Knowledge catalogue
arXiv:2607.24662v1 Announce Type: new Abstract: Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap. We s
arXiv:2607.23642v1 Announce Type: cross Abstract: Discrete optimization algorithms are often analyzed through continuous-time limiting ODEs, but a convergence certificate for the ODE is not automatica
arXiv:2603.20317v2 Announce Type: replace Abstract: Space-based compute is becoming plausible as launch costs fall and data-intensive AI workloads grow. This paper proposes a workload-centric framewor
arXiv:2607.24237v1 Announce Type: new Abstract: Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point simil
arXiv:2604.01457v3 Announce Type: replace Abstract: Large language models are often not just wrong, but confidently wrong: when they produce factually incorrect answers, they tend to verbalize overly
arXiv:2607.23765v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance across multiple domains, but using the most capable model for every query is prohibitive a
arXiv:2607.23191v1 Announce Type: new Abstract: Fine-tuned code LLMs can be conditioned on a lightweight design-intent header to steer parametric CAD generation, but whether the model actually reads t
arXiv:2607.23865v1 Announce Type: new Abstract: Supervised deep learning models rely on large, accurately labeled datasets, yet noisy annotations are often unavoidable and can severely degrade perform
arXiv:2607.21800v1 Announce Type: cross Abstract: 18F-FDG PET/CT plays a central role in staging, treatment planning, and response assessment for head and neck cancer by providing functional informati
arXiv:2607.22376v1 Announce Type: new Abstract: Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books. We introduce a pi
arXiv:2607.22129v1 Announce Type: new Abstract: Accurate tree-level forest monitoring using laser scanning data requires reliable tree delineation, consistent tree correspondence across multitemporal
arXiv:2607.22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains. It is still challenging due to possession of high inter-class similar
arXiv:2607.22053v1 Announce Type: new Abstract: Segmentation of adjacent structures with similar intensity distributions remains a challenging problem in image analysis, particularly when object bound
arXiv:2607.21964v1 Announce Type: new Abstract: Understanding how robots and humans move in shared spaces is essential for designing effective social robot navigation policies and predicting human beh
arXiv:2511.13465v5 Announce Type: replace Abstract: This paper studies the exponential decay mechanism of the second-moment estimate in Adam. We propose AdamNX and a time-varying second-moment decay r
arXiv:2607.17441v1 Announce Type: cross Abstract: Deepfake generation has raised growing concerns regarding digital media authenticity, misinformation, identity fraud, and public trust. Recent studies
arXiv:2607.22286v1 Announce Type: new Abstract: Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics s
arXiv:2607.21980v1 Announce Type: new Abstract: Mastodon as a decentralized federation of independently moderated social servers poses unique challenges for the detection and mitigation of toxic conte
arXiv:2412.12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confid
arXiv:2601.05148v2 Announce Type: replace Abstract: Pathology foundation models substantially advanced the possibilities in computational pathology --- yet tradeoffs in terms of performance, robustnes
arXiv:2602.21473v3 Announce Type: replace Abstract: A key challenge in translating Visual Place Recognition (VPR) from the lab to long-term deployment is ensuring a priori that a system can meet user-
arXiv:2607.22342v1 Announce Type: new Abstract: The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such inform
arXiv:2607.22474v1 Announce Type: new Abstract: In overparameterized linear regression, many weak spectral directions act like a ridge penalty on the signal-bearing spectrum; negative ridge is the nat
arXiv:2508.13899v2 Announce Type: replace Abstract: Accurate ultrasound image segmentation is fundamentally challenged by target-context entanglement, where lesion cues are easily mixed with surroundi
arXiv:2503.07869v4 Announce Type: replace Abstract: Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data
arXiv:2607.22258v1 Announce Type: cross Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks. However, their performanc
arXiv:2607.22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are other
arXiv:2607.21654v1 Announce Type: cross Abstract: The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes. It offers a minimally r
arXiv:2607.22386v1 Announce Type: new Abstract: Latent domain watermarking for diffusion models embeds watermarks directly into the latent prior, enjoying non-intrusiveness to model parameters and sea
