Knowledge catalogue
arXiv:2505.03597v2 Announce Type: replace Abstract: Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and w
arXiv:2605.04519v1 Announce Type: new Abstract: Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder m
arXiv:2605.04071v1 Announce Type: new Abstract: Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to intervention tokens and te
arXiv:2605.05077v1 Announce Type: new Abstract: Accurate image segmentation is essential for modern computer vision applications such as image editing, autonomous driving, and medical image analysis.
arXiv:2511.01202v3 Announce Type: replace-cross Abstract: Despite the unprecedented empirical triumphs of LLMs across diverse real-world applications, the prevailing research paradigm remains overwhel
arXiv:2605.04327v1 Announce Type: new Abstract: We propose an architecture for integrating high-level, human-provided safety rules and operator-aligned semantic preferences into autonomous robot navig
arXiv:2605.04535v1 Announce Type: new Abstract: Inferring continuum models directly from video is hampered by two facts: the recorded field is uncalibrated image intensity rather than a physical state
arXiv:2501.14171v3 Announce Type: replace-cross Abstract: Multi-modal brain MRI provides essential complementary information for clinical diagnosis. However, acquiring all modalities in practice is of
arXiv:2605.04770v1 Announce Type: new Abstract: While zero-shot appearance-based 3D gaze estimation offers significant cost-efficiency by directly mapping RGB images to gaze vectors, its reliability i
arXiv:2605.04449v1 Announce Type: new Abstract: Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models
arXiv:2605.03383v1 Announce Type: new Abstract: Lithology classification aims to infer subsurface rock types from well-logging signals, supporting downstream applications like reservoir characterizati
arXiv:2605.04474v1 Announce Type: new Abstract: Geometry is central to PDE-governed systems, motivating shape optimization and inversion. Classical pipelines conduct costly forward simulation with geo
arXiv:2605.05164v1 Announce Type: new Abstract: Accurate analysis of histopathological images is critical for disease diagnosis and treatment planning. Whole-slide images (WSIs), which digitize tissue
arXiv:2605.03410v2 Announce Type: new Abstract: Out-of-distribution (OOD) detection identifies test samples that fall outside a model's training distribution, a capability critical for safe deployment
arXiv:2605.04279v1 Announce Type: new Abstract: Transformer self-attention can be interpreted as a gradient flow on the unit sphere, in which tokens evolve under softmax interaction potentials and ten
arXiv:2605.04643v1 Announce Type: new Abstract: Approximating the ideological position of Members of Parliament (MPs) is a fundamental task in political science, helping researchers understand legisla
arXiv:2605.04376v1 Announce Type: new Abstract: The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite thes
arXiv:2605.04698v1 Announce Type: cross Abstract: Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work inves
arXiv:2605.04435v1 Announce Type: new Abstract: Feedforward Gaussian Splatting has recently emerged as an efficient paradigm for 4D reconstruction in autonomous driving. However, in unstructured off-r
arXiv:2605.04581v1 Announce Type: new Abstract: Light field (LF) image super-resolution benefits from Epipolar Plane Images (EPIs), whose line slopes explicitly encode disparity. However, existing Tra
arXiv:2605.05072v1 Announce Type: new Abstract: 3D occupancy prediction aims to infer dense, voxel-wise scene semantics from sensor observations, where the 2D-to-3D view transformation serves as a cru
Nous Research announced improvements or updates to their Hermes Agent system, highlighting enhanced capabilities or performance. The post was shared on X (formerly Twitter), suggesting it covers recen
arXiv:2605.04191v1 Announce Type: cross Abstract: Public attitudes toward artificial intelligence are heterogeneous, ordinally measured, and poorly captured by any single dependency graph. Existing or
arXiv:2605.04594v1 Announce Type: new Abstract: Many real-world heterogeneous graphs exhibit pronounced heterophily, where connected nodes often have dissimilar labels or play different semantic roles
arXiv:2605.04304v1 Announce Type: cross Abstract: Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While ex
arXiv:2605.04257v1 Announce Type: new Abstract: Cold spraying is an increasingly common approach for repairing and manufacturing components due to its solid-state manufacturing capabilities. However,
arXiv:2507.15736v2 Announce Type: replace Abstract: Innovation is a key driving force of human civilization. As the body of knowledge has grown considerably, bridging knowledge across different discip
arXiv:2605.04064v1 Announce Type: cross Abstract: Reasoning capabilities are crucial for reliable medical visual question answering (VQA); however, existing datasets rarely include reasoning explanati
arXiv:2605.05050v1 Announce Type: cross Abstract: Gap-closing rate and visual looming swap discriminative dominance depending on deceleration intensity - a finding that reconciles a long-standing conf
