Knowledge catalogue
arXiv:2608.30537v1 Announce Type: new Abstract: Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectral I
arXiv:2608.31091v1 Announce Type: cross Abstract: Watermarking has been proposed as a way to identify synthetic samples in estimation settings where no metadata is available to distinguish them from r
arXiv:2608.28669v1 Announce Type: new Abstract: Recovering an executable parametric CAD program from an observed object is fundamentally ambiguous, because the same final geometry can result from diff
arXiv:2603.21716v2 Announce Type: replace-cross Abstract: Efficient selection among multiple generative models is increasingly important in modern generative AI, where sampling from suboptimal models
arXiv:2608.28913v1 Announce Type: new Abstract: High-resolution 3D radar data is scarce. Commodity mmWave sensors use small antenna arrays that limit angular resolution to several degrees, and existin
arXiv:2608.29278v1 Announce Type: new Abstract: Omni-modal large language models are increasingly evaluated on clean text--vision--audio inputs, where every channel is present, synchronized, and readi
arXiv:2608.30366v1 Announce Type: new Abstract: The loss landscape of Deep Neural Networks (DNNs) exhibits highly complex and non-convex properties. Recent studies have revealed the phenomenon of mode
arXiv:2608.29547v1 Announce Type: new Abstract: Image based shape control provides a simple means of controlling the whole body configuration of soft robots. However, existing data driven approaches a
arXiv:2608.30636v1 Announce Type: new Abstract: Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning model
arXiv:2602.11793v2 Announce Type: replace-cross Abstract: Watermarking has emerged as a crucial technique for detecting and attributing content generated by large language models. While recent advance
arXiv:2608.29139v1 Announce Type: new Abstract: Learning effective multimodal entity representations is fundamental for reasoning tasks such as multimodal knowledge graph completion (MMKGC). However,
arXiv:2608.30279v1 Announce Type: new Abstract: Point cloud video representation learning is crucial for 3D dynamic scene understanding. In this paper, we propose MoSaiC, a novel Motion-Saliency Compl
arXiv:2608.30975v1 Announce Type: cross Abstract: Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep lear
arXiv:2510.01159v3 Announce Type: replace Abstract: Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applicati
arXiv:2608.30726v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) is a fundamental component of affective computing that aims to decipher complex emotional states by integrating verb
arXiv:2608.30262v1 Announce Type: new Abstract: Scientific simulations generate collections of physical fields with heterogeneous statistics and dependencies, yet learned compressors often encode thos
arXiv:2608.29074v1 Announce Type: new Abstract: We study online optimization with nested shrinking feasible regions in two settings: convex optimization with nested evolving feasible sets (CONES) and
arXiv:2608.29714v1 Announce Type: cross Abstract: Longitudinal cohort studies produce repeated data that enable the assessment of time-varying association patterns between exposures and health outcome
arXiv:2608.28608v1 Announce Type: cross Abstract: Ancient Indian medical texts like Sushruta Samhita have extensive information on diseases, treatments, and surgical techniques. Yet, their ancient for
arXiv:2608.29045v1 Announce Type: new Abstract: Biclustering, or co-clustering, aims to discover coherent submatrices by grouping rows and columns of a data matrix simultaneously. This local two-dimen
arXiv:2608.29568v1 Announce Type: cross Abstract: Different attack methods follow different search trajectories, they succeed on different subsets of samples, whereas existing hard-label black-box tex
arXiv:2608.30423v1 Announce Type: cross Abstract: Splatting-based algorithms reconstruct photorealistic, real-time-renderable, and mesh-exportable 3D scenes from regular images, but they represent a s
arXiv:2608.30616v1 Announce Type: new Abstract: Pottery is a primary source for reconstructing the chronological and economic dimensions of past societies. Archaeologists often document ceramic finds
arXiv:2607.19313v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on diffic
arXiv:2608.29904v1 Announce Type: new Abstract: Modern video generators routinely fail at physical dynamics: objects float, trajectories violate gravity, contacts vanish. Standard denoising and flow-m
arXiv:2608.31120v1 Announce Type: new Abstract: The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used to
arXiv:2608.29913v1 Announce Type: new Abstract: TinyML includes the implementation of machine learning on devices with limited memory and computing resources. With the development of technology, AI sy
arXiv:2511.19120v2 Announce Type: replace-cross Abstract: The structure of naming systems in natural languages hinges on a trade-off between high informativeness and low complexity. Focusing on the do
arXiv:2608.30756v1 Announce Type: cross Abstract: Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their
