Knowledge catalogue
arXiv:2604.22367v1 Announce Type: cross Abstract: Sign language research has achieved significant progress due to the advances in large language models (LLMs). However, the intrinsic ability of LLMs t
arXiv:2604.22238v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models promise generalist robot manipulation, but are typically trained and deployed as short-horizon policies that assume
arXiv:2604.22428v1 Announce Type: new Abstract: Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tool
arXiv:2604.22140v1 Announce Type: cross Abstract: We study stochastic multi-armed bandits in which the objective is a statistical functional of the long-run reward distribution, rather than expected r
arXiv:2604.21956v1 Announce Type: new Abstract: Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detect
arXiv:2604.21960v1 Announce Type: cross Abstract: Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, t
arXiv:2604.22391v1 Announce Type: cross Abstract: The Super Learner (SL) is a widely used ensemble method that combines predictions from a library of learners based on their predictive performance. In
arXiv:2603.15017v3 Announce Type: replace Abstract: Because human preferences are too complex to codify, AIs operate with misspecified objectives. Optimizing such objectives often produces undesirable
arXiv:2604.22335v1 Announce Type: new Abstract: Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithf
arXiv:2512.20831v2 Announce Type: replace Abstract: Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisio
arXiv:2604.22294v1 Announce Type: cross Abstract: Real-world document question answering is challenging. Analysts must synthesize evidence across multiple documents and different parts of each documen
arXiv:2604.22477v1 Announce Type: new Abstract: Neuron labeling assigns textual descriptions to internal units of deep networks. Existing approaches typically rely on highly activating examples, often
arXiv:2604.22542v1 Announce Type: cross Abstract: Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. T
arXiv:2511.13193v2 Announce Type: replace Abstract: Multi-agent systems (MAS) built on large language models (LLMs) often suffer from inefficient 'free-for-all' communication, leading to exponential t
arXiv:2604.22693v1 Announce Type: cross Abstract: Selecting a small, high-quality subset from a large corpus for fine-tuning is increasingly important as corpora grow to tens of millions of datapoints
arXiv:2601.19674v2 Announce Type: replace-cross Abstract: Ambitious decarbonisation targets are rapidly increasing the commission of new offshore wind farms. For these newly commissioned plants to run
arXiv:2601.19963v2 Announce Type: replace-cross Abstract: Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this chal
arXiv:2604.22560v1 Announce Type: cross Abstract: Graph Visual Question Answering (GVQA) for autonomous driving organizes reasoning into ordered stages, namely Perception, Prediction, and Planning, wh
arXiv:2410.13713v2 Announce Type: replace Abstract: Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique nece
arXiv:2507.04535v2 Announce Type: replace-cross Abstract: Neural networks with a latency requirement on the order of microseconds, like the ones used at the CERN Large Hadron Collider, are typically d
arXiv:2411.07378v3 Announce Type: replace Abstract: Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention with
arXiv:2604.22562v1 Announce Type: cross Abstract: Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair rewards. Current methods
arXiv:2604.22499v1 Announce Type: new Abstract: Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses
arXiv:2510.19592v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To direct
arXiv:2604.22496v1 Announce Type: new Abstract: In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at d
arXiv:2604.22331v1 Announce Type: new Abstract: This study analyses simulated and real-world implementations of depth-aware rover navigation, highlighting the transition from stereo vision to monocula
arXiv:2603.00177v2 Announce Type: replace-cross Abstract: The proliferation of AI-generated text has intensified the need for reliable authorship verification, yet current output-based methods are inc
arXiv:2604.22629v1 Announce Type: cross Abstract: This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained ac
arXiv:2604.22606v1 Announce Type: new Abstract: Health misinformation remains one of the most pressing challenges on social media, particularly when cultural traditions intersect with scientific-sound
arXiv:2604.22515v1 Announce Type: new Abstract: Handwritten Arabic manuscripts preserve the Arab world's intellectual and cultural heritage, and writer identification supports provenance, authenticity
arXiv:2511.10571v2 Announce Type: replace Abstract: Hidden Markov Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Class
