Knowledge catalogue
arXiv:2605.04447v1 Announce Type: new Abstract: Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation model
arXiv:2605.03420v1 Announce Type: cross Abstract: This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level
arXiv:2605.04998v1 Announce Type: cross Abstract: Chord progression generation is practically important but understudied. Most large-scale symbolic music systems target melody, multi-track arrangement
arXiv:2605.04882v1 Announce Type: new Abstract: Automated glaucoma detection is critical for preventing irreversible vision loss and reducing the burden on healthcare systems. However, ensuring fairne
arXiv:2510.00468v4 Announce Type: replace Abstract: We provide evidence that eigenanalysis of the empirical neural tangent kernel (eNTK) can surface feature directions in trained neural networks. Acro
arXiv:2605.05034v1 Announce Type: new Abstract: Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large a
arXiv:2605.04590v1 Announce Type: new Abstract: Text-based image segmentation aims to delineate object boundaries within an image from text prompts, offering higher flexibility and broader application
arXiv:2605.05072v1 Announce Type: new Abstract: 3D occupancy prediction aims to infer dense, voxel-wise scene semantics from sensor observations, where the 2D-to-3D view transformation serves as a cru
arXiv:2605.04682v1 Announce Type: cross Abstract: Spatial transcriptomics offers spatially resolved gene expression profiling within tissue sections, but its cost and limited throughput hinder large-s
arXiv:2602.07608v2 Announce Type: replace Abstract: Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it w
arXiv:2603.11911v3 Announce Type: replace Abstract: We present InSpatio-WorldFM, an open-source real-time frame model for spatial intelligence. Unlike video-based world models that rely on sequential
arXiv:2605.04445v1 Announce Type: new Abstract: The rapid advancement of generative technologies has made synthetic images nearly indistinguishable from real ones, thereby creating an urgent need for
arXiv:2605.04323v1 Announce Type: new Abstract: Understanding soil is fundamental to agriculture, carbon cycling, and environmental sustainability, yet progress is limited by fragmented and heterogene
arXiv:2605.04772v1 Announce Type: new Abstract: Access to diverse, well-annotated medical images with interactive learning tools is fundamental for training practitioners in medicine and related field
arXiv:2605.04566v1 Announce Type: cross Abstract: Recent studies have shown that large generative models can solve vision tasks they were not explicitly trained for. However, existing evidence relies
arXiv:2605.03762v1 Announce Type: new Abstract: Large language models are moving from static text generators toward real-world decision-support systems, where forecasting is a composite capability tha
arXiv:2605.05027v1 Announce Type: new Abstract: Lifelong person re-identification (LReID) aims to train a generalizable model with sequentially collected data. However, such models often suffer from s
arXiv:2605.04560v1 Announce Type: new Abstract: Perceptual image compression focuses on preserving high visual quality under low-bitrate constraints. Most existing approaches to perceptual compression
arXiv:2605.04412v1 Announce Type: new Abstract: 3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects from a s
arXiv:2605.05206v1 Announce Type: new Abstract: We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small
arXiv:2605.03085v1 Announce Type: new Abstract: Electroencephalography (EEG) signals provide millisecond-level temporal resolution but their analysis is limited by remarkable noise and inter-subject v
arXiv:2605.04045v1 Announce Type: new Abstract: Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines
arXiv:2509.15692v2 Announce Type: replace-cross Abstract: Simultaneous speech-to-text translation (Simul-S2TT) aims to translate speech into target text in real time, outputting translations while rec
Jeremy Howard proposes three key enhancements for large language models: incorporating latent space diffusion-like reasoning processes, adding a persistent recurrent state mechanism, and implementing
arXiv:2511.08877v2 Announce Type: replace Abstract: Large language models (LLMs) generate fluent text across a wide range of tasks, but the fabrication of non-existent academic citations remains a cri
arXiv:2605.03555v1 Announce Type: new Abstract: Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Exper
arXiv:2605.01235v1 Announce Type: cross Abstract: Driven by the escalating global burden of mental health conditions, music-based interventions have attracted significant attention as a non-invasive,
arXiv:2605.03252v1 Announce Type: cross Abstract: LoRA fine-tuning of diffusion transformers (DiT) on multi-style data suffers from style bleed: a single low-rank residual cannot represent several dis
arXiv:2605.02004v1 Announce Type: new Abstract: Personalized models are essential in digital health because individuals exhibit substantial physiological and behavioral heterogeneity. Yet personalizat