arXiv:2608.29943v1 Announce Type: cross Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to r
arXiv:2608.29944v1 Announce Type: new Abstract: Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learni
arXiv:2608.31017v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes under the reassurance that a clinician signs every note. We audited three commercial AI scribes on the same 14
arXiv:2608.30828v1 Announce Type: new Abstract: We present three large-scale studies of spoken parliamentary speech across four Slavic languages (Croatian, Czech, Polish, Serbian), drawing on over 6,0
arXiv:2608.30222v1 Announce Type: new Abstract: Hybrid systems like tilt-rotor bicopter drones combine the beneficial characteristics of both fixed-wing and rotary-wing technology, enabling long endur
arXiv:2608.29187v1 Announce Type: new Abstract: The point-based occupancy prediction paradigm has achieved an attractive trade-off between accuracy and efficiency by modeling 3D space sparsely. Howeve
arXiv:2603.17356v3 Announce Type: replace Abstract: Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While L
arXiv:2608.29753v1 Announce Type: new Abstract: Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read
arXiv:2608.30326v1 Announce Type: cross Abstract: Speech enhancement is often used as a front-end for robust ASR, yet recurrent temporal and cross-band modules introduce sequential dependencies that r
arXiv:2608.29996v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background c
arXiv:2608.28754v1 Announce Type: cross Abstract: This article introduces peer k-oversight, a property of sequential collective decision mechanisms requiring at least k agents to be responsible for ev
arXiv:2511.16830v4 Announce Type: replace Abstract: Recent studies show that text-to-image (T2I) diffusion models are vulnerable to backdoor attacks, where a trigger in the input prompt can steer gene
arXiv:2501.18018v2 Announce Type: replace-cross Abstract: The neurons of artificial neural networks were originally invented when much less was known about biological neurons than is known today. Our
arXiv:2608.29287v1 Announce Type: new Abstract: Shot boundary detection (SBD) is widely treated as the localisation of local visual discontinuities, yet many false positives such as hand-held shake, i
arXiv:2510.26444v2 Announce Type: replace-cross Abstract: Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is a critical task in precision medicin
arXiv:2608.30873v1 Announce Type: cross Abstract: LLMs are increasingly used for interpersonal advice and as tools for studying social behavior across languages and cultures. A common shortcut for eli
arXiv:2603.03485v4 Announce Type: replace-cross Abstract: Recent video diffusion models have achieved impressive capabilities as large-scale generative world models. However, these models often strugg
arXiv:2608.29549v1 Announce Type: cross Abstract: Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar g
arXiv:2601.14056v2 Announce Type: replace-cross Abstract: Training robust visual surveillance models requires large-scale datasets with precise spatial annotations, yet collecting real surveillance da
arXiv:2402.04355v4 Announce Type: replace-cross Abstract: We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generat
arXiv:2603.05575v2 Announce Type: replace-cross Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a black
arXiv:2608.30449v1 Announce Type: new Abstract: Click-through rate (CTR) models vary in feature-interaction design, yet their top networks usually remain a single multilayer perceptron shared by all e
arXiv:2608.30388v1 Announce Type: cross Abstract: Cross-view video representation learning aims to capture viewpoint-invariant action semantics despite substantial appearance changes across egocentric
arXiv:2603.09703v2 Announce Type: replace Abstract: Progressive transmission of 3D Gaussian Splatting (3DGS) requires each completed transmission stage to be decodable from received data and directly
arXiv:2603.17546v2 Announce Type: replace Abstract: Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual
arXiv:2608.30344v1 Announce Type: new Abstract: Most shape reconstruction methods assume measurements defined over planar sensing domains, such as RGB images or depth maps. In this paper, we use a cur
arXiv:2608.29765v1 Announce Type: new Abstract: Structured pruning uses surrogate objectives because direct task evaluation over every feasible mask is too expensive. Most evaluations report average s
arXiv:2608.28622v1 Announce Type: cross Abstract: Large language model training corpora grow through successive, often redundant releases, so each release must be deduplicated against both itself and
arXiv:2608.30652v1 Announce Type: new Abstract: Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embeddin
arXiv:2603.12165v3 Announce Type: replace Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult t
arXiv:2603.00620v3 Announce Type: replace Abstract: Multilingual NLP research increasingly involves hundreds or thousands of languages across different datasets. Managing, discovering, and reporting l