Knowledge catalogue
arXiv:2503.04404v3 Announce Type: replace Abstract: This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Althou
arXiv:2506.23487v2 Announce Type: replace-cross Abstract: We propose a novel test for assessing partial effects in Frechet regression with responses lying on the Bures-Wasserstein manifold. Under the
arXiv:2608.29322v1 Announce Type: new Abstract: Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or co
arXiv:2608.28823v1 Announce Type: cross Abstract: Artists coordinate human pose, illumination, and camera placement to convey narrative and emotion, but existing generative methods typically model the
arXiv:2608.29456v1 Announce Type: new Abstract: As artificial intelligence is increasingly integrated into chest X-ray (CXR) interpretation, triage, and clinical decision support, understanding its vu
arXiv:2608.29706v1 Announce Type: new Abstract: A token's representation is carried through the network layer by layer. The whole vocabulary carried together forms a flow. We fit this flow's equation
arXiv:2608.29530v1 Announce Type: cross Abstract: Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modeled
arXiv:2608.28930v1 Announce Type: cross Abstract: Hidden-state probes effectively detect LLM hallucinations, but the geometry of the signal remains poorly characterized, driving increasingly complex p
arXiv:2608.28980v1 Announce Type: cross Abstract: Can the specialized architectures that machine learning has traditionally built for structured data be replaced by language-based models? This questio
arXiv:2608.28949v1 Announce Type: cross Abstract: We study a class of product-reference diffusion algorithms for sampling from a discrete distribution. We show that their sampling performance can be c
arXiv:2603.11842v2 Announce Type: replace-cross Abstract: The post-ChatGPT surge has rapidly reframed IS research and practice. As organizations and society grapple with GenAI adoption, a body of seco
arXiv:2608.30052v1 Announce Type: cross Abstract: When a generative search interface answers a commercial question, which market's products it names is decided before the model reasons about the produ
arXiv:2608.29246v1 Announce Type: cross Abstract: Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Nowa
arXiv:2608.30087v1 Announce Type: new Abstract: Unlearning an identity from a face-conditioned generator by redirecting its conditioning embedding can silently fail if the redirected output is still v
arXiv:2608.30900v1 Announce Type: cross Abstract: The downwash wake of a hovering quadrotor governs both the vehicle's own performance and the safe spacing of multi-rotor formations. Prior measurement
arXiv:2607.16676v2 Announce Type: replace-cross Abstract: How deep does a graph neural network need to be on a sparse graph? We study its purest statistical form: node classification on the sparse con
arXiv:2601.15897v3 Announce Type: replace Abstract: Multi-modal scene reconstruction integrating RGB and thermal infrared data is essential for robust environmental perception across diverse lighting
arXiv:2608.29374v1 Announce Type: new Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs) t
arXiv:2608.30640v1 Announce Type: new Abstract: While self-supervised approaches to reinforcement learning have achieved strong results by learning representations of states and actions, a key open qu
arXiv:2601.12075v2 Announce Type: replace Abstract: Language models used in retrieval-augmented settings must arbitrate between parametric knowledge stored in their weights and contextual information
arXiv:2603.02465v2 Announce Type: replace-cross Abstract: Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video data
arXiv:2608.29459v1 Announce Type: new Abstract: Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reaso
arXiv:2504.13754v4 Announce Type: replace-cross Abstract: Peripheral neuroblastic tumors (pNTs) are among the most common extracranial solid tumors in children, and accurate pathological subtyping is
arXiv:2608.29869v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are often regarded as energy-efficient alternatives to artificial neural networks (ANNs), yet their advantage depends cri
arXiv:2608.30424v1 Announce Type: cross Abstract: Large language models can support writing, but existing tools require users to explicitly articulate prompts-particularly burdensome in creative writi
arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive outputs
arXiv:2608.28610v1 Announce Type: new Abstract: Existing LLM moral evaluations typically present models with isolated moral vignettes and elicit a single-shot decision, neglecting a factor known to pr
arXiv:2608.30081v1 Announce Type: cross Abstract: Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples in
arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyin
arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigge
arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as trusted
