Knowledge catalogue
arXiv:2609.28459v1 Announce Type: new Abstract: We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replac
arXiv:2609.27418v1 Announce Type: new Abstract: Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two
arXiv:2609.27389v1 Announce Type: cross Abstract: Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is co
arXiv:2609.27664v1 Announce Type: new Abstract: Cooperation emergence is a central problem in multi-agent systems because decentralized agents must coordinate while adapting to the changing behavior o
arXiv:2609.27337v1 Announce Type: cross Abstract: Open RAN (O-RAN) slicing xApps must adapt resource allocations to changing channel conditions and traffic demands while meeting service-level agreemen
arXiv:2609.28150v1 Announce Type: new Abstract: Closed-loop revision is increasingly used in large language model (LLM) applications, but failures may reflect incomplete feedback or ineffective respon
arXiv:2609.27860v1 Announce Type: new Abstract: A pointwise-unbiased one-bit compressor reconstructs every real input in expectation while transmitting one bit. For a scalar source P with CDF F, mean
arXiv:2609.28053v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) training requires global load balance to prevent expert under-utilization and local balance for efficient expert-parallel exe
arXiv:2609.26926v1 Announce Type: cross Abstract: Large-scale text annotation brings expert insight to millions of documents, often through a codebook that AI annotators follow. Developing a robust co
arXiv:2609.27309v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) trains a team of agents that share one environment and learn their policies together. Training maximizes the
arXiv:2507.16761v3 Announce Type: replace Abstract: Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical image analysis.
arXiv:2609.27883v1 Announce Type: cross Abstract: Closed-loop discovery systems increasingly execute experiments and update decisions autonomously, turning record integrity into part of the experiment
arXiv:2609.27077v1 Announce Type: new Abstract: To avoid collisions, a robot must repeatedly predict where nearby agents may move, usually with an imperfect model of their dynamics. Reachable sets pro
arXiv:2410.00848v2 Announce Type: replace-cross Abstract: A faster implementation of mixtures of Manly transformations is proposed. This method, called FastManly, uses Newton's method for optimization
arXiv:2609.27571v1 Announce Type: cross Abstract: Deployment requires an agent to turn application code into a running system whose services connect, become ready, and remain observable. FDE-Bench eva
arXiv:2609.28105v1 Announce Type: cross Abstract: Multi-center clinical studies and biomedical research collaborations increasingly seek to utilize data across centers to build models that generalize
arXiv:2609.27654v1 Announce Type: cross Abstract: Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending,
arXiv:2609.27110v1 Announce Type: cross Abstract: Vision-language models (VLMs) are deployed as zero-shot judges of image aesthetics, and panels of several models are recommended, on thin evidence, as
arXiv:2609.27710v1 Announce Type: new Abstract: Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high c
arXiv:2609.28110v1 Announce Type: cross Abstract: Dental cone-beam computed tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captur
arXiv:2511.19486v3 Announce Type: replace-cross Abstract: Driven by recent advances in artificial intelligence, a growing literature has demonstrated the potential of using large language models (LLMs
arXiv:2609.28395v1 Announce Type: new Abstract: Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally
arXiv:2609.27160v1 Announce Type: new Abstract: Unlike most intensely physical pursuits, surgical robotic teleoperation training focuses primarily on task outcomes rather than surgeon body posture or
arXiv:2609.28182v1 Announce Type: new Abstract: Coordinating large population of flexible grid-edge devices can alleviate the need for time-consuming and capital-intensive network upgrades, and AI-bas
arXiv:2609.27657v1 Announce Type: cross Abstract: Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples f
arXiv:2609.26857v1 Announce Type: cross Abstract: Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversabilit
arXiv:2609.27385v1 Announce Type: cross Abstract: Time-series foundation models (TSFMs) provide forecasts for operational decisions, but accuracy alone does not determine their value. Evaluating agent
arXiv:2609.27399v1 Announce Type: cross Abstract: Recent zero-shot text-to-speech (ZS-TTS) systems can reproduce a speaker's voice with high fidelity from only a few seconds of reference speech, raisi
arXiv:2609.28378v1 Announce Type: cross Abstract: Humanoid control, leveraging human demonstrations, has achieved diverse, agile, and natural locomotion behaviors through reinforcement learning (RL).
