Knowledge catalogue
arXiv:2604.27182v1 Announce Type: cross Abstract: Time-series data augmentation plays a crucial role in regression-oriented forecasting tasks, where limited data restricts the performance of deep lear
arXiv:2503.01835v2 Announce Type: replace Abstract: Transformers have achieved remarkable success across multiple fields, yet their impact on 3D medical image segmentation remains limited with convolu
arXiv:2604.28123v1 Announce Type: cross Abstract: The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinfo
arXiv:2604.27598v1 Announce Type: new Abstract: Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires
arXiv:2604.27379v1 Announce Type: new Abstract: Dialogue models are inherently reactive, responding to the current user turn without anticipating upcoming intents, which leads to redundant interaction
arXiv:2604.27814v1 Announce Type: new Abstract: Joint probabilistic modeling is essential for forecasting irregular multivariate time series (IMTS) to accurately quantify uncertainty. Existing approac
arXiv:2604.27349v1 Announce Type: cross Abstract: The increasing dependency among Filipino college students on artificial intelligence (AI) poses concerns about the potential decline of fundamental ac
arXiv:2602.07408v2 Announce Type: replace Abstract: Predicting gene regulation responses to biological perturbations requires reasoning about underlying biological causalities. While large language mo
arXiv:2604.28055v1 Announce Type: cross Abstract: Individualized Alzheimer's disease (AD) progression prediction requires models that use irregular visits, account for censoring, avoid diagnostic leak
arXiv:2604.27472v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models advance robotic control via strong visual-linguistic priors. However, existing VLAs predominantly frame pretraining
arXiv:2512.07703v2 Announce Type: replace Abstract: Large foundation models have emerged in the last years and are pushing performance boundaries for a variety of tasks. Training or even finetuning su
arXiv:2604.27533v1 Announce Type: new Abstract: Evaluating automatic speech recognition (ASR) systems is a classical but difficult and still open problem, which often boils down to focusing only on th
arXiv:2511.17372v2 Announce Type: replace-cross Abstract: Classical autoencoders are widely used to learn features of input data. To improve the feature learning, classical masked autoencoders extend
arXiv:2604.27450v1 Announce Type: cross Abstract: Dense, dynamic crowds pose a persistent challenge for autonomous mobile robots. Purely reactive planning methods, such as Model Predictive Path Integr
arXiv:2604.27702v1 Announce Type: new Abstract: Video snapshot compressive imaging (SCI) enables the reconstruction of dynamic scenes from a single snapshot measurement. Recently, NeRF-based methods h
arXiv:2604.27193v1 Announce Type: new Abstract: Automatic Emergency Braking (AEB) systems represent a safety-critical national interest, with the National Highway Traffic Safety Administration (NHTSA)
arXiv:2505.19630v4 Announce Type: replace Abstract: Large language models (LLMs) struggle in real-world clinical consultations. Single-turn consultation systems require patients to describe all sympto
arXiv:2604.27850v1 Announce Type: new Abstract: Task-based dialogue systems assist users in achieving specific goals, such as executing actions or retrieving information, through natural language inte
arXiv:2604.27319v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in recent years, driving their adoption across a wide range of domains, including compu
arXiv:2604.27106v1 Announce Type: cross Abstract: Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward
arXiv:2509.09513v2 Announce Type: replace-cross Abstract: Biophysical diffusion MRI models like Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compart
arXiv:2604.27233v1 Announce Type: new Abstract: Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-ho
arXiv:2604.28028v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized Text-to-SQL generation, allowing users to query structured data using natural language with growing e
arXiv:2604.27234v1 Announce Type: new Abstract: Remaining Useful Life (RUL) estimation is a critical component of Prognostics and Health Management (PHM), enabling proactive maintenance scheduling and
arXiv:2604.28075v1 Announce Type: cross Abstract: Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, fo
arXiv:2604.28190v1 Announce Type: new Abstract: We show that Frechet Distance (FD), long considered impractical as a training objective, can in fact be effectively optimized in the representation spac
arXiv:2604.27323v1 Announce Type: cross Abstract: Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their eff
arXiv:2604.27552v1 Announce Type: new Abstract: While 3D Gaussian splatting (3DGS) offers explicit and efficient scene representations for cone-beam computed tomography reconstruction, conventional ph
arXiv:2604.28025v1 Announce Type: new Abstract: Single-image human mesh recovery provides a compact 3D, person-centric representation that supports analysis, animation, AR and VR, rehabilitation, and
arXiv:2604.27859v1 Announce Type: new Abstract: Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined env
