Search: “papers”

arXiv:2605.16257v1 Announce Type: new Abstract: Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands

arXiv:2605.15352v1 Announce Type: new Abstract: Opening heavy, self closing doors, especially those that require pulling remains a long standing challenge in robotics. Humans naturally employ both arm

arXiv:2605.15725v1 Announce Type: cross Abstract: Latent Action Models (LAMs) enable the learning of world models from unlabeled video by inferring abstract actions between consecutive frames. However

arXiv:2605.15304v1 Announce Type: new Abstract: The relations connecting propositions in discourse such as cause (A because B) or concession (A although B) are a subject of intense interest in Computa

arXiv:2605.15368v1 Announce Type: new Abstract: Group-convolutional neural networks (GCNNs) are among the most important methods for introducing symmetry as an inductive bias in deep learning: In each

arXiv:2605.15205v1 Announce Type: new Abstract: Improving the Theory of Mind (ToM) capability of Large Language Models (LLMs) is crucial for effective social interactions between these AI models and h

arXiv:2605.15543v1 Announce Type: cross Abstract: Many games of interest in the real world are often intractably large, thereby necessitating the use of game abstraction to shrink them in size, typica

arXiv:2512.09673v3 Announce Type: replace-cross Abstract: Equivariant neural networks encode the intrinsic symmetry of data as an inductive bias, which has achieved impressive performance in wide doma

arXiv:2605.15221v1 Announce Type: cross Abstract: AlphaEvolve and FunSearch have demonstrated the potential of combining large language models (LLMs) with evolutionary search for automated algorithm d

arXiv:2605.15586v1 Announce Type: cross Abstract: Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of

arXiv:2512.15067v3 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to e

arXiv:2605.15249v1 Announce Type: cross Abstract: AI models are increasingly deployed in cloud-native environments to support scalable and automated services. However, while platforms such as Kubernet

arXiv:2605.12581v1 Announce Type: cross Abstract: Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a fundamental challenge

arXiv:2605.16223v1 Announce Type: cross Abstract: Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natur

arXiv:2605.15824v1 Announce Type: new Abstract: Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot suppor

arXiv:2504.13850v2 Announce Type: replace-cross Abstract: Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low res

arXiv:2602.20630v4 Announce Type: replace Abstract: Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning

arXiv:2605.15880v1 Announce Type: cross Abstract: Thermal infrared imaging is robust to illumination variations and smoke interference, making it important for all-weather perception. However, the lac

arXiv:2605.15575v1 Announce Type: new Abstract: Relational graph learning models relational databases as graphs and has demonstrated superior performance on a wide range of relational predictive tasks

arXiv:2502.12187v3 Announce Type: replace Abstract: Hallucinations, a phenomenon where a language model (LM) generates nonfactual content, pose a significant challenge to the practical deployment of L

arXiv:2605.15689v1 Announce Type: new Abstract: Fine-grained image recognition classifies subcategories such as bird species or car models. While state-of-the-art (SOTA) models are accurate, they are

arXiv:2603.13864v2 Announce Type: replace-cross Abstract: Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems

arXiv:2605.15384v1 Announce Type: cross Abstract: Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time

arXiv:2605.16069v1 Announce Type: new Abstract: Timeseries regression models often struggle to leverage large volumes of labeled multimodal data, particularly when the data are irregularly sampled or

arXiv:2506.23552v2 Announce Type: replace Abstract: The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS

arXiv:2605.15463v1 Announce Type: new Abstract: As machine learning models grow in complexity, they increasingly struggle with three conflicting demands: the need for high accuracy, the requirement fo

arXiv:2605.16154v1 Announce Type: new Abstract: Reinforcement learning (RL) allows vision-language-action (VLA) policies to generalize beyond their training distribution by optimizing directly for tas

arXiv:2605.15760v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) optimization is most commonly performed using standard optimizers (Adam, SGD). While stable across diverse scenes, standard

arXiv:2605.15535v1 Announce Type: new Abstract: Underwater salient object detection (USOD) has attracted increasing attention for underwater visual scene understanding and vision-guided robotic applic

arXiv:2605.15242v1 Announce Type: new Abstract: The reliability of Healthcare Information Systems (HIS) is frequently compromised by human-induced data entry errors, which existing statistical anomaly

arXiv:2605.15621v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but their inference cost grows rapidly with the number of visual tokens, e

arXiv:2605.15231v1 Announce Type: cross Abstract: Nonlinear finite element crash simulations are accurate but computationally expensive, limiting their use in iterative design optimisation. Machine-le

arXiv:2605.15326v1 Announce Type: new Abstract: Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlu

arXiv:2602.10687v3 Announce Type: replace-cross Abstract: Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and vi

arXiv:2605.15860v1 Announce Type: new Abstract: Accurate geometric calibration of RGB-thermal infrared (TIR) stereo camera systems is essential for multimodal building envelope analysis, yet remains c

arXiv:2409.03897v3 Announce Type: replace Abstract: Large-scale multi-agent systems are often deployed across wide geographic areas, where agents interact with heterogeneous environments. There is an

arXiv:2602.03922v3 Announce Type: replace Abstract: Standard sequence mixing layers used in language models struggle to balance efficiency and performance. Self-attention performs well on long context

arXiv:2605.15553v1 Announce Type: cross Abstract: Sixth-generation mobile networks (6G) are approaching a structural inflection point. Five generations of vendor-led architectures have left operators

arXiv:2605.15765v1 Announce Type: cross Abstract: Optimization problems with drones are widely studied in a variety of civilian tasks, mainly due to their ability to traverse rough terrains and to car

arXiv:2605.15647v1 Announce Type: new Abstract: Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and

arXiv:2605.15253v1 Announce Type: new Abstract: Machine learning research increasingly bifurcates into two disconnected modes: benchmark-driven engineering that prioritizes metrics over understanding,

arXiv:2605.15363v1 Announce Type: new Abstract: Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and s

arXiv:2508.08431v3 Announce Type: replace-cross Abstract: Spectral variability significantly impacts the accuracy and convergence of hyperspectral unmixing algorithms. Many methods address complex spe

arXiv:2511.09884v2 Announce Type: replace Abstract: Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, determinist

arXiv:2403.13805v2 Announce Type: replace-cross Abstract: CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of c

arXiv:2605.16065v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3D-GS) enables real-time 3D scene reconstruction but lacks robust segmentation for editing tasks such as object removal, extrac