Knowledge catalogue
arXiv:2609.31632v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis is evolving from short-segment classification toward long-horizon interpretation that demands iterative evidence a
arXiv:2503.10439v4 Announce Type: replace Abstract: Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper,
arXiv:2609.31880v1 Announce Type: new Abstract: We present FAVOR (Free-floating Arm Velocity Optimization with Recursive Sensitivities), a planner for collision-free reaching, tracking, and prescribed
arXiv:2609.32929v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient o
arXiv:2609.32956v1 Announce Type: new Abstract: Prior information for real-world physical quantities is most elegantly expressed via partial differential equations (PDEs). In this paper, we propose a
arXiv:2609.32176v1 Announce Type: new Abstract: The support recovery problem in mixture of linear classifiers intends to identify which features actually matter when data is generated by a mixture of
arXiv:2609.34608v1 Announce Type: new Abstract: World Action Models (WAMs) enable future-aware control by jointly modeling actions and environment dynamics. However, iterative diffusion or flow infere
arXiv:2609.33762v1 Announce Type: cross Abstract: LLM agents resend their whole conversation on every turn, and most of it was already processed on the previous turn. Serving systems avoid recomputing
arXiv:2604.05137v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can generate functionally correct programs that differ substantially in execution efficiency. Existing inference-
arXiv:2609.35368v1 Announce Type: new Abstract: Exo-to-egocentric video generation aims to synthesize what a person sees from their own viewpoint given third-person footage and a target head trajector
arXiv:2609.31751v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have advanced rapidly in static visual understanding, yet remain unreliable when judging how an egocentric task is progres
arXiv:2609.32486v1 Announce Type: new Abstract: Deploying a language model under different computing and latency budgets calls for compact models with different quality-cost trade-offs. Towards this e
arXiv:2609.31804v1 Announce Type: cross Abstract: Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure,
arXiv:2609.35622v1 Announce Type: cross Abstract: We study two-arm contextual bandits with arm-specific single indices and a shared unknown monotone link. Monotonicity makes the optimal action depend
arXiv:2609.33444v1 Announce Type: cross Abstract: A nascent family of methods that forgoes the policy gradient and reweights a supervised regression instead has garnered momentum in reinforcement lear
arXiv:2609.34373v1 Announce Type: new Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should
arXiv:2609.32100v1 Announce Type: new Abstract: The neural scaling law relating longer training to better performance through a power law is central to today's large language models (LLMs), yet its or
arXiv:2609.35047v1 Announce Type: cross Abstract: A robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge. It need not start from
arXiv:2609.31905v1 Announce Type: new Abstract: We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the
arXiv:2609.32399v1 Announce Type: new Abstract: Endoscopic scene reconstruction requires modeling tissue motion and temporal appearance while recovering fine surface detail. Deformable Gaussian models
arXiv:2609.31638v1 Announce Type: new Abstract: Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It
arXiv:2609.31648v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are studied mainly in robotics, where visual observations and language instructions are mapped to physical actions.
arXiv:2609.33758v1 Announce Type: new Abstract: Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than
arXiv:2609.33261v1 Announce Type: new Abstract: Multimodal engineering benchmarks largely evaluate static understanding, such as recognizing components, interpreting diagrams, or answering technical q
arXiv:2609.34178v1 Announce Type: new Abstract: Video text editing aims to replace or add text in a video while keeping the rest of the video unchanged, which requires the edited text to be correct in
arXiv:2609.31657v1 Announce Type: new Abstract: Remote-sensing foundation models offer strong representations for SAR imagery, but their behavior under severe long-tail class imbalance is still not we
arXiv:2609.31633v1 Announce Type: new Abstract: In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turb
arXiv:2609.31911v1 Announce Type: new Abstract: Medical report generation has made significant progress with the rise of modern vision-language models and the growing availability of large-scale medic
arXiv:2609.34185v1 Announce Type: new Abstract: Weight compression helps large neural networks fit deployment memory budgets, but common fixed-width formats offer only coarse storage choices. Entropy
arXiv:2601.22944v3 Announce Type: replace Abstract: Out-of-distribution generalization becomes challenging when spurious correlations vary across environments while difficult or underrepresented sampl
arXiv:2609.34285v1 Announce Type: new Abstract: Imprecise annotations may support several plausible labelling distributions, yet learning methods often resolve this ambiguity into a single predictive
