arXiv:2608.17796v1 Announce Type: cross Abstract: Deep learning-based DDoS detectors for 5G-enabled cyber-physical systems face scarce labeled attack data and unrealistic synthetic substitutes, which
arXiv:2608.17093v1 Announce Type: cross Abstract: Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency
arXiv:2608.17703v1 Announce Type: cross Abstract: Mobile robots that operate in side by side with humans and critical facilities must reach their goals at low cost, despite often unknown true traversa
arXiv:2608.17950v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these dist
arXiv:2608.17983v1 Announce Type: cross Abstract: Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisi
arXiv:2608.17896v1 Announce Type: new Abstract: Recurrent models process long contexts efficiently by compressing their history into a fixed-size state, but modern architectures typically do so in a s
arXiv:2608.17079v1 Announce Type: new Abstract: Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting
arXiv:2608.17453v1 Announce Type: new Abstract: Long-horizon humanoid vision--language--action (VLA) control with head-mounted stereo cameras requires visual interfaces that can exploit complementary
arXiv:2608.18063v1 Announce Type: new Abstract: High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions
arXiv:2608.17849v1 Announce Type: new Abstract: Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision var
arXiv:2608.17941v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but relies on costly rollout explor
arXiv:2608.16930v1 Announce Type: cross Abstract: Existing multi-task learning methods rely on hard sharing, multiple paths or experts, adaptive sharing, and dynamic expansion. However, their capacity
arXiv:2608.17843v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it informs model beh
arXiv:2608.17787v1 Announce Type: cross Abstract: Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with
arXiv:2607.15209v2 Announce Type: replace Abstract: Multilingual pre-trained language models such as XLM-R perform well for major languages but struggle with low-resource Ge'ez-script languages, large
arXiv:2407.13753v3 Announce Type: replace Abstract: Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disord
arXiv:2608.17203v1 Announce Type: cross Abstract: Contrastive learning has become a cornerstone of modern representation learning, powering CLIP-style models that underpin text-to-image generation, vi
arXiv:2608.17573v1 Announce Type: cross Abstract: In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambi
arXiv:2603.04061v2 Announce Type: replace-cross Abstract: Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of quantum hypothesis t
arXiv:2608.17802v1 Announce Type: new Abstract: Let arepsilon_1,ldots,arepsilon_n be independent Rademacher signs and let a=(a_1,ldots,a_n)inR^n satisfy the normalization below. For the normalized Rad
arXiv:2608.17827v1 Announce Type: new Abstract: Public institutions face a persistent challenge in selecting LLMs suited to their specific context. Existing benchmarks, however, are of limited use as
arXiv:2511.16088v3 Announce Type: replace-cross Abstract: Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks
arXiv:2608.01324v2 Announce Type: replace Abstract: Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches
arXiv:2603.00162v2 Announce Type: replace-cross Abstract: [18F]FDG-PET/CT is a cornerstone imaging modality for guiding oncology therapies, yet human expert shortages necessitate more efficient diagno
arXiv:2608.17832v1 Announce Type: new Abstract: Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a uniqu
arXiv:2506.06584v2 Announce Type: replace Abstract: Learning Gaussian Mixture Models (GMMs) is a fundamental problem in statistics and machine learning, with the Expectation-Maximization (EM) algorith
arXiv:2608.17938v1 Announce Type: cross Abstract: Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against an explicit
arXiv:2608.17634v1 Announce Type: new Abstract: The operatorname{do}-operator is described graphically by deleting arrows into its targets and functionally by replacing their mechanisms with constants
arXiv:2608.17535v1 Announce Type: new Abstract: Simultaneously reconstructing and understanding 3D environments is essential for embodied agents. Toward this goal, feed-forward semantic 3D Gaussian Sp
arXiv:2608.18036v1 Announce Type: cross Abstract: MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieva
arXiv:2608.16927v1 Announce Type: cross Abstract: As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and i
arXiv:2608.18056v1 Announce Type: new Abstract: Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path rerouting can be high