arXiv:2608.30514v1 Announce Type: cross Abstract: We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-di
arXiv:2608.30704v1 Announce Type: new Abstract: AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perf
arXiv:2608.30485v1 Announce Type: new Abstract: The language a legislature uses to debate women's rights, even in favour of them, encodes systematic patterns of sexism that persist across two centurie
arXiv:2608.30795v1 Announce Type: cross Abstract: End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numer
arXiv:2608.28865v1 Announce Type: cross Abstract: Joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation can reduce duplicated processing in intelligent receivers,
arXiv:2608.30390v1 Announce Type: new Abstract: Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches pri
arXiv:2608.29279v1 Announce Type: new Abstract: Deep learning is often criticized for its theoretical research lagging behind practice. To make deep learning easier to understand, the entropy space th
arXiv:2408.02111v4 Announce Type: replace-cross Abstract: Despite the extreme popularity of deep learning in science and industry, its formal understanding is limited. This thesis puts forth notions o
arXiv:2608.28665v1 Announce Type: new Abstract: Recent open-vocabulary segmentation models have advanced semantic perception for UAVs, but predictions from moving aerial platforms can remain temporall
arXiv:2604.03592v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps rema
arXiv:2608.30260v1 Announce Type: cross Abstract: While it is well-established that prosody carries crucial cues for syntactic structure, the degree and nature of correspondence between these two doma
arXiv:2608.28981v1 Announce Type: new Abstract: As air traffic volumes in the National Airspace System continue to expand, in particular in the low altitude airspaces, the need for scalable decision s
arXiv:2608.29249v1 Announce Type: new Abstract: The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by Large Language Models (LLMs). While ofte
arXiv:2608.30870v1 Announce Type: new Abstract: Semantic segmentation in 3D Gaussian Splatting (3DGS) is crucial for advancing 3D scene understanding. Existing methods predominantly rely on feature di
arXiv:2505.12196v2 Announce Type: replace Abstract: The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conjectu
arXiv:2608.29720v1 Announce Type: new Abstract: Inertial odometry (IO) is critical for aerial robots, where aggressive maneuvers and poor lighting degrade visual sensors. Recent learning-based IO meth
arXiv:2608.28997v1 Announce Type: new Abstract: In May 2026 an OpenAI model produced a counterexample to the Erdos unit distance conjecture. Five mathematicians published a human-verified version the
arXiv:2608.30480v1 Announce Type: new Abstract: Object hallucination remains a persistent reliability issue in large vision-language models, where generated object mentions may sound plausible but lac
arXiv:2608.29449v1 Announce Type: cross Abstract: We survey the theory of vocabulary growth founded in the setting of stochastic processes. In particular, we model the expected number of types through
arXiv:2608.30823v1 Announce Type: cross Abstract: The vocal music of each language carries a distinctive sonic identity, even without instrumental accompaniment. We ask whether these differences are m
arXiv:2505.16567v4 Announce Type: replace-cross Abstract: Finetuning open-weight Large Language Models (LLMs) is standard practice for achieving task-specific performance improvements. Until now, fine
arXiv:2608.19269v4 Announce Type: replace-cross Abstract: Benchmarks can run without determining what their results license. We freeze a large evaluation collection and attempt to replay its historica
arXiv:2608.29296v1 Announce Type: cross Abstract: Larger batches reduce the variance of stochastic gradients per update and are therefore often expected to accelerate training. Yet whether this statis
arXiv:2608.30086v1 Announce Type: new Abstract: Credit-default prediction is an important task in financial decision making. Traditional methods use fitted classifiers such as logistic regression and
arXiv:2603.25269v2 Announce Type: replace Abstract: Hateful content online is often expressed using fact-like, not necessarily correct information, especially in coordinated online harassment campaign
arXiv:2608.29903v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has made AI-generated text detection increasingly critical. Existing zero-shot detectors assume
arXiv:2608.30395v1 Announce Type: new Abstract: As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inf
arXiv:2604.02194v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retrieve
arXiv:2608.29571v1 Announce Type: new Abstract: Flexible adaptation to context and shared pragmatic intuitions contribute to smooth human conversation. Iterated reference games---in which players repe