Knowledge catalogue
arXiv:2604.08045v1 Announce Type: new Abstract: Adnexal mass evaluation via ultrasound is a challenging clinical task, often hindered by subjective interpretation and significant inter-observer variab
arXiv:2604.07665v1 Announce Type: new Abstract: Self-supervised monocular depth estimation (MDE) has received increasing interests in the last few years. The objects in the scene, including the object
arXiv:2604.06518v1 Announce Type: cross Abstract: Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and ins
arXiv:2604.06699v1 Announce Type: cross Abstract: Automated prompt optimization is crucial for eliciting reliable reasoning from large language models (LLMs), yet most API-only prompt optimizers itera
arXiv:2512.10510v2 Announce Type: replace-cross Abstract: Offline-to-Online Reinforcement Learning (O2O RL) faces a critical dilemma in balancing the use of a fixed offline dataset with newly collecte
arXiv:2511.11159v2 Announce Type: replace Abstract: Many tasks in machine learning can be described as or reduced to learning a probability distribution given a finite set of samples. A common approac
arXiv:2604.08077v1 Announce Type: new Abstract: Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fi
arXiv:2512.24933v2 Announce Type: replace-cross Abstract: Multi-step LLM pipelines can solve complex tasks, but jointly optimizing prompts across steps remains challenging due to missing step-level su
arXiv:2604.08525v1 Announce Type: cross Abstract: Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginni
arXiv:2601.10587v2 Announce Type: replace Abstract: The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise
arXiv:2511.22475v2 Announce Type: replace-cross Abstract: We present adversarial flow models, a class of generative models that belongs to both the adversarial and flow families. Our method supports n
arXiv:2604.06289v1 Announce Type: cross Abstract: In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at
arXiv:2604.06475v1 Announce Type: new Abstract: Deep Learning Reduced Order Models (ROMs) are becoming increasingly popular as surrogate models for parametric partial differential equations (PDEs) due
arXiv:2604.07039v1 Announce Type: cross Abstract: Robotic systems lack a principled abstraction for organizing intelligence, capabilities, and execution in a unified manner. Existing approaches either
arXiv:2405.16240v3 Announce Type: replace Abstract: In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the f
arXiv:2604.08448v1 Announce Type: new Abstract: AfriVoices-KE is a large-scale multilingual speech dataset comprising approximately 3,000 hours of audio across five Kenyan languages: Dholuo, Kikuyu, K
arXiv:2604.07007v1 Announce Type: cross Abstract: Autonomous AI agents are beginning to operate across organizational boundaries on the open internet -- discovering, transacting with, and delegating t
arXiv:2604.06696v1 Announce Type: new Abstract: The rapid development of AI agent systems is leading to an emerging Internet of Agents, where specialized agents operate across local devices, edge node
arXiv:2604.06296v1 Announce Type: cross Abstract: AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has pr
arXiv:2604.08009v1 Announce Type: new Abstract: Autonomous indoor flight for critical asset inspection presents fundamental challenges in perception, planning, control, and learning. Despite rapid pro
arXiv:2604.07814v1 Announce Type: new Abstract: Accurate and interpretable plant disease diagnosis remains a major challenge for vision-language models (VLMs) in real-world agriculture. We introduce A
arXiv:2603.13354v3 Announce Type: replace-cross Abstract: Reliable crop disease detection requires models that perform consistently across diverse acquisition conditions, yet existing evaluations ofte
arXiv:2503.03088v4 Announce Type: replace-cross Abstract: The Segment Anything Model (SAM) has revolutionized image and video segmentation with its powerful zero-shot capabilities. However, its massiv
arXiv:2509.01878v2 Announce Type: replace-cross Abstract: Marine ecosystems face increasing pressure due to climate change, driving the need for scalable, AI-powered monitoring solutions to inform eff
arXiv:2604.06566v1 Announce Type: cross Abstract: As the complexity of modern workloads and hardware increasingly outpaces human research and engineering capacity, existing methods for database perfor
arXiv:2604.08479v1 Announce Type: new Abstract: Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM respons
arXiv:2604.03634v2 Announce Type: replace Abstract: We establish that temporal averaging over multiple observations is the degenerate case of algebraic group action with the trivial group G={e}. A
arXiv:2604.08133v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become a dominant architecture for scaling large language models due to their sparse activation mechanism. However, the s
arXiv:2510.20220v3 Announce Type: replace Abstract: Recent research has focused on mitigating algorithmic bias in clustering by incorporating fairness constraints into algorithmic design. Notions such
arXiv:2604.07169v1 Announce Type: cross Abstract: Bayesian filtering and smoothing for high-dimensional nonlinear dynamical systems are fundamental yet challenging problems in many areas of science an