arXiv:2601.23022v3 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world
arXiv:2604.22128v1 Announce Type: new Abstract: When trained on tasks requiring an understanding of hierarchical structure, transformers have been found to represent this hierarchy in distinct ways: i
arXiv:2604.22413v1 Announce Type: cross Abstract: Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while ot
arXiv:2604.22529v1 Announce Type: new Abstract: Self-supervised learning has achieved remarkable success in learning visual representations from clean data, yet remains challenging when clean observat
arXiv:2604.22014v1 Announce Type: cross Abstract: We present DM^3-Nav, a fully decentralized multi-agent semantic navigation system supporting multimodal open-vocabulary goal specification and multi-o
arXiv:2604.22110v1 Announce Type: new Abstract: Standard supervised classification trains models to imitate the exact labels provided by a perfect oracle. This imitation happens in a single pass, rest
arXiv:2604.22281v1 Announce Type: new Abstract: Recent advances in vision-language models have demonstrated remarkable performance across diverse multi-modal tasks, including document question answeri
arXiv:2604.00813v3 Announce Type: replace-cross Abstract: End-to-end autonomous driving has evolved from the conventional paradigm based on sparse perception into vision-language-action (VLA) models,
arXiv:2604.22152v1 Announce Type: new Abstract: Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for
arXiv:2604.22104v1 Announce Type: new Abstract: An asymmetric two-link robot supported atop a flat platform by wheels that roll and pivot freely, but do not slip laterally, will develop forward moment
arXiv:2604.22325v1 Announce Type: new Abstract: Existing Natural Language Processing (NLP) resources often lack the task-specific information required for real-world problems and provide limited cover
arXiv:2512.05859v2 Announce Type: replace Abstract: Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater a
arXiv:2604.22379v1 Announce Type: new Abstract: Recent advances in distilling expensive diffusion models into efficient few-step generators show significant promise. However, these methods typically d
arXiv:2502.00955v2 Announce Type: replace Abstract: Monte Carlo Tree Search (MCTS) based methods provide promising approaches for generating synthetic data to enhance the self-training of Large Langua
arXiv:2604.22036v1 Announce Type: cross Abstract: This paper introduces EgoMAGIC (Medical Assistance, Guidance, Instruction, and Correction), an egocentric medical activity dataset collected as part o
arXiv:2602.02689v2 Announce Type: replace-cross Abstract: We propose Eidolon, a post-quantum signature scheme grounded on the NP-complete k-colorability problem. Our construction generalizes the Goldr
arXiv:2604.22119v1 Announce Type: new Abstract: As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own ob
arXiv:2604.22189v1 Announce Type: new Abstract: This letter presents an energy-efficient multi-robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing
arXiv:2603.10093v2 Announce Type: replace-cross Abstract: Recent 3D molecular generation methods primarily use asynchronous auto-regressive or synchronous diffusion models. While auto-regressive model
arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increa
arXiv:2604.22089v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other contents (e.g., images) h
arXiv:2604.22595v1 Announce Type: new Abstract: CLIP has demonstrated strong generalization in visual domains through natural language supervision, even for video action recognition. However, most exi
arXiv:2604.22207v1 Announce Type: cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate
arXiv:2604.22296v1 Announce Type: new Abstract: Synthetic image generation is one of the crucial input for planetary missions. It enables researchers and engineers to visualize planned planetary missi
arXiv:2604.22183v1 Announce Type: new Abstract: Achieving sharp 3D reconstruction from motion-blurred images alone becomes challenging, motivating recent methods to incorporate event cameras, benefiti
arXiv:2603.24350v2 Announce Type: replace-cross Abstract: A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a 'sel
arXiv:2604.22539v1 Announce Type: new Abstract: Thematic maps play a central role in academic communication, yet their large-scale design evolution has rarely been examined empirically. This study pre
arXiv:2406.14111v2 Announce Type: replace-cross Abstract: Expander decompositions of graphs have significantly advanced the understanding of many classical graph problems and led to numerous fundament
arXiv:2604.22580v1 Announce Type: cross Abstract: As the demand to integrate Artificial Intelligence into high-stakes environments continues to grow, explaining the reasoning behind neural-network pre