Knowledge catalogue
arXiv:2606.02552v1 Announce Type: cross Abstract: Despite advances in depth estimation, flying points remain a persistent failure mode: near object boundaries, depth estimators often predict spurious
arXiv:2606.00162v1 Announce Type: cross Abstract: Robotic systems generate large volumes of multimodal sensor data, but converting ROS bag recordings into machine learning datasets is often handled by
arXiv:2606.00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectr
arXiv:2606.02491v1 Announce Type: new Abstract: We present MORPHOS, a novel autoregressive framework that generates dynamic 3D assets from videos across diverse representations, including meshes, 3D G
arXiv:2606.01846v1 Announce Type: new Abstract: Mosquito-borne infectious diseases cause more than 700000 deaths worldwide each year. The long-term use of conventional chemical insecticides has induce
arXiv:2606.01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware questio
arXiv:2606.00107v1 Announce Type: cross Abstract: Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic. Clinical practice relie
arXiv:2606.01538v1 Announce Type: cross Abstract: To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (M
arXiv:2601.16884v3 Announce Type: replace Abstract: We study multigrade deep learning (MGDL) as a principled framework for structured error refinement in deep neural networks. While the approximation
arXiv:2606.02147v1 Announce Type: cross Abstract: Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring c
arXiv:2606.01800v1 Announce Type: cross Abstract: Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data, even though English d
arXiv:2601.21959v2 Announce Type: replace-cross Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided test
arXiv:2606.01078v1 Announce Type: new Abstract: Transport MCMC trains a normalizing flow to precondition Metropolis--Hastings proposals, achieving high empirical efficiency on challenging posteriors;
arXiv:2411.11793v2 Announce Type: replace Abstract: In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients m
arXiv:2606.02042v1 Announce Type: new Abstract: Continual industrial anomaly detection with diffusion models suffers from historical normality prior drift and catastrophic forgetting. Existing continu
arXiv:2606.00557v1 Announce Type: new Abstract: To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insuff
arXiv:2606.02430v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through di
arXiv:2606.01148v1 Announce Type: new Abstract: Natural-language explanations are often treated as a unified interface for understanding model behavior, but different explanation sources may support s
arXiv:2606.02493v1 Announce Type: new Abstract: Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions, where users are sensitive to how responses
arXiv:2606.00683v1 Announce Type: new Abstract: Recent progress in the development of language models has been defined by scale, with each generation absorbing more of the world's knowledge into its w
arXiv:2606.00661v1 Announce Type: cross Abstract: We establish the finite-sample concentration rate for the Median-of-Incomplete-U-Statistics (MIU), an efficient robust estimator for the expectation o
arXiv:2512.10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities construction
arXiv:2606.00102v1 Announce Type: new Abstract: Over the centuries, probability theory has grown from the calculus of games of chance into a central framework for reasoning under uncertainty. This art
arXiv:2606.00568v1 Announce Type: new Abstract: Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it
arXiv:2606.02158v1 Announce Type: new Abstract: AI-generated text increasingly blends with human writing, raising practical risks such as misinformation, academic misuse, and corpora contamination. Wh
arXiv:2606.01427v1 Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many
arXiv:2602.09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of Lamb
arXiv:2606.00835v1 Announce Type: new Abstract: Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, ev
arXiv:2412.10362v2 Announce Type: replace-cross Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-cond
arXiv:2606.00956v1 Announce Type: new Abstract: This paper studies a one-step lookahead Bayesian optimization (BO) method and its theoretical guarantee. Although the empirical effectiveness of one-ste
arXiv:2310.20545v3 Announce Type: replace Abstract: We present a multi-task optimization approach based on a deep learning architecture for time series forecasting. We leverage large collections of ti
arXiv:2606.00499v1 Announce Type: new Abstract: Video generation models are becoming a scalable form of world models, but they mainly generate plausible motion rather than proactively control or optim
arXiv:2606.00265v1 Announce Type: cross Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme
arXiv:2606.01537v1 Announce Type: new Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data. We present
arXiv:2606.01399v1 Announce Type: new Abstract: We present PAI-Studio, a new reference-conditioned video synthesis task that addresses a long-standing challenge in cinematic background replacement: ge
arXiv:2606.01947v1 Announce Type: cross Abstract: Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art re
arXiv:2606.01543v1 Announce Type: new Abstract: Data scarcity in multimodal pathology motivates unified generative models that synthesize modality-specific appearance while preserving anatomically coh
arXiv:2602.12080v2 Announce Type: replace Abstract: Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation. Although prior work has ex
arXiv:2606.01188v1 Announce Type: cross Abstract: Translating natural-language hardware requirements into correct printed circuit board (PCB) schematics remains difficult in embedded, IoT, and wearabl
arXiv:2606.01469v1 Announce Type: new Abstract: The development of automatic term extraction has become increasingly important in modern technology. Automatic term extraction can be found in virtually
arXiv:2604.05634v2 Announce Type: replace Abstract: Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation. While existing MU methods are effective, mos
arXiv:2606.00253v1 Announce Type: cross Abstract: Fine-tuning Vision-Language-Action (VLA) models for mobile manipulators with heterogeneous joint spaces can produce a counterintuitive result: the che
arXiv:2606.02021v1 Announce Type: new Abstract: Can a visually plausible food mesh be trusted to estimate the volume of consumed food? method investigates this question using selected paired before- a
arXiv:2602.16745v2 Announce Type: replace-cross Abstract: Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-tim
arXiv:2507.18863v2 Announce Type: replace-cross Abstract: Visual Automatic Speech Recognition (V-ASR) is a challenging task that involves interpreting spoken language solely from visual information, s
arXiv:2606.00439v1 Announce Type: new Abstract: A central challenge in visual intelligence is learning the physical structure of scenes from raw videos: how regions form objects and the laws that gove
arXiv:2606.01652v1 Announce Type: cross Abstract: Recently, partial differential equations (PDEs) have been used to directly model the measurement process in signal processing, although their evaluati
arXiv:2606.02278v1 Announce Type: cross Abstract: State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inacc
arXiv:2606.01572v1 Announce Type: cross Abstract: Predicting patient-specific facial soft-tissue deformation is critical for iterative orthognathic surgery planning. However, current computational met
arXiv:2603.06453v2 Announce Type: replace Abstract: While recent image generation models demonstrate a remarkable ability to handle a wide variety of image generation tasks, this flexibility makes the
arXiv:2606.02010v1 Announce Type: cross Abstract: PlanarBench tests whether LLMs can draw planar graphs as ASCII art given only an edge list -- a spatial reasoning task that resists memorization becau
arXiv:2606.00080v1 Announce Type: cross Abstract: Marine plankton underpin aquatic food webs and play a key role in global CO2 sequestration, making reliable species identification critical for unders
arXiv:2606.01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression resp
arXiv:2606.01049v1 Announce Type: new Abstract: Large-scale biomedical image-text datasets extracted from scientific literature provide valuable resources for medical multimodal model training. These
arXiv:2606.01858v1 Announce Type: new Abstract: Users increasingly expect image generation models to quickly adapt to highly diverse and personalized requirements, such as producing images with distin
arXiv:2602.00415v2 Announce Type: replace Abstract: Memory is not merely a storage mechanism for intelligent systems, but a structure for organizing evidence and constraining belief. This is especiall
arXiv:2502.20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse on sustainable artificial intellige
arXiv:2606.00124v1 Announce Type: new Abstract: Positional embeddings (PEs) in Vision Transformers (ViTs) are known to impact performance and robustness, but their role in shaping internal spatial rep
arXiv:2603.14798v2 Announce Type: replace-cross Abstract: We propose a machine-learning algorithm for Bayesian inverse problems in the function-space regime. Based on one-step generative transport, th
arXiv:2606.02366v1 Announce Type: new Abstract: We present PRIMA (*PRI*ors for *M*esh *A*daptation), a framework for robust 3D quadruped mesh recovery under severe species and pose imbalance. Existing