Knowledge catalogue
arXiv:2604.15408v1 Announce Type: cross Abstract: Token pruning methods for Vision Transformers (ViTs) promise quadratic reductions in attention FLOPs by dropping uninformative patches. Yet when prune
arXiv:2510.21977v2 Announce Type: replace Abstract: Large language models (LLMs) offer a promising way to simulate human survey responses, potentially reducing the cost of large-scale data collection.
arXiv:2604.15866v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER), yet their ge
arXiv:2510.09033v3 Announce Type: replace Abstract: Recent work suggests that LLMs 'know what they don't know', positing that hallucinated and factually correct outputs arise from distinct internal pr
arXiv:2604.16256v1 Announce Type: cross Abstract: Reasoning in vision-language models (VLMs) has recently attracted significant attention due to its broad applicability across diverse downstream tasks
arXiv:2604.15851v1 Announce Type: cross Abstract: Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-leve
arXiv:2512.23421v3 Announce Type: replace Abstract: World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the rea
arXiv:2604.16047v1 Announce Type: cross Abstract: The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accel
arXiv:2604.15865v1 Announce Type: new Abstract: Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition betwe
arXiv:2604.16104v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential fo
arXiv:2509.09530v2 Announce Type: replace Abstract: Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost a
arXiv:2506.13215v2 Announce Type: replace Abstract: Recently, patch deformation-based methods have demonstrated significant effectiveness in multi-view stereo due to their incorporation of deformable
arXiv:2505.16176v2 Announce Type: replace Abstract: In mathematical reasoning, data selection strategies predominantly rely on static, externally defined metrics, which fail to adapt to the evolving c
arXiv:2512.17052v4 Announce Type: replace Abstract: Function calling agents powered by Large Language Models (LLMs) select external tools to automate complex tasks. On-device agents typically use a re
arXiv:2506.03103v2 Announce Type: replace Abstract: Reconstructing dynamic hand-object contacts is essential for realistic manipulation in AI character animation, XR, and robotics, yet it remains chal
arXiv:2604.16082v1 Announce Type: cross Abstract: Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a ch
arXiv:2604.15822v1 Announce Type: cross Abstract: Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compare
arXiv:2509.14977v2 Announce Type: replace Abstract: Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, a
arXiv:2604.15327v1 Announce Type: cross Abstract: Universities are microcosms of urban ecosystems, with concentrated consumption patterns in food, transport, energy, and product usage. These environme
arXiv:2604.15911v1 Announce Type: new Abstract: Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative video synthesis, but their practical deployment remains co
arXiv:2604.16044v1 Announce Type: new Abstract: Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these mo
arXiv:2511.14791v2 Announce Type: replace-cross Abstract: Early detection of faults in district heating substations is imperative to reduce return temperatures and enhance efficiency. However, progres
arXiv:2604.16238v1 Announce Type: new Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such for
arXiv:2604.16284v1 Announce Type: new Abstract: Atmospheric haze significantly degrades wildlife imagery, impeding computer vision applications critical for conservation, such as animal detection, tra
arXiv:2503.13543v2 Announce Type: replace-cross Abstract: Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL
arXiv:2604.15864v1 Announce Type: new Abstract: Solid-state LiDAR-inertial SLAM has attracted significant attention due to its advantages in speed and robustness. However, achieving accurate mapping i
arXiv:2601.05808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich a
arXiv:2409.01794v2 Announce Type: replace-cross Abstract: In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum
arXiv:2604.15461v1 Announce Type: cross Abstract: LLM-based simulators offer a promising path for generating complex synthetic data where traditional differentially private (DP) methods struggle with
arXiv:2604.15329v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human responses in behavioral research, yet it remains unclear when LLM-generated data
arXiv:2604.15961v1 Announce Type: new Abstract: There is no consensus in the field of synthetic data on concise metrics for quality evaluations or benchmarks on large health datasets, such as historic
arXiv:2604.16279v1 Announce Type: new Abstract: Large Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse s
arXiv:2411.18328v2 Announce Type: replace Abstract: Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with tradition
arXiv:2604.15787v1 Announce Type: cross Abstract: We introduce EVIL (extbf{EV}olving extbf{I}nterpretable algorithms with extbf{L}LMs), an approach that uses LLM-guided evolutionary search to discover
arXiv:2510.13220v2 Announce Type: replace Abstract: A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like 'clever but cl
arXiv:2509.11982v2 Announce Type: replace Abstract: Financial bubbles often arrive without much warning, but create long-lasting economic effects. For example, during the dot-com bubble, innovative te
arXiv:2604.15380v1 Announce Type: cross Abstract: We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-p
arXiv:2604.15588v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However,
arXiv:2604.15560v1 Announce Type: cross Abstract: NASA's Transiting Exoplanet Survey Satellite (TESS) has identified thousands of exoplanet candidates, yet many remain unconfirmed due to the limitatio
arXiv:2604.15877v1 Announce Type: new Abstract: As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory
arXiv:2604.15319v1 Announce Type: cross Abstract: Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space (typically 2D or 3D), based on which visualiza
arXiv:2507.18756v2 Announce Type: replace Abstract: Multi-Armed Bandit (MAB) algorithms are widely used in recommender systems that require continuous, incremental learning. A core aspect of MABs is t
arXiv:2604.15388v1 Announce Type: cross Abstract: Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, tr
arXiv:2604.15841v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative
arXiv:2604.15336v1 Announce Type: cross Abstract: Large language models (LLMs) enable increasingly capable tutoring-style conversational agents, yet effective tutoring requires sensitivity to learners
arXiv:2604.15619v1 Announce Type: new Abstract: Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of suc
arXiv:2510.13920v2 Announce Type: replace Abstract: Query-focused table summarization requires generating natural language summaries of tabular data conditioned on a user query, enabling users to acce
arXiv:2505.21072v4 Announce Type: replace Abstract: Large Language Models (LLMs) enhanced with retrieval, an approach known as Retrieval-Augmented Generation (RAG), have achieved strong performance in
arXiv:2604.15672v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates language model inference by drafting tokens from a cheap proposal model and verifying them against an expensive
arXiv:2604.15646v1 Announce Type: new Abstract: Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet wr
arXiv:2604.15795v1 Announce Type: new Abstract: 3D object detection models trained in one server plays an important role in autonomous driving, robotics manipulation, and augmented reality scenarios.
arXiv:2604.15775v1 Announce Type: new Abstract: Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mode
arXiv:2512.22278v2 Announce Type: replace Abstract: The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal h
arXiv:2604.16243v1 Announce Type: new Abstract: Reinforcement learning has advanced video reasoning in large multi-modal models, yet dominant pipelines either rely on on-policy self-exploration, which
arXiv:2604.16298v1 Announce Type: new Abstract: UAV vision-language navigation (VLN) requires an agent to navigate complex 3D environments from an egocentric perspective while following ambiguous mult
arXiv:2604.15488v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offer
arXiv:2604.16265v1 Announce Type: new Abstract: Existing multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited rep
arXiv:2604.15614v1 Announce Type: new Abstract: This paper proposes a novel method that incorporates empowerment when reasoning actions in reinforcement learning (RL), thereby achieving the flexibilit
arXiv:2504.01137v3 Announce Type: replace Abstract: Text-to-image generation models suffer from alignment problems, where generated images fail to accurately capture the objects and relations in the t
arXiv:2604.15395v1 Announce Type: new Abstract: Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towa