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arXiv:2602.21404v1 Announce Type: new Abstract: A central premise in evolutionary biology is that individual variation can generate information asymmetries that facilitate the emergence of hierarchical...
arXiv:2602.21406v1 Announce Type: new Abstract: Temporal Action Segmentation (TAS) requires dividing videos into action segments, yet the vast space of activities and alternative breakdowns makes...
arXiv:2602.21408v1 Announce Type: new Abstract: Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian...
arXiv:2602.21411v1 Announce Type: new Abstract: Convex Agreement (CA) strengthens Byzantine Agreement (BA) by requiring the output agreed upon to lie in the convex hull of...
arXiv:2602.21415v1 Announce Type: new Abstract: Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available...
arXiv:2602.21416v1 Announce Type: new Abstract: We introduce the task of SVG extraction, which consists in translating specific visual inputs from an image into scalable vector...
arXiv:2602.21418v1 Announce Type: new Abstract: This work presents a wearable human activity recognition (HAR) system that performs real-time inference directly inside a shank-mounted inertial measurement...
arXiv:2602.21420v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the leading paradigm for enhancing reasoning in Large Language Models (LLMs). However,...
arXiv:2602.21421v1 Announce Type: new Abstract: Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do...
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent...
arXiv:2602.21425v1 Announce Type: new Abstract: Instrumented Timed Up and Go (TUG) analysis can support clinical and research decision-making, but robust and reproducible markerless pipelines are...
arXiv:2602.21426v1 Announce Type: new Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging....
arXiv:2602.21428v1 Announce Type: new Abstract: Medical Vision Language Models (VLMs) can change their answers when clinicians rephrase the same question, which raises deployment risks. We...
arXiv:2602.21429v1 Announce Type: new Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions....
arXiv:2602.21435v1 Announce Type: new Abstract: Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework. However,...
arXiv:2602.21441v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not...
arXiv:2602.21442v1 Announce Type: new Abstract: The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms...
arXiv:2602.21444v1 Announce Type: new Abstract: 6G is deemed as a key technology to support emerging applications with stringent requirements for highly dependable and timecritical communication....
arXiv:2602.21445v1 Announce Type: new Abstract: Action chunking has recently emerged as a standard practice in flow-based Vision-Language-Action (VLA) models. However, the effect and choice of...
arXiv:2602.21447v1 Announce Type: new Abstract: Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and...
arXiv:2602.21448v1 Announce Type: new Abstract: Development of new multiscale mathematical models often entails considerable complexity and multiple undetermined parameters, typically arising from closure relations. To...
arXiv:2602.21450v1 Announce Type: new Abstract: This paper presents a novel vector field strategy for controlling fully-actuated systems on connected matrix Lie groups, ensuring convergence to...
arXiv:2602.21452v1 Announce Type: new Abstract: Introduction: Deep learning-based segmentation models are increasingly integrated into clinical imaging workflows, yet their robustness to adversarial perturbations remains incompletely...
arXiv:2602.21454v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) can be interpreted as discrete-time state-space models, where the state evolution corresponds to an infinite-impulse-response (IIR)...