arXiv:2605.02917v1 Announce Type: new Abstract: Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts,
arXiv:2605.03075v1 Announce Type: cross Abstract: Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score compositio
arXiv:2605.03824v1 Announce Type: new Abstract: Complex information needs may involve set-compositional queries using conjunction, disjunction, and exclusion, yet it remains unclear whether current re
arXiv:2605.03279v1 Announce Type: new Abstract: Automatic modulation classification (AMC) in real-world deployments demands robustness to distribution shifts arising from hardware impairments, unseen
arXiv:2605.03849v1 Announce Type: new Abstract: Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching
arXiv:2605.03237v1 Announce Type: cross Abstract: Project-based learning improves student engagement and learning outcomes, yet allocating students to appropriately challenging projects while forming
arXiv:2605.03245v1 Announce Type: cross Abstract: Image-based Joint-Embedding Predictive Architecture (I-JEPA) offers a promising approach to visual self-supervised learning through masked feature pre
arXiv:2605.03716v1 Announce Type: new Abstract: Multimodal visual object tracking can be divided into to several kinds of tasks (e.g. RGB and RGB+X tracking), based on the input modality. Existing met
arXiv:2605.01477v1 Announce Type: new Abstract: We present Action Agent, a two-stage framework that unifies agentic navigation video generation with flow-constrained diffusion control for multi-embodi
arXiv:2605.01255v1 Announce Type: new Abstract: Training large language models (LLMs) is highly memory-intensive, as training must store not only weights and optimizer states but also intermediate act
arXiv:2605.01642v1 Announce Type: new Abstract: Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time. We introduce
arXiv:2605.01741v1 Announce Type: new Abstract: Cone Beam Computed Tomography (CBCT) is pivotal for 3D diagnostic imaging in dentistry. However, the development of robust AI models for volumetric anal
arXiv:2605.00937v1 Announce Type: cross Abstract: We propose an arbitrary Lagrangian-Eulerian (ALE)-consistent machine learning framework for long-term fluid-structure interaction (FSI) prediction on
arXiv:2506.03820v2 Announce Type: replace Abstract: Hausa texts are often characterized by writing anomalies, such as incorrect character substitutions and spacing errors, which sometimes hinder natur
arXiv:2605.02357v1 Announce Type: new Abstract: In 3D point cloud understanding, the core challenge lies in accurately capturing discriminative features within complex neighborhoods, which directly af
arXiv:2605.01634v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) offer a flexible paradigm for solving differential equations by embedding governing laws into the training obje
arXiv:2605.02159v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak tran
arXiv:2509.01453v2 Announce Type: replace Abstract: Images vary in how memorable they are to humans. Inspired by findings from cognitive science and computer vision, we explore correlates of image mem
arXiv:2605.01323v1 Announce Type: new Abstract: In this article, we introduce SiNFluD, a novel benchmark dataset for Sindhi figurative language classification. We first collect raw text from various b
arXiv:2605.01858v1 Announce Type: new Abstract: Streaming video understanding requires processing unbounded video streams with limited memory and computation, posing two key challenges. First, continu
arXiv:2605.02245v1 Announce Type: new Abstract: Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep arch
arXiv:2605.01848v1 Announce Type: new Abstract: Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pat
arXiv:2605.01869v1 Announce Type: cross Abstract: Token communication has emerged as a promising framework for efficient wireless transmission by representing source data as compact semantic tokens. H
arXiv:2605.00371v1 Announce Type: cross Abstract: In this paper, we propose GaMMA, a state-of-the-art (SoTA) large multimodal model (LMM) designed to achieve comprehensive musical content understandin
arXiv:2507.07129v3 Announce Type: replace-cross Abstract: We study a constrained training regime for decoder-only Transformers in which the token interface is fixed, previously trained dense blocks ar
arXiv:2605.02133v1 Announce Type: new Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale
arXiv:2605.01580v1 Announce Type: new Abstract: Modern deep learning usually treats models as separate artifacts: trained independently, specialized for particular purposes, and replaced when improved
arXiv:2605.01954v1 Announce Type: cross Abstract: Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback i
arXiv:2511.03488v2 Announce Type: replace Abstract: Physiological signals are inherently heterogeneous: they are collected under diverse acquisition setups, differ in the number and type of modalities
arXiv:2603.29258v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that
arXiv:2605.01640v1 Announce Type: cross Abstract: Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to