arXiv:2609.28216v1 Announce Type: cross Abstract: Agentic engineering systems can edit repositories, run tools and tests, build firmware, synthesize schematics, and prepare deployable or manufacturabl
arXiv:2609.28222v1 Announce Type: new Abstract: Unified 3D vision-language systems must combine complementary geometry, scale, and appearance cues while supporting tasks from instance segmentation to
arXiv:2609.28192v1 Announce Type: new Abstract: Remote sensing change detection (RSCD) is essential for monitoring land-cover changes and urban development. However, most methods demand pixel-level ch
arXiv:2609.28183v1 Announce Type: new Abstract: Electrocardiography (ECG) contains subject-specific morphology that supports biometric recognition, yet image-based performance depends on how the wavef
arXiv:2609.27118v1 Announce Type: new Abstract: Greenhouse climate management aims to improve crop production while limiting energy use. This requires knowing how a crop will respond before conditions
arXiv:2609.27363v1 Announce Type: new Abstract: Camera localization in a prior LiDAR map provides a persistent geometric reference for long-term robotic navigation, yet remains challenging because of
arXiv:2609.27833v1 Announce Type: new Abstract: Many computations admit several valid execution orders because independent subgoals or disjoint state updates can commute. Reinforcement learning with v
arXiv:2609.27939v1 Announce Type: new Abstract: Natural language processing (NLP) applied to open-ended teaching-evaluation comments (Student Evaluation of Teaching, SET) has tracked the field's techn
arXiv:2609.28414v1 Announce Type: cross Abstract: Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulati
arXiv:2603.21565v2 Announce Type: replace-cross Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) is severely challenged by coherent speckle noise, whose interference can be pr
arXiv:2609.27244v1 Announce Type: new Abstract: A neural network's layers can be treated as time steps of a state-space model, turning Bayesian training into a smoothing problem: a forward pass propag
arXiv:2609.27779v1 Announce Type: new Abstract: Class-guided 3D object generation is important for intelligent content creation, virtual environments, and digital asset design. Although 3D Gaussian Sp
arXiv:2609.27850v1 Announce Type: new Abstract: 3D Gaussian Splatting provides an efficient representation for 3D reconstruction, and recent extensions attach semantic attributes to Gaussians for open
arXiv:2609.27264v1 Announce Type: new Abstract: We present GaussPDE, a framework that injects physically structured partial differential equation (PDE) dynamics into pretrained 3D Gaussian scenes with
arXiv:2609.27682v1 Announce Type: new Abstract: In-context learning (ICL) enables large vision-language models (LVLMs) to perform tasks by following patterns from in-context examples, yet its potentia
arXiv:2609.28258v1 Announce Type: cross Abstract: Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies
arXiv:2609.27848v1 Announce Type: new Abstract: The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We propo
arXiv:2609.27370v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial
arXiv:2609.27018v1 Announce Type: new Abstract: Residual vector quantization (RVQ) turns physiological waveforms into compact token sequences, but conventional masked modeling treats every incorrect t
arXiv:2609.28194v1 Announce Type: cross Abstract: Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, pr
arXiv:2609.27156v1 Announce Type: new Abstract: Large reasoning models can produce correct yet unnecessarily long reasoning traces. Existing methods improve reasoning efficiency with trajectory-level
arXiv:2609.28179v1 Announce Type: new Abstract: GPU robotics lacks the reusable numerical infrastructure of mature CPU stacks, instead relying on compiler frameworks that introduce overhead or repeate
arXiv:2609.27912v1 Announce Type: new Abstract: Global forecasting models pool many series and learn one shared function. Gradient-boosted trees are their most common form. We measure a failure of thi
arXiv:2609.27695v1 Announce Type: new Abstract: Perceiving objects in the environment is a fundamental capability of autonomous robots. In dark or low-light environments, external cameras often fail t
arXiv:2511.22857v2 Announce Type: replace Abstract: Inverse rendering of indoor scenes remains challenging due to the ambiguity between reflectance and lighting, exacerbated by inter-reflections among
arXiv:2601.05353v3 Announce Type: replace Abstract: Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk. However, cur
arXiv:2609.26806v1 Announce Type: cross Abstract: This paper presents a complete, structure-preserving port to Lean 4 of the Isabelle/HOL dataset accