Knowledge catalogue
arXiv:2610.05921v1 Announce Type: cross Abstract: In generative models, Optimal Transport (OT) is used to improve Flow Matching (FM) by reducing noise-data coupling cost. However, different noise-to-o
arXiv:2610.04220v1 Announce Type: new Abstract: The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is i
arXiv:2610.05765v1 Announce Type: new Abstract: Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse
arXiv:2610.04931v1 Announce Type: new Abstract: This paper introduces a real-time thumbnail optimization system deployed at a global O(B) scale on a major short-form video platform. Unlike traditional
arXiv:2610.06809v1 Announce Type: new Abstract: Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free
arXiv:2610.06083v1 Announce Type: new Abstract: Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper s
arXiv:2610.05293v1 Announce Type: new Abstract: We describe the BossouChimpanzee video dataset, a unique long-term visual record of wild chimpanzees at an outdoor laboratory for field experiments in B
arXiv:2610.04878v1 Announce Type: cross Abstract: Human-agent evaluations often compress interaction into a single performance score, even when human and automated policies adapt differently over time
arXiv:2610.06571v1 Announce Type: new Abstract: Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences,
arXiv:2610.06725v1 Announce Type: new Abstract: Mixture-of-experts (MoE) layers increase model capacity without a proportional increase in per-example computation. However, conventional flat routers c
arXiv:2610.03759v1 Announce Type: cross Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases
arXiv:2610.04946v1 Announce Type: new Abstract: Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and he
arXiv:2103.16559v3 Announce Type: cross Abstract: Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art met
arXiv:2610.04402v1 Announce Type: cross Abstract: We investigate the mutual visibility problem for a swarm of nge3 autonomous mobile robots under the budget-constrained mobility fault model. The robot
arXiv:2610.05604v1 Announce Type: new Abstract: This paper reports on the challenges encountered and lessons learned during the development and deployment of a flexible multi-sensor platform for auton
arXiv:2610.05845v1 Announce Type: new Abstract: Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for co
arXiv:2610.05040v1 Announce Type: new Abstract: Mapping post-harvest burned cropland is difficult when fires are small and fragmented and reliable labels are scarce. We developed a calibrated weak-sup
arXiv:2610.05400v1 Announce Type: cross Abstract: Long-video agents can actively gather question-relevant evidence, but they typically leave a central decision implicit: when has the agent seen enough
arXiv:2610.06035v1 Announce Type: new Abstract: Recovering geometry, materials, and lighting from photographs is highly ambiguous when only static illumination is available. Active-lighting setups red
arXiv:2610.05618v1 Announce Type: cross Abstract: Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force
arXiv:2610.04444v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape conseque
arXiv:2610.05863v1 Announce Type: new Abstract: High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constraine
arXiv:2610.04405v1 Announce Type: new Abstract: Despite the growing popularity of physics-informed neural networks (PINNs), their applicability in the long-time integration of partial differential equ
arXiv:2610.04199v1 Announce Type: new Abstract: A robotic microscope watching living cells cannot afford to look as often as it would like. Every volume it acquires costs photons the specimen does not
arXiv:2610.06260v1 Announce Type: new Abstract: Diffusion transformers (DiTs) achieve state-of-the-art image generation, but their sampling cost limits deployment. Quantizing both weights and activati
arXiv:2610.06238v1 Announce Type: new Abstract: We investigate the use of formal methods to provide tight and sound generalization bounds for learning algorithms. By casting the traditional notion of
arXiv:2610.05870v1 Announce Type: new Abstract: Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detecto
arXiv:2610.05078v1 Announce Type: new Abstract: Accurate retinal vessel segmentation requires features that capture vascular geometry while preserving information for fine-scale reconstruction. We pro
arXiv:2406.12150v2 Announce Type: replace Abstract: Post-hoc attribution is widely used to identify important model inputs, but evaluating whether these attributions identify truly informative feature
arXiv:2610.05305v1 Announce Type: cross Abstract: Large Language Model (LLM) inference has become the dominant workload in modern AI systems, requiring serving infrastructures to maximize throughput w
arXiv:2610.04357v1 Announce Type: new Abstract: We give checkable positive-definiteness criteria for the limiting neural tangent kernel (NTK) and high-probability finite-width gradient-descent guarant
arXiv:2610.06687v1 Announce Type: new Abstract: While existing camera-controllable video generation models can produce visually compelling sequences, preserving intrinsic 4D spatiotemporal coherence r
arXiv:2610.05744v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) has been widely adopted in recent large language model (LLM) architectures. However, scaling up MoE in LLM training introduces
arXiv:2610.04020v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingl
arXiv:2610.04204v1 Announce Type: new Abstract: Training large language models (LLMs) entails a fundamental trade-off: memory-efficient optimizers such as Adam discard cross-parameter curvature, where
arXiv:2610.05411v1 Announce Type: new Abstract: Motion capture data is rarely directly usable, as they typically exhibit missing segments, jitter, drift and contact artifacts. Traditionally, corrupted
arXiv:2610.06829v1 Announce Type: cross Abstract: Open-source web agents are now strong enough to execute realistic browser tasks, but training them with reinforcement learning still depends on weak s
arXiv:2510.07971v2 Announce Type: replace Abstract: Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loop
arXiv:2610.04558v1 Announce Type: cross Abstract: We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in project
arXiv:2610.06679v1 Announce Type: new Abstract: Tabular foundation models perform in-context learning (ICL) by conditioning predictions on labeled training examples provided as context. Unlike traditi
arXiv:2610.06138v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-const
arXiv:2610.05324v1 Announce Type: cross Abstract: Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while
arXiv:2610.05033v1 Announce Type: new Abstract: Generating a high-quality gaming world from a natural-language game intent requires joint reasoning about scene structure, spatial layout, gameplay obje
arXiv:2610.05481v1 Announce Type: cross Abstract: Large language models for code generation often fail on execution, multilingual coverage, and contamination control, especially under frozen backbone
arXiv:2610.05053v1 Announce Type: new Abstract: Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods larg
arXiv:2610.05913v1 Announce Type: new Abstract: Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditi
arXiv:2610.05590v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction tests whether models can identify clinically significant interactions for drugs without training-time
arXiv:2610.05898v1 Announce Type: new Abstract: Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these respo
arXiv:2610.05936v1 Announce Type: cross Abstract: One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network
arXiv:2603.11048v2 Announce Type: replace Abstract: We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting from charact
arXiv:2607.14539v2 Announce Type: replace Abstract: Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi-robot systems. However, robots encounter
arXiv:2610.05332v1 Announce Type: new Abstract: This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and
arXiv:2610.04680v1 Announce Type: new Abstract: Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However
arXiv:2610.05127v1 Announce Type: new Abstract: Physics-informed neural network (PINN) training minimizes a weighted combination of partial differential equation (PDE), boundary-condition, and initial
arXiv:2511.19453v2 Announce Type: replace-cross Abstract: Autonomous vehicles continuously generate large volumes of heterogeneous sensor and state data streams. While this data supports real-time aut
arXiv:2610.06520v1 Announce Type: new Abstract: Neurons of the brain exhibit a rich repertoire of electrophysiology dynamics with the same repeated stimulus eliciting very different voltage responses
arXiv:2610.06104v1 Announce Type: new Abstract: Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles. We presen
arXiv:2610.04605v1 Announce Type: new Abstract: Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cues to semantically meaningful conce
arXiv:2610.04239v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) for large language models (LLMs) aims to provide reliable measures of predictive confidence, yet current methods are o
arXiv:2610.04143v1 Announce Type: new Abstract: Most feature-based choice models, classical and deep, score items and apply a single softmax. We introduce consideration circuits (CC), feature-based mo