arXiv:2509.18308v3 Announce Type: replace Abstract: Pulmonary Embolism (PE) is a life-threatening condition for which accurate and timely detection is critical to patient care. However, our systematic
arXiv:2604.27553v1 Announce Type: new Abstract: When the visual style of text is considered, a wide variety can be observed in font, color, and size. However, when a word is read, its meaning is indep
arXiv:2604.27504v1 Announce Type: new Abstract: Recent generative models have shown strong performance in generating diverse 3D assets from 2D images, a fundamental research topic in computer vision a
arXiv:2604.28056v1 Announce Type: new Abstract: Large language models (LLMs) make reward design in reinforcement learning substantially more scalable, but generated rewards are not automatically relia
arXiv:2604.27559v1 Announce Type: cross Abstract: Radiology report generation (RRG) has emerged as a promising approach to alleviate radiologists' workload and reduce human errors by automatically gen
arXiv:2604.27616v1 Announce Type: new Abstract: People commonly leverage structured content to accelerate knowledge acquisition and research problem solving. Among these, roadmaps guide researchers th
arXiv:2604.27621v1 Announce Type: cross Abstract: A critical bottleneck hindering further advancement in embodied AI and robotics is the challenge of scaling robot data. To address this, the field of
arXiv:2604.26988v1 Announce Type: new Abstract: Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unfores
arXiv:2604.27387v1 Announce Type: new Abstract: Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes of different types and
arXiv:2604.27617v1 Announce Type: cross Abstract: With the widespread application of Unmanned Aerial Vehicles (UAVs) in bridge structural health monitoring, deep learning-based automatic crack detecti
arXiv:2604.28161v1 Announce Type: new Abstract: The robotic manipulation of Deformable Linear Objects (DLOs) is a fundamental challenge due to the high-dimensional, non-linear dynamics of flexible str
arXiv:2601.14289v2 Announce Type: replace-cross Abstract: Understanding research papers remains challenging for foundation models due to specialized scientific discourse and complex figures and tables
arXiv:2604.27780v1 Announce Type: cross Abstract: Large Language Models (LLMs) have rapidly improved in performance across code-related tasks, making their integration into Register Transfer Level (RT
arXiv:2604.27358v1 Announce Type: new Abstract: As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents
arXiv:2602.20549v2 Announce Type: replace-cross Abstract: The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measuremen
arXiv:2604.27447v1 Announce Type: cross Abstract: Modern stochastic optimization pipelines increasingly rely on learned generative models to represent uncertainty, while downstream decisions are evalu
arXiv:2604.27738v1 Announce Type: cross Abstract: Autoregressive Neural Networks based on dense or convolutional layers have recently been shown to be a viable strategy for generating classical spin s
arXiv:2604.27508v1 Announce Type: new Abstract: Understanding human actions is critical for advancing behavior analysis in human-robot interaction. Particularly in tasks that demand quick and proactiv
arXiv:2604.27467v1 Announce Type: cross Abstract: Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback
arXiv:2604.27025v1 Announce Type: cross Abstract: Automatic feature engineering is an effective approach for improving predictive performance in tabular learning. However, expand-and-reduce methods, s
arXiv:2604.27596v1 Announce Type: new Abstract: In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practica
arXiv:2604.27426v1 Announce Type: cross Abstract: Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although ''local offline
arXiv:2604.27464v1 Announce Type: cross Abstract: Autonomous agent frameworks built upon large language models (LLMs) are evolving into complex, tool-integrated, and continuously operating systems, in
arXiv:2604.27204v1 Announce Type: new Abstract: In the field of universal automatic phonetic transcription (APT), clean and diverse training transcriptions are required. However, such high-quality dat
arXiv:2604.27264v1 Announce Type: cross Abstract: Autonomous agents can adapt their behaviour to changing environments, but remain bound to requirements, goals, and capabilities fixed at design time,
arXiv:2604.27538v1 Announce Type: new Abstract: Automated plant recognition plays a crucial role in biodiversity monitoring and conservation, yet current approaches rely heavily on supervised learning
arXiv:2604.27169v1 Announce Type: new Abstract: We show that the geometric relations between semantic features in large language models' hidden states closely mirror human psychological associations.
arXiv:2408.13122v2 Announce Type: replace-cross Abstract: The Variational Bayesian method (VB) is used to solve the probability distributions of latent variables with the minimum free energy criterion
arXiv:2604.27439v1 Announce Type: new Abstract: This study analyzes Indonesian student opinions on the adoption of artificial intelligence in higher education using two approaches: TF-IDF-based machin
arXiv:2604.28163v1 Announce Type: cross Abstract: The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in