Knowledge catalogue
arXiv:2606.15157v2 Announce Type: replace-cross Abstract: KV cache eviction methods typically use a single retention-rule family throughout a model, making eviction-method identity a model-level desig
arXiv:2602.06578v2 Announce Type: replace-cross Abstract: Adversarial attacks against deep neural networks are commonly constructed under ell_p norm constraints, most often using p=1, p=2 or p=infty,
arXiv:2609.19827v1 Announce Type: new Abstract: With the widespread industrial deployment of Large Language Models (LLMs), DeepSearch has emerged as the dominant paradigm for resolving complex user qu
arXiv:2609.20175v1 Announce Type: cross Abstract: The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow
arXiv:2609.06025v2 Announce Type: replace Abstract: Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principl
arXiv:2607.09678v2 Announce Type: replace Abstract: When LLM agents hand information to one another, does the message format matter? Two literatures disagree: format-optimization work reports that str
arXiv:2609.19152v1 Announce Type: new Abstract: Misinformation detection tools often rely on binary true and false classifications or models trained on historical examples, limiting their usefulness w
arXiv:2609.20817v1 Announce Type: cross Abstract: Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Mo
arXiv:2609.19475v1 Announce Type: new Abstract: Diffusion-based Vision-Language-Action (VLA) models achieve strong performance in embodied tasks, but their iterative sampling imposes heavy computation
arXiv:2609.20341v1 Announce Type: new Abstract: Magnetic Resonance Imaging (MRI) acquired at different field strengths exhibits pronounced variation in noise, resolution, homogeneity, and contrast, wh
arXiv:2603.25244v2 Announce Type: replace Abstract: Adversarial attacks can readily compromise deep neural networks (DNNs). In particular, transferable attacks (TAs) exploit the shared vulnerabilities
arXiv:2609.20138v1 Announce Type: new Abstract: This work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying op
arXiv:2609.20067v1 Announce Type: new Abstract: Deep learning models for multi-modal breast cancer diagnosis achieve high predictive accuracy but remain clinically unacceptable without actionable, cou
arXiv:2609.19383v1 Announce Type: new Abstract: Counterfactual (CF) explanations identify changes that alter an input's classification. While existing methods produce realistic and low-cost CFs, they
arXiv:2609.19362v1 Announce Type: cross Abstract: We study high-order safety-critical control of quadrotor teams under bounded inputs and pairwise collision-avoidance constraints. Squared-distance bar
arXiv:2609.19740v1 Announce Type: new Abstract: Recent innovations in deep learning have significantly enhanced the diagnosis of medical images, although they are based on the use of centralized data
arXiv:2609.19202v1 Announce Type: cross Abstract: We study federated soft clustering over federated learning (FL) networks of devices that each hold a private local dataset and fit a personalized Gaus
arXiv:2609.20026v1 Announce Type: new Abstract: Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial c
arXiv:2609.19559v1 Announce Type: new Abstract: Heterogeneous federated learning requires clients with diverse computational capacities to collaboratively train a global model, where each client train
arXiv:2512.18809v3 Announce Type: replace-cross Abstract: Short-form video moderation is increasingly pushed toward edge and privacy-sensitive settings, where users may intend videos for a limited aud
arXiv:2609.19863v1 Announce Type: cross Abstract: Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapp
arXiv:2609.19549v1 Announce Type: cross Abstract: Communication is made possible by common ground---the unspoken knowledge that people share and presuppose of one another, whether that be online or of
arXiv:2609.20169v1 Announce Type: new Abstract: Next-Generation Sequencing has revolutionized the study of genetic mutations, enabling large-scale investigations into their roles in disease developmen
arXiv:2609.20457v1 Announce Type: cross Abstract: While multimodal large language models (MLLMs) enable a wide range of image-text reasoning tasks, recent incidents indicate that they are vulnerable t
arXiv:2609.19680v1 Announce Type: new Abstract: Financial QA systems are typically improved before deployment through better retrieval, prompting, or agent coordination, leaving their reliability beha
arXiv:2609.20078v1 Announce Type: new Abstract: Active visual exploration of tabletop objects often requires reorienting an unknown resting object onto a different stable support face to expose occlud
arXiv:2609.20769v1 Announce Type: new Abstract: Flow matching has emerged as the state-of-the-art generative model and has been used for plug-and-play (PnP) priors to solve inverse problems in computa
arXiv:2505.11939v3 Announce Type: replace-cross Abstract: Electrocardiograms (ECGs) are essential non-invasive tools for diagnosing cardiovascular diseases. While recent multimodal ECG-Report contrast
arXiv:2609.19504v1 Announce Type: cross Abstract: As model-generated content is increasingly consumed by other model instances in automated workflows, a practically important question arises: can a mo
arXiv:2503.15867v4 Announce Type: replace-cross Abstract: Forensic deepfake analysis demands more than binary classification: investigators need region-grounded natural language explanations they can
