Knowledge catalogue
arXiv:2608.17707v1 Announce Type: new Abstract: Audio-driven avatar generation requires realistic lip-sync, expressive motion, and real-time streaming. Recent work achieves the latter via self-forcing
arXiv:2608.17896v1 Announce Type: new Abstract: Recurrent models process long contexts efficiently by compressing their history into a fixed-size state, but modern architectures typically do so in a s
arXiv:2608.17069v1 Announce Type: cross Abstract: Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data neve
arXiv:2608.17079v1 Announce Type: new Abstract: Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting
arXiv:2608.17453v1 Announce Type: new Abstract: Long-horizon humanoid vision--language--action (VLA) control with head-mounted stereo cameras requires visual interfaces that can exploit complementary
arXiv:2608.18063v1 Announce Type: new Abstract: High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions
arXiv:2608.16910v1 Announce Type: cross Abstract: National AI strategies increasingly guide governance, workforce development, innovation, and competitiveness, but less is known about how they frame e
arXiv:2608.16907v1 Announce Type: cross Abstract: Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on Ge
arXiv:2608.17819v1 Announce Type: new Abstract: Aerial manipulation extends robotic operations to previously inaccessible aerial environments. Unlike arm-equipped aerial systems, tiltable-multirotors
arXiv:2608.17516v1 Announce Type: new Abstract: Gender bias or other social biases in large language models (LLMs) are frequently evaluated with question answering or survey benchmarks where the LLM n
arXiv:2505.22104v2 Announce Type: replace Abstract: Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a shield,
arXiv:2608.17849v1 Announce Type: new Abstract: Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision var
arXiv:2608.17941v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but relies on costly rollout explor
arXiv:2608.17646v1 Announce Type: new Abstract: This monograph develops elimination geometry (EG), a typed, native-loss, audit-oriented framework for studying when locally optimal objects can be reali
arXiv:2608.16930v1 Announce Type: cross Abstract: Existing multi-task learning methods rely on hard sharing, multiple paths or experts, adaptive sharing, and dynamic expansion. However, their capacity
arXiv:2608.17512v1 Announce Type: new Abstract: Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing m
arXiv:2608.17102v1 Announce Type: new Abstract: Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, includ
arXiv:2608.17843v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it informs model beh
arXiv:2608.17700v1 Announce Type: new Abstract: Cross-distribution generalization remains a critical bottleneck in deepfake detection. While recent efforts leverage the semantic priors of large-scale
arXiv:2509.00203v3 Announce Type: replace Abstract: Parameterized partial differential equations (PDEs) underpin the mathematical modeling of complex systems in diverse domains, including engineering,
arXiv:2608.17787v1 Announce Type: cross Abstract: Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with
arXiv:2603.02876v2 Announce Type: replace Abstract: Large Language Model personas, explicit profiles specifying a user's attributes, preferences, and behavioural tendencies, are increasingly used to s
arXiv:2608.17971v1 Announce Type: new Abstract: The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical we
arXiv:2608.17524v1 Announce Type: new Abstract: This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functio
arXiv:2608.17731v1 Announce Type: new Abstract: Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently ambiguous. E
arXiv:2608.17726v1 Announce Type: new Abstract: Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance cos
arXiv:2608.17933v1 Announce Type: new Abstract: Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsup
arXiv:2507.15240v2 Announce Type: replace Abstract: For classification with imbalanced class frequencies, i.e., imbalanced classification (IC), standard accuracy is known to be misleading as a perform
arXiv:2607.15209v2 Announce Type: replace Abstract: Multilingual pre-trained language models such as XLM-R perform well for major languages but struggle with low-resource Ge'ez-script languages, large
arXiv:2608.17167v1 Announce Type: cross Abstract: Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation over yet-unob
arXiv:2608.17522v1 Announce Type: cross Abstract: Persistent shortages in the surgical workforce and inherent limitations of traditional training methods highlight the necessity of automated, data-dri
arXiv:2608.17247v1 Announce Type: new Abstract: Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this
arXiv:2509.12040v3 Announce Type: replace-cross Abstract: Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sens
arXiv:2407.13753v3 Announce Type: replace Abstract: Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disord
arXiv:2608.17203v1 Announce Type: cross Abstract: Contrastive learning has become a cornerstone of modern representation learning, powering CLIP-style models that underpin text-to-image generation, vi
arXiv:2608.17360v1 Announce Type: cross Abstract: Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) without accoun
arXiv:2608.17573v1 Announce Type: cross Abstract: In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambi
arXiv:2608.16971v1 Announce Type: new Abstract: Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning t
arXiv:2603.04061v2 Announce Type: replace-cross Abstract: Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of quantum hypothesis t
arXiv:2608.17027v1 Announce Type: new Abstract: Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hung
arXiv:2608.17202v1 Announce Type: new Abstract: Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in minute
arXiv:2608.17691v1 Announce Type: new Abstract: Key-keyway assembly tasks impose strict geometric constraints and are highly sensitive to grasp pose deviations in uncertain environments. This work pre
arXiv:2608.17075v1 Announce Type: cross Abstract: Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available
arXiv:2608.17802v1 Announce Type: new Abstract: Let arepsilon_1,ldots,arepsilon_n be independent Rademacher signs and let a=(a_1,ldots,a_n)inR^n satisfy the normalization below. For the normalized Rad
arXiv:2608.17103v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capable of expl
arXiv:2607.16660v2 Announce Type: replace-cross Abstract: The increasing adoption of Large Language Models (LLMs) as AI components in modern software systems introduces distinct security risks to the
arXiv:2608.18076v1 Announce Type: cross Abstract: Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically o
arXiv:2608.17454v1 Announce Type: new Abstract: This paper presents a pipeline for analyzing media bias and framing in online news. The pipeline groups articles into topics and events, adds named-enti
arXiv:2608.17827v1 Announce Type: new Abstract: Public institutions face a persistent challenge in selecting LLMs suited to their specific context. Existing benchmarks, however, are of limited use as
arXiv:2608.17618v1 Announce Type: cross Abstract: Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actio
arXiv:2511.16088v3 Announce Type: replace-cross Abstract: Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks
arXiv:2608.01324v2 Announce Type: replace Abstract: Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches
arXiv:2608.17694v1 Announce Type: cross Abstract: Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that input is alrea
arXiv:2603.00162v2 Announce Type: replace-cross Abstract: [18F]FDG-PET/CT is a cornerstone imaging modality for guiding oncology therapies, yet human expert shortages necessitate more efficient diagno
arXiv:2608.17440v1 Announce Type: new Abstract: Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely
arXiv:2608.17832v1 Announce Type: new Abstract: Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a uniqu
arXiv:2608.17389v1 Announce Type: new Abstract: Long-sequence 3D reconstruction from RGB videos requires both accurate local geometry and globally consistent camera motion. Feed-forward models provide
arXiv:2506.06584v2 Announce Type: replace Abstract: Learning Gaussian Mixture Models (GMMs) is a fundamental problem in statistics and machine learning, with the Expectation-Maximization (EM) algorith
arXiv:2502.00213v5 Announce Type: replace-cross Abstract: Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite ex
arXiv:2608.17938v1 Announce Type: cross Abstract: Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against an explicit