arXiv:2607.21823v1 Announce Type: new Abstract: We show that, up to isotropic scaling, the Gaussian RBF reproducing kernel Hilbert space (RKHS) is asymptotically isometric to Euclidean space in the la
arXiv:2607.21745v1 Announce Type: cross Abstract: Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep
arXiv:2607.22041v1 Announce Type: new Abstract: There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs), particular
arXiv:2607.21904v1 Announce Type: cross Abstract: Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. I
arXiv:2607.21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather
arXiv:2607.22148v1 Announce Type: new Abstract: In this work, we aim to discretize the high-dimensional visual representations to bridge the gap with language models - a non-trivial challenge, as exis
arXiv:2607.22514v1 Announce Type: new Abstract: Dysphagia is a debilitating late effect of head and neck cancer (HNC) treatment, yet timely identification of at-risk patients remains challenging in su
arXiv:2607.21958v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written c
arXiv:2607.21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a
arXiv:2607.17872v2 Announce Type: replace-cross Abstract: Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting ov
arXiv:2607.22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoida
arXiv:2607.22087v1 Announce Type: new Abstract: Generalization remains a critical bottleneck in AI-generated image detection. Because many modern generators are proprietary or adversarially modified,
arXiv:2607.22182v1 Announce Type: new Abstract: Large language model (LLM) evaluation spans diverse tasks and benchmarks, yet evidence remains organized around tasks rather than the capabilities they
arXiv:2607.22255v1 Announce Type: new Abstract: The segmentation of multiple degradations has been a challenging problem in the field of image segmentation. Existing level set approaches commonly adop
arXiv:2607.21999v1 Announce Type: new Abstract: Long-tailed learning couples two sources of poor generalization: head classes dominate training exposure, while under-represented classes often converge
arXiv:2607.22199v1 Announce Type: new Abstract: Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain inc
arXiv:2607.22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance. Many data-driven pipelines use fix
arXiv:2408.02295v4 Announce Type: replace Abstract: Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian. This assumption can miss the heavy-ta
Systematic failures of vision models on semantically coherent subsets, known as error slices, reveal limitations in robustness and evaluation. Existing slice discovery approaches largely model slices
arXiv:2607.21675v1 Announce Type: cross Abstract: Neural Radiance Fields (NeRF) have revolutionized novel view synthesis, yet their classical implementations remain computationally intensive for high-
arXiv:2412.01101v2 Announce Type: replace Abstract: Face-swapping DeepFakes have become an escalating societal concern, attracting increasing attention in recent years. To counter this, we investigate
arXiv:2603.19139v2 Announce Type: replace Abstract: Learning systems must balance generalization across experiences with discrimination of task-relevant details. Effective learning therefore requires
arXiv:2607.22389v1 Announce Type: cross Abstract: With the rapid adoption of long-context large language models (LLMs), the continuously growing KV cache during decoding has become the critical memory
arXiv:2607.22101v1 Announce Type: new Abstract: Diffusion models have recently achieved remarkable success in high-fidelity image synthesis, yet their application to visual text generation and editing
arXiv:2607.22367v1 Announce Type: new Abstract: Feature-attribution methods assign scores relating input variables to a model's output, but do not by themselves characterize how explicitly defined int
arXiv:2506.06616v2 Announce Type: replace Abstract: Accurate and interpretable detection of depressive language in social media can support early identification of mental health conditions and inform
arXiv:2607.22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is
arXiv:2607.21897v1 Announce Type: new Abstract: The rapid advancement of generative models has spurred the critical need to evaluate the worst-case robustness of deepfake detectors. In this paper, we
arXiv:2508.01782v4 Announce Type: replace-cross Abstract: Recently, large language models (LLMs) have driven promising progress in lossless image compression. However, directly adopting existing parad
arXiv:2508.13826v4 Announce Type: replace-cross Abstract: Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limit
arXiv:2607.21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and