arXiv:2609.19589v1 Announce Type: cross Abstract: Data attribution methods using gradient similarity are widely used to analyze and select training data for large language models, but what gradient si
arXiv:2609.20207v1 Announce Type: new Abstract: Large language models produce prompt-dependent probabilities over words, whereas scientific systems require uncertainty over meaningful states that can
arXiv:2609.20535v1 Announce Type: new Abstract: Deep learning predictive maintenance models suffer from poor transferability across machines and operating conditions, especially when labelled data are
arXiv:2609.20340v1 Announce Type: new Abstract: Ultrasound image analysis plays a crucial role in cancer screening and prenatal diagnosis, yet comprehensive assessment requires jointly addressing task
arXiv:2609.19630v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into vehicle voice assistants. But linking natural-language requests to vehicle functions creat
arXiv:2609.19445v1 Announce Type: cross Abstract: The rapid expansion of multimodal models has surfaced formidable bottlenecks in computation, memory, and deployment, catalyzing the rise of Efficient
arXiv:2609.19553v1 Announce Type: new Abstract: Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This gr
arXiv:2609.19413v1 Announce Type: cross Abstract: Robot manipulation policies are improving quickly, and real-robot evaluation remains the standard evidence for that progress. It still relies on a hum
arXiv:2609.19928v1 Announce Type: new Abstract: Per-user LLM inference on transaction histories binds the inference budget linearly to user count, which becomes prohibitive at applied scale. We re-cas
arXiv:2609.19447v1 Announce Type: new Abstract: Robots that follow natural-language instructions in everyday indoor environments must act on incomplete human utterances. Instructions often omit essent
arXiv:2609.19596v1 Announce Type: new Abstract: Full-duplex speech models listen and speak at once, promising always-on assistants. Yet they must also decide when they should speak. Human listeners sp
arXiv:2609.20673v1 Announce Type: new Abstract: To operate effectively in human environments, robots must identify articulated objects, segment their movable and interactive parts, and estimate their
arXiv:2609.20088v1 Announce Type: new Abstract: Medical image segmentation supports quantitative clinical analysis and computer-aided diagnosis. Recent methods for medical image segmentation have impr
arXiv:2609.19716v1 Announce Type: new Abstract: Pre-trained 3D vision models have substantially advanced point cloud analysis, yet adapting them to downstream tasks via full fine-tuning is computation
arXiv:2609.19315v1 Announce Type: cross Abstract: Large language models (LLMs) provide a flexible interface for long-horizon robot planning, but generated plans often fail to respect embodiment constr
arXiv:2507.10059v2 Announce Type: replace Abstract: Large Language Models have achieved remarkable performance across a large number of tasks, but face critical deployment and usage barriers due to su
arXiv:2609.19887v1 Announce Type: new Abstract: Pronouns, adverbs and other functional words (such as they, her, somewhere, there) are often used in language to replace concrete nouns or phrases, when
arXiv:2609.20358v1 Announce Type: new Abstract: Characterizing the physical properties of clay and cementitious materials matters across many fields, from materials science to geological waste disposa
arXiv:2609.19209v1 Announce Type: new Abstract: Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challeng
arXiv:2609.20262v1 Announce Type: new Abstract: Nearly every acquisition function for active object detection shares one arrangement, in that the model being improved is also the model being interroga
arXiv:2609.20776v1 Announce Type: cross Abstract: Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fi
arXiv:2604.02745v2 Announce Type: replace Abstract: Radar odometry is crucial for robust localization in challenging environments; however, the sparsity of reliable returns and distinctive noise chara
arXiv:2609.20005v1 Announce Type: new Abstract: People increasingly turn to AI chatbots for news and explanations of world events. But do they receive the same political answers when they ask in diffe
arXiv:2609.19452v1 Announce Type: cross Abstract: Robots are moving out of the structured factory floor and into unstructured environments such as disaster sites, planetary surfaces, and agricultural
arXiv:2609.20016v1 Announce Type: cross Abstract: The EU AI Act (Regulation 2024/1689) imposes technical obligations on high-risk AI providers, yet Articles 8-15 were drafted for predictive AI and lea
arXiv:2609.20116v1 Announce Type: new Abstract: We study GPT-6-Astra in a zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) system, where it interprets instructions, assesse
arXiv:2609.19850v1 Announce Type: new Abstract: Actor-critic architecture has been widely used in continuous robot control. However, they rely on learning a value network, introducing additional compu
arXiv:2609.20087v1 Announce Type: new Abstract: Effective manipulation across diverse objects is critical for applications ranging from agricultural harvesting to laboratory and domestic automation. W
arXiv:2609.19956v1 Announce Type: new Abstract: Tree-based Monte-Carlo Tree Search (MCTS) duplicates the same state when it is reached through different trajectories, which can waste simulations in st
arXiv:2609.19228v1 Announce Type: new Abstract: Dexterous robot hands must often grasp objects whose shape, size, and pose are known only approximately. Grasp planners typically require accurate objec