Knowledge catalogue
arXiv:2608.16016v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional asses
arXiv:2608.15454v1 Announce Type: new Abstract: Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization.
arXiv:2304.14385v4 Announce Type: replace-cross Abstract: We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising
arXiv:2412.07672v2 Announce Type: replace-cross Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses oft
arXiv:2608.15537v1 Announce Type: cross Abstract: Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textur
arXiv:2608.14637v1 Announce Type: new Abstract: Long-duration stationary energy storage requires batteries whose degradation can be detected before substantial capacity loss has accumulated. Iron-chro
arXiv:2608.15282v1 Announce Type: cross Abstract: Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process model
arXiv:2504.09209v3 Announce Type: replace-cross Abstract: Masked modeling has shown promise in co-speech gesture generation. However, it struggles to identify semantically significant frames for effec
arXiv:2608.15502v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant challenges f
arXiv:2603.18739v4 Announce Type: replace Abstract: Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict computation and memory bud
arXiv:2608.15522v1 Announce Type: new Abstract: Recent audio-visual generation models can synthesize synchronized video and sound in a unified diffusion process, but their inference cost remains high
arXiv:2608.14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remai
arXiv:2508.21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question
arXiv:2608.16270v1 Announce Type: new Abstract: Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset. Existing gradient-approximati
arXiv:2608.14645v1 Announce Type: new Abstract: Studying climate change requires reducing uncertainties in CO2 and CH4 emission estimates to better distinguish anthropogenic from natural sources, whic
arXiv:2608.15614v1 Announce Type: cross Abstract: The use of multimodal LLMs (MLLMs) for egocentric video understanding with wearable devices is constrained by the token budget. Memory and compute cos
arXiv:2608.15060v1 Announce Type: new Abstract: Touch is fundamental to dexterous manipulation, yet most egocentric human data increasingly used for robot learning lacks tactile information. Directly
arXiv:2608.16084v1 Announce Type: new Abstract: Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error g
arXiv:2608.15105v1 Announce Type: cross Abstract: Recent progress in optimization research has highlighted the sharpness of the loss landscape as a key factor in narrowing the generalization gap. Moti
arXiv:2512.22983v2 Announce Type: replace Abstract: Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic mani
arXiv:2608.14731v1 Announce Type: new Abstract: Worldwide, millions of senior citizens are suffering from Alzheimer disease abbreviated as AD, a well- versed form of dementia. AD is featured by amnesi
arXiv:2608.15796v1 Announce Type: new Abstract: Unsupervised 3D instance segmentation of outdoor LiDAR scans has traditionally relied on handcrafted geometric priors such as density-based clustering,
arXiv:2608.14825v1 Announce Type: cross Abstract: Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs. Much of the safe
arXiv:2606.20753v2 Announce Type: replace-cross Abstract: Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet thei
arXiv:2608.16101v1 Announce Type: cross Abstract: Coresets distill large datasets into small, representative subsets for efficient downstream learning. Yet Optimal Transport (OT)-based selection typic
arXiv:2608.15349v1 Announce Type: cross Abstract: To accelerate single image super-resolution (SISR) networks on large images (2K-8K), many recent approaches decompose an image into small patches and
arXiv:2509.04899v4 Announce Type: replace-cross Abstract: Restricted Boltzmann machines (RBMs) are energy-based models originating from statistical physics, in which hidden units mediate the probabili
arXiv:2508.18037v2 Announce Type: replace Abstract: Leveraging information from public data has become increasingly crucial in enhancing the utility of differentially private (DP) methods. Traditional
arXiv:2503.09020v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and pro
arXiv:2608.14706v1 Announce Type: cross Abstract: Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedul
arXiv:2302.12177v4 Announce Type: replace-cross Abstract: Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a pro
arXiv:2608.14773v1 Announce Type: cross Abstract: The efficient-KAN literature---covering Chebyshev, wavelet, and radial-basis-function variants of the original Kolmogorov-Arnold Network---has been be
arXiv:2608.15749v1 Announce Type: new Abstract: Existing 3D editing methods have made notable progress in controllability, yet they remain limited in several important ways. Most approaches rely on te
arXiv:2608.15424v1 Announce Type: cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patien
arXiv:2608.14585v1 Announce Type: new Abstract: Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and
arXiv:2608.14863v1 Announce Type: cross Abstract: LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified.
arXiv:2608.14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individu
arXiv:2608.14583v1 Announce Type: cross Abstract: Vehicular Ad Hoc Networks (VANETs) are a key component of intelligent transportation systems, enabling real-time communication between vehicles. Howev
arXiv:2601.21979v2 Announce Type: replace Abstract: Feature embeddings acquired from pretrained models are widely used in medical applications of deep learning to assess the characteristics of dataset
arXiv:2608.16647v1 Announce Type: new Abstract: On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization be
arXiv:2608.15383v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) language models reduce arithmetic by activating only a small subset of experts per token, yet deployment still requires
arXiv:2608.14952v1 Announce Type: cross Abstract: A structured world-state (entities, relations, context, and predictive cues) is designed to preserve prediction-critical content when perception degra
arXiv:2503.00992v2 Announce Type: replace Abstract: In this paper we leverage psychological methods to investigate LLMs' conceptual mastery in applying rules. We introduce a novel procedure to match t
arXiv:2608.15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowle
arXiv:2608.16416v1 Announce Type: new Abstract: Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in
arXiv:2602.18849v2 Announce Type: replace-cross Abstract: We develop a sensitivity analysis for transformer attention in a geometry aligned with tokenwise computation. Our main result is the exact ide
arXiv:2603.16241v2 Announce Type: replace Abstract: Semi-supervised crowd analysis is a prominent area of research, as unlabeled data are typically abundant and inexpensive to obtain. However, traditi
arXiv:2608.16295v1 Announce Type: new Abstract: AI coding agents need more than relevant snippets: they need business semantics, validation evidence, relations, and assurance that their context is cur
arXiv:2412.09557v3 Announce Type: replace-cross Abstract: Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in
arXiv:2608.14826v1 Announce Type: cross Abstract: The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as
arXiv:2510.17088v3 Announce Type: replace-cross Abstract: Financial anomalies arise from heterogeneous mechanisms - price shocks, liquidity freezes, contagion cascades, and momentum reversals - yet ex
arXiv:2608.14620v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural net
arXiv:2608.16476v1 Announce Type: new Abstract: Learning embodied urban navigation policies from real-world data is constrained by the cost of task-specific data collection and the limited coverage of
arXiv:2608.14603v1 Announce Type: cross Abstract: Cooperative perception enables vehicles and infrastructure to exchange sensor data via Vehicle-to-Everything (V2X) communication, extending sensing co
arXiv:2608.16697v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogene
arXiv:2608.15408v1 Announce Type: new Abstract: Deep operator networks can become statistically unstable when partial differential equation inputs are observed at thousands of strongly correlated sens
arXiv:2608.15113v1 Announce Type: cross Abstract: Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings. However, the high representational ca
arXiv:2603.02830v2 Announce Type: replace-cross Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interv
arXiv:2608.15867v1 Announce Type: cross Abstract: Synthetic populations are critical inputs for activity-based travel demand models, yet generating realistic populations from limited survey data remai
arXiv:2608.16148v1 Announce Type: new Abstract: Multi-view multi-label feature selection aims to identify a compact and informative feature subset from heterogeneous views while preserving discriminat