arXiv:2605.04917v1 Announce Type: new Abstract: Learning tractable linear representations of nonlinear dynamical systems via Koopman operator theory is often hindered by dictionary selection, temporal
arXiv:2510.08431v3 Announce Type: replace Abstract: Although continuous-time consistency models (e.g., sCM, MeanFlow) are theoretically principled and empirically powerful for fast academic-scale diff
arXiv:2510.17903v3 Announce Type: replace-cross Abstract: This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially obser
arXiv:2512.23864v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain bl
arXiv:2605.04569v1 Announce Type: new Abstract: Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottle
arXiv:2605.04769v1 Announce Type: new Abstract: Heterogeneous Face Recognition (HFR) aims at matching face images captured across different sensing modalities, such as thermal-to-visible or near-infra
arXiv:2605.05110v1 Announce Type: new Abstract: Designing reward functions for agile robotic maneuvers in reinforcement learning remains difficult, and demonstration-based approaches often require ref
arXiv:2512.22588v3 Announce Type: replace Abstract: One of the main limitations of multirotor UAVs is their short flight time due to battery constraints. A practical solution for continuous operation
arXiv:2501.03972v2 Announce Type: replace Abstract: The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems of
arXiv:2605.03932v1 Announce Type: cross Abstract: Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed
arXiv:2312.06423v3 Announce Type: replace-cross Abstract: Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid prolif
arXiv:2602.22291v3 Announce Type: replace Abstract: While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we argue that a comprehens
arXiv:2605.04873v1 Announce Type: new Abstract: Recent advances in natural language processing have enabled increasingly accurate estimation of psychological traits from language. However, most existi
arXiv:2605.04308v1 Announce Type: new Abstract: Continual incorporation of new knowledge is essential for the long-term evolution of large language models (LLMs). Existing approaches typically rely on
arXiv:2605.03675v1 Announce Type: new Abstract: Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over
arXiv:2605.03216v1 Announce Type: cross Abstract: Strategy-proofness is a fundamental desideratum in mechanism design, ensuring truthful reporting and robust participation. Stability is another centra
arXiv:2605.05003v1 Announce Type: new Abstract: Reward models are a key component of large language model alignment, serving as proxies for human preferences during training. However, existing evaluat
arXiv:2605.04134v1 Announce Type: new Abstract: We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be e
arXiv:2605.04731v1 Announce Type: new Abstract: Building height (BH) and building footprint (BF) jointly describe the vertical and horizontal extent of the built environment and are required inputs fo
arXiv:2605.04058v1 Announce Type: new Abstract: Parameter-efficient transfer learning (PETL) has emerged as a pivotal paradigm for adapting pre-trained foundation models to downstream tasks, significa
arXiv:2605.04680v1 Announce Type: new Abstract: EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval. However, limited paired data and
arXiv:2605.04589v1 Announce Type: cross Abstract: A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically id
arXiv:2605.04313v1 Announce Type: new Abstract: Causal reasoning in natural language requires identifying relevant variables, understanding their interactions, and reasoning about effects and interven
arXiv:2605.04397v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) holds great promise for continuous heart-rate monitoring of drivers in intelligent vehicles. However, its performance
arXiv:2506.07548v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) has reached competitive performance on cooperative tasks against scripted adversaries, yet most meth
arXiv:2605.04838v1 Announce Type: cross Abstract: The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any cons
arXiv:2509.24943v2 Announce Type: replace Abstract: Long videos, characterized by temporal complexity and sparse task-relevant information, pose significant reasoning challenges for AI systems. Althou
arXiv:2603.22844v4 Announce Type: replace Abstract: Surgical smoke severely degrades intraoperative video quality, obscuring anatomical structures and limiting surgical perception. Existing learning-b
arXiv:2605.05163v1 Announce Type: new Abstract: Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on
arXiv:2605.05040v1 Announce Type: new Abstract: On-policy distillation is an efficient alternative to reinforcement learning, offering dense token-level training signals. However, its reliance on a st
arXiv:2605.04286v1 Announce Type: new Abstract: Climate classification plays a vital role in agricultural planning, hydrological studies, and climate science. One of the most widely used systems for c