Knowledge catalogue
arXiv:2503.15770v3 Announce Type: replace-cross Abstract: Depth foundation models (DFMs) offer strong learned priors for 3D perception from single RGB images but lack physical depth cues, leading to a
arXiv:2607.06999v1 Announce Type: cross Abstract: This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowled
arXiv:2607.06585v1 Announce Type: new Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in econ
arXiv:2607.07374v1 Announce Type: new Abstract: Dynamic environments remain a fundamental challenge for visual SLAM, where unreliable observations from moving objects and rapid motion degrade state es
arXiv:2603.15245v2 Announce Type: replace Abstract: Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) ge
arXiv:2607.07146v1 Announce Type: cross Abstract: The Internal Waves Service screens the Sentinel-1 Wave-mode archive for internal solitary waves, routing detections to experts whose adjudication time
arXiv:2607.06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or
arXiv:2607.07170v1 Announce Type: new Abstract: Online 3D scene graph generation builds a persistent, structured representation of a scene by incrementally fusing 2D observations into a global 3D grap
arXiv:2607.07634v1 Announce Type: cross Abstract: Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A ma
arXiv:2607.06829v1 Announce Type: new Abstract: Rail track extraction is essential for effective railway asset management and maintenance, especially in automated inspection and mapping workflows. Thi
arXiv:2607.07646v1 Announce Type: new Abstract: Does RL post-training merely amplify primitive skills already latent in a base model, or can it compose primitive skills into new higher-level strategie
arXiv:2603.00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning. Contemporary quant
arXiv:2607.07492v1 Announce Type: new Abstract: Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, an
arXiv:2607.06909v1 Announce Type: new Abstract: Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs (
arXiv:2512.17648v2 Announce Type: replace Abstract: Streaming Speech-to-Text Translation (StreamST) requires producing translations concurrently with incoming speech under strict latency constraints,
arXiv:2607.07542v1 Announce Type: cross Abstract: Powered prosthetic hands are frequently abandoned, largely due to the limited functionality of current devices that rely on surface electromyography (
arXiv:2607.06620v1 Announce Type: cross Abstract: Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry.
arXiv:2607.07386v1 Announce Type: new Abstract: Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention mod
arXiv:2607.06949v1 Announce Type: new Abstract: Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data, which is often difficult to acquire. While synthetic h
arXiv:2601.16946v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-base
arXiv:2607.06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and
arXiv:2501.18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized
arXiv:2502.00037v4 Announce Type: replace-cross Abstract: We introduce Superstate Quantum Mechanics (SQM), a theory that considers states in Hilbert space subject to multiple quadratic constraints, wi
arXiv:2607.06841v1 Announce Type: cross Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approac
arXiv:2607.06642v1 Announce Type: new Abstract: Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common h
Meta has made significant progress on its superintelligence research agenda, which aims to develop advanced AI systems with broad capabilities. The update covers Meta's infrastructure investments, res
arXiv:2607.07423v1 Announce Type: new Abstract: We prove that, in the realizable PAC setting, the sample complexity of exact-trace learning for full autoregressive Chain-of-Thought traces is upper bou
arXiv:2607.06640v1 Announce Type: cross Abstract: A learned world model is usually judged by how faithfully it reconstructs its observations or predicts reward, as though quality were something the mo
arXiv:2604.00533v2 Announce Type: replace Abstract: Large Language Models (LLMs) generalize across tasks through reusable representations and flexible reasoning, yet remain brittle in real deployment
arXiv:2607.07350v1 Announce Type: new Abstract: Limited flight endurance significantly restricts the operational range of unmanned aerial vehicles (UAVs) in long duration missions such as surveillance
arXiv:2607.07408v1 Announce Type: new Abstract: Automatic prosodic segmentation identifies boundaries between speech units from acoustic and linguistic evidence. Although recent deep learning approach
arXiv:2507.10881v2 Announce Type: replace Abstract: Tubular tree structures, such as blood vessels and airways, are essential in human anatomy and accurately tracking them while preserving their topol
arXiv:2607.06638v1 Announce Type: new Abstract: Self-paced learning (SPL) is an effective learning paradigm that simulates the human learning process by progressing from easy to difficult samples base
arXiv:2507.14177v2 Announce Type: replace-cross Abstract: This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose h
arXiv:2607.06570v1 Announce Type: cross Abstract: Value-of-information (VOI) analysis is usually conducted under a single probability measure. However, in practice, the available evidence often pins t
arXiv:2607.07099v1 Announce Type: new Abstract: Squint and cataract are major ocular disorders that majorly affect visual perception and interaction capability. This paper proposes a real-time video-b
arXiv:2507.05116v5 Announce Type: replace-cross Abstract: Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural languag
arXiv:2607.06988v1 Announce Type: cross Abstract: Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot dem
arXiv:2607.07127v1 Announce Type: cross Abstract: Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in m
arXiv:2506.19348v2 Announce Type: replace Abstract: Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing
arXiv:2607.07395v1 Announce Type: cross Abstract: Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-sh
arXiv:2606.19042v2 Announce Type: replace-cross Abstract: In vibe coding, an emerging AI-driven paradigm, an LLM generates an entire program from a natural language prompt, but what happens to the var
arXiv:2607.07123v1 Announce Type: new Abstract: Segmenting slender curvilinear structures such as retinal vessels, cracks, and roads demands topological correctness, as even a single-pixel discontinui
arXiv:2607.06115v1 Announce Type: cross Abstract: In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the sur
arXiv:2512.07997v2 Announce Type: replace-cross Abstract: Gestures are an integral part of our daily interactions with the environment. Hand gesture recognition (HGR) is the process of interpreting hu
arXiv:2601.17257v2 Announce Type: replace Abstract: We introduce a constrained optimization framework for training transformers that behave like optimization descent algorithms. Specifically, we enfor
arXiv:2511.17240v3 Announce Type: replace-cross Abstract: We study the problem of learning an unknown graph via group queries on node subsets, where each query reports whether at least one edge is pre
arXiv:2210.16286v2 Announce Type: replace Abstract: To understand the training dynamics of neural networks, prior studies have considered the mean-field limit of two-layer neural networks as the width
arXiv:2602.11059v2 Announce Type: replace-cross Abstract: This paper addresses the issue of inversion in cases where (1) the observation system is modeled by a linear transformation and additive error
arXiv:2607.05406v1 Announce Type: cross Abstract: Large language models generate code from natural language prompts, enabling 'vibe coding,' which allows non-programmers to develop computational solut
arXiv:2607.06479v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physica
arXiv:2607.05467v1 Announce Type: new Abstract: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream
arXiv:2607.05461v1 Announce Type: cross Abstract: Existing methods for testing deep neural networks (DNNs) primarily prioritize test inputs likely to reveal model faults under a fixed labeling budget.
arXiv:2607.06457v1 Announce Type: new Abstract: Audio Descriptions (ADs) narrate visual content for Blind and Low Vision (BLV) audiences during gaps in audiovisual media. There is growing momentum aro
arXiv:2607.01499v2 Announce Type: replace Abstract: Recent advances in Image-to-Video generation allow a single image to be animated into a convincing video under text guidance, raising serious copyri
arXiv:2607.05726v1 Announce Type: new Abstract: Association unlearning aims to disable learned label-attribute shortcuts while preserving task performance. Existing evaluations mainly measure output-l
arXiv:2607.06364v1 Announce Type: new Abstract: Mapping cloud security controls to technical metrics is currently a manual process. This paper proposes domain adaptation of Sentence Transformer models
arXiv:2503.17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps. Quantum kernels follow the same principle: input data are encoded into quantum states, whi
arXiv:2607.05473v1 Announce Type: cross Abstract: According to commonly consented theories, the minimum hardware requirement for gaze tracker is one camera and two light sources to realize gaze estima
arXiv:2607.05445v1 Announce Type: cross Abstract: Extended Reality (XR) wearables require always-on perception within tight power envelopes of a few watts and motion-to-photon latency budgets below 20