arXiv:2604.07883v1 Announce Type: cross Abstract: History textbooks often contain implicit biases, nationalist framing, and selective omissions that are difficult to audit at scale. We propose an agen
arXiv:2306.02781v4 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in moder
arXiv:2604.07755v1 Announce Type: new Abstract: Despite extensive research, Large Language Models continue to hallucinate when generating code, particularly when using libraries. On NL-to-code benchma
arXiv:2604.06946v1 Announce Type: cross Abstract: Automated test case generation from natural language requirements remains a challenging problem in software engineering due to the ambiguity of requir
arXiv:2410.22177v2 Announce Type: replace-cross Abstract: As more applications of large language models (LLMs) for 3D content for immersive environments emerge, it is crucial to study user behaviour t
arXiv:2604.07053v2 Announce Type: replace Abstract: Recent feed-forward Gaussian reconstruction models adopt a pixel-aligned formulation that maps each 2D pixel to a 3D Gaussian, entangling Gaussian r
arXiv:2604.07277v1 Announce Type: cross Abstract: Online reinforcement learning (RL) serves as an effective method for enhancing the capabilities of Android agents. However, guiding agents to learn th
arXiv:2604.07900v1 Announce Type: new Abstract: Industrial anomaly generation is a crucial method for alleviating the data scarcity problem in anomaly detection tasks. Most existing anomaly synthesis
arXiv:2601.20524v2 Announce Type: replace Abstract: Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent
arXiv:2604.06454v1 Announce Type: cross Abstract: In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often
arXiv:2604.05351v3 Announce Type: replace-cross Abstract: Image Goal Navigation (ImageNav) is evaluated by a coarse success criterion, the agent must stop within 1m of the target, which is sufficient
arXiv:2505.06182v5 Announce Type: replace-cross Abstract: Active perception is a fundamental skill that enables us humans to deal with uncertainty in our inherently partially observable environment. F
arXiv:2510.20200v2 Announce Type: replace Abstract: Replicability, introduced by (Impagliazzo et al. STOC '22), is the notion that algorithms should remain stable under a resampling of their inputs (g
arXiv:2604.07282v1 Announce Type: cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be t
arXiv:2604.07831v1 Announce Type: cross Abstract: Existing red-teaming studies on GUI agents have important limitations. Adversarial perturbations typically require white-box access, which is unavaila
arXiv:2604.07096v1 Announce Type: new Abstract: Multi-objective bandits have attracted increasing attention because of their broad applicability and mathematical elegance, where the reward of each arm
arXiv:2504.10284v5 Announce Type: replace Abstract: Literature review tables are essential for summarizing and comparing collections of scientific papers. In this paper, we study the automatic generat
arXiv:2510.07927v2 Announce Type: replace Abstract: Anomaly detection plays a pivotal role in manufacturing quality control, yet its application is constrained by limited abnormal samples and high man
arXiv:2511.22396v2 Announce Type: replace-cross Abstract: Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (
arXiv:2604.07120v1 Announce Type: cross Abstract: Current operational Earth Observation (EO) services, including the Copernicus Emergency Management Service (CEMS), the European Forest Fire Informatio
arXiv:2604.06287v1 Announce Type: new Abstract: Mathematical models and numerical simulations offer a non-invasive way to explore cardiovascular phenomena, providing access to quantities that cannot b
arXiv:2604.07815v1 Announce Type: new Abstract: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory. While token-level sparse att
arXiv:2604.06710v1 Announce Type: new Abstract: We present ATANT (Automated Test for Acceptance of Narrative Truth), an open evaluation framework for measuring continuity in AI systems: the ability to
arXiv:2604.02022v2 Announce Type: replace Abstract: Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions
arXiv:2604.08070v1 Announce Type: new Abstract: Darija, the Moroccan Arabic dialect, is rich in visual content yet lacks specialized Optical Character Recognition (OCR) tools. This paper introduces At
arXiv:2604.07967v1 Announce Type: new Abstract: Adversarial claim rewriting is widely used to test fact-checking systems, but standard metrics fail to capture truth-conditional consistency and often l
arXiv:2604.06416v1 Announce Type: cross Abstract: Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We ev
arXiv:2604.06266v1 Announce Type: cross Abstract: Software-Defined Networking (SDN) improves network flexibility but also increases the need for reliable and interpretable intrusion detection. Large L
arXiv:2509.23435v2 Announce Type: replace-cross Abstract: The creation of high-quality multimodal datasets remains fundamental for advancing role-playing capabilities in large language models (LLMs).
arXiv:2506.06975v5 Announce Type: replace-cross Abstract: As API access becomes a primary interface to large language models (LLMs), users often interact with black-box systems that offer little trans