arXiv:2609.33939v1 Announce Type: cross Abstract: Reliable 3D spatial understanding is essential for autonomous navigation, obstacle avoidance, and scene reconstruction. While state-of-the-art learned
arXiv:2609.32661v1 Announce Type: new Abstract: Maximum common edge subgraph (MCES) matching finds a partial vertex correspondence between two labeled graphs that preserves as many labeled edges as po
arXiv:2609.34817v1 Announce Type: new Abstract: Human dexterity is guided by two eyes watching two hands: binocular vision supplies the metric 3D structure that fine-grained manipulation consumes. Ego
arXiv:2609.34018v1 Announce Type: cross Abstract: Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires
arXiv:2609.35269v1 Announce Type: cross Abstract: The rising number of concept unlearning techniques for text-to-image (T2I) diffusion models has produced a fragmented evaluation landscape. Methods ar
arXiv:2609.34627v1 Announce Type: new Abstract: Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly,
arXiv:2609.35277v1 Announce Type: new Abstract: Tironian notes are generally regarded as the first Latin shorthand system and are notable for their large, fine-grained symbol inventory. Their high vis
arXiv:2609.32190v1 Announce Type: new Abstract: DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational model
arXiv:2609.31981v1 Announce Type: cross Abstract: As Internet of Things (IoT) networks increasingly depend on machine learning for anomaly, malware, intrusion detection, and network monitoring, such s
arXiv:2609.35614v1 Announce Type: new Abstract: Adam and its variants dominate neural network training, but a single run only reveals whether a configuration works well after most of its budget is spe
arXiv:2509.15880v2 Announce Type: replace Abstract: Geometry-grounded vision models, such as VGGT, have emerged as robust visual encoders, providing essential geometric priors for robotic manipulation
arXiv:2609.34672v1 Announce Type: new Abstract: Fine-grained visual understanding requires models to recognize small details within complex images. Multimodal on-policy self-distillation (OPSD) addres
arXiv:2609.34520v1 Announce Type: new Abstract: Deep learning models for Alzheimer disease (AD) classification routinely report near-perfect discrimination, yet few are shown to rest on AD-relevant ne
arXiv:2609.34853v1 Announce Type: new Abstract: Open-vocabulary 3D scene understanding enables object localization and segmentation from free-form text queries without a fixed category vocabulary. Man
arXiv:2609.34114v1 Announce Type: new Abstract: Fairness, as a fundamental social norm, continues to pose a longstanding puzzle regarding its emergence. Traditional game-theoretic models largely rely
arXiv:2609.33101v1 Announce Type: cross Abstract: Little is known about how to manually design agents capable of dexterous manipulation. Some design principles have been inferred from close examinatio
arXiv:2609.35616v1 Announce Type: new Abstract: Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation. Existing generators use inc
arXiv:2609.34719v1 Announce Type: new Abstract: Autonomous excavation requires a spatial representation that jointly captures terrain geometry and task-relevant semantics. Existing excavation mapping
arXiv:2609.34146v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring from photoplethysmography (PPG) has strong potential for wearable health and telemonitoring, but accu
arXiv:2609.35128v1 Announce Type: new Abstract: Hyperbolic neural networks introduce geometric operations that require explicit treatment in relevance propagation. Equivalent geometric realizations ca
arXiv:2602.04553v2 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations, yet their application to nuclear scatteri
arXiv:2609.32641v1 Announce Type: new Abstract: Graph neural networks typically rely on dense, floating-point node representations, which can impose substantial memory and computational costs. Binary
arXiv:2609.33811v1 Announce Type: new Abstract: Motorized two-wheelers (MTW) dominate Indian roads but remain underrepresented in driver behavior research. This study presents the first large-scale an
arXiv:2609.32352v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging remain insuf
arXiv:2602.06187v2 Announce Type: replace Abstract: Federated learning (FL) enables collaborative training while keeping raw data on clients. Deletion requests and the discovery of poisoned data creat
arXiv:2609.35575v1 Announce Type: new Abstract: The real-world performance of current vision-language-action models is fundamentally constrained by the limited coverage of expert demonstrations and th
arXiv:2609.31915v1 Announce Type: new Abstract: Hybrid quantum methods have received limited study for resource-constrained facial biometrics. We present a hybrid quantum-classical facial recognition
arXiv:2308.09456v2 Announce Type: replace Abstract: Overtaking on two-lane roads is a safety-critical decision-making problem for autonomous vehicles, since oncoming traffic may force the ego vehicle
arXiv:2609.34175v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are typically adapted using successful demonstrations, which provide direct action supervision but rarely cover fa