Knowledge catalogue
arXiv:2604.22649v1 Announce Type: cross Abstract: Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) re
arXiv:2604.22156v1 Announce Type: cross Abstract: Purpose: Accurate assessment of the Critical View of Safety (CVS) during laparoscopic cholecystectomy is essential to prevent bile duct injury, a comp
arXiv:2604.22452v1 Announce Type: new Abstract: Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language m
arXiv:2510.11586v2 Announce Type: replace Abstract: Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LL
arXiv:2601.12430v2 Announce Type: replace Abstract: Vision-language model (VLM) hallucination is commonly linked to imbalanced allocation of attention across input modalities: system, image and text.
arXiv:2604.22337v1 Announce Type: new Abstract: Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We p
arXiv:2603.19500v2 Announce Type: replace Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel mu
arXiv:2604.22237v1 Announce Type: cross Abstract: Diagnosing student problem behaviors requires teachers to synthesize multifaceted information, identify behavioral categories, and plan intervention s
arXiv:2412.19780v2 Announce Type: replace Abstract: Tensor networks are a tool first employed in the context of many-body quantum physics that now have a wide range of uses across the computational sc
arXiv:2510.07632v2 Announce Type: replace Abstract: Frontier AI models have achieved remarkable progress, yet recent studies suggest they struggle with compositional reasoning, often performing at or
arXiv:2604.21938v1 Announce Type: cross Abstract: Embodied AI is widely discussed as a job-displacement problem. The deeper risk, however, is governance lag: the inability of public institutions to ke
arXiv:2601.12979v3 Announce Type: replace Abstract: The pursuit of real-time agentic interaction has driven interest in Diffusion-based Large Language Models (dLLMs) as alternatives to auto-regressive
arXiv:2604.22627v1 Announce Type: cross Abstract: Joint measurements on multiple copies of a quantum state provide access to nonlinear observables such as operatorname{tr}(rho^t), but whether replica
arXiv:2505.20435v3 Announce Type: replace-cross Abstract: Existing interpretability methods for Large Language Models (LLMs) predominantly capture linear directions or isolated features. This overlook
arXiv:2604.22351v1 Announce Type: cross Abstract: Mid-infrared astronomy from the ground faces critical challenges in accurately detecting and quantifying sources due to the dominant spatially and tim
arXiv:2604.22709v1 Announce Type: new Abstract: While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference. Non-verbal
arXiv:2604.22695v1 Announce Type: cross Abstract: Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to ch
arXiv:2506.05038v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks. However, these models exhibit unexpecte
arXiv:2506.17299v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become increasingly deployed in safety-critical applications, the lack of systematic methods to assess their v
arXiv:2509.20979v2 Announce Type: replace Abstract: In modern GPU inference, cache efficiency remains a major bottleneck, and heuristic policies such as extsc{LRU} can perform far worse than the offli
arXiv:2604.22464v1 Announce Type: new Abstract: Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing C
arXiv:2604.22260v1 Announce Type: cross Abstract: Urban transportation systems face growing safety challenges that require scalable intelligence for emerging smart mobility infrastructures. While rece
arXiv:2604.22226v1 Announce Type: new Abstract: Sports videos are a challenging domain for multimodal understanding because they involve complex and dynamic human activities. Despite rapid progress in
arXiv:2604.22068v1 Announce Type: cross Abstract: Validating Autonomous Vehicles (AVs) requires exposure to rare, safety-critical scenarios, infrequent in routine driving data. Existing benchmarks add
arXiv:2601.03779v2 Announce Type: replace Abstract: We explore intrinsic dimension (ID) of LLM representations as a marker of linguistic complexity. Specifically, we test whether ID differences across
arXiv:2604.22552v1 Announce Type: new Abstract: Physical adversarial patch attacks critically threaten pedestrian detection, causing surveillance and autonomous driving systems to miss pedestrians and
arXiv:2511.22277v2 Announce Type: replace Abstract: Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when
arXiv:2512.20761v3 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) are transforming the field of forecasting. However, evaluating them on historical data is increasingly d
arXiv:2604.22225v1 Announce Type: new Abstract: While generative text-to-speech (TTS) models approach human-level quality, monolithic metrics fail to diagnose fine-grained acoustic artifacts or explai
arXiv:2604.22177v1 Announce Type: new Abstract: Multimodal MRI offers complementary information for brain tumor segmentation, but clinical scans often lack one or more modalities, which degrades segme
arXiv:2603.18941v2 Announce Type: replace-cross Abstract: The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and
arXiv:2602.07038v2 Announce Type: replace-cross Abstract: Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual qual
arXiv:2604.22209v1 Announce Type: cross Abstract: Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each
arXiv:2604.21999v1 Announce Type: cross Abstract: We study learned memory tokens as computational scratchpad for a single-block Universal Transformer (UT) with Adaptive Computation Time (ACT) on Sudok
arXiv:2604.22174v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) holds significant promise for the label-scarce Synthetic Aperture Radar (SAR) domain, yet its efficacy is severely
arXiv:2508.06165v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for know
arXiv:2604.22579v1 Announce Type: cross Abstract: We study whether deep networks for medical imaging learn useful nonrobust features - predictive input patterns that are not human interpretable and hi
arXiv:2604.22555v1 Announce Type: new Abstract: Estimating racial disparity requires individual-level race data, which are often unavailable due to the sensitivity of collecting such information. To a
arXiv:2504.06148v3 Announce Type: replace Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual-text processing. However, existi
arXiv:2604.22196v1 Announce Type: new Abstract: Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and t
arXiv:2604.22215v1 Announce Type: cross Abstract: Verbal confidence elicitation is widely used to extract uncertainty estimates from LLMs. We tested whether seven instruction-tuned open-weight models
arXiv:2604.22554v1 Announce Type: new Abstract: Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely change
arXiv:2210.05513v2 Announce Type: replace Abstract: We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifical
arXiv:2604.22142v1 Announce Type: new Abstract: This study examines how large language model rewriting alters the style and narrative texture of personal narratives. It analyzes 300 personal narrative
arXiv:2603.16663v5 Announce Type: replace-cross Abstract: The AIED community envisions AI evolving 'from tools to teammates,' yet most research still examines AI agents primarily through one-on-one hu
arXiv:2604.22153v1 Announce Type: cross Abstract: When you ask an AI assistant for advice about your career, your marriage, or a conflict with your family, does it give you the same answer regardless
arXiv:2604.22002v1 Announce Type: new Abstract: Social media platforms have become primary channels for health information in the Global South. Using gomutra (cow urine) discourse on YouTube in India
arXiv:2604.22273v1 Announce Type: new Abstract: Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear. We frame self-correcti
arXiv:2602.21977v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has emerged as a leading technique for efficiently fine-tuning text-to-image diffusion models, and its widespread adoptio
arXiv:2510.21285v4 Announce Type: replace Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harm
arXiv:2604.21993v1 Announce Type: new Abstract: We study the detection of transient liquidity erosion ('crumbling quotes') in electronic limit order books, where observable quote deterioration may ref
arXiv:2604.22127v1 Announce Type: new Abstract: Hybrid language models that interleave attention with recurrent components are increasingly competitive with pure Transformers, yet standard LoRA practi
arXiv:2604.22096v1 Announce Type: cross Abstract: In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real-world
arXiv:2604.22102v1 Announce Type: cross Abstract: Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. To mitigate this, we
arXiv:2604.22723v1 Announce Type: cross Abstract: We present a method for discovering morphological features in low-resource Bantu languages by combining cross-lingual transfer learning with unsupervi
arXiv:2604.21442v1 Announce Type: new Abstract: The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby pr
arXiv:2604.21568v1 Announce Type: new Abstract: Autonomous robots deployed in mass casualty incidents (MCI) face the challenge of making critical decisions based on incomplete and noisy perceptual dat
arXiv:2604.21740v1 Announce Type: cross Abstract: Discrete-event systems and supervisory control theory provide a rigorous framework for specifying correct-by-construction behavior. However, their pra
arXiv:2604.21729v1 Announce Type: new Abstract: Pumping fluids is fundamental to a wide range of industrial, environmental, and biomedical applications. Among various pumping mechanisms, peristaltic p
arXiv:2509.03294v3 Announce Type: replace-cross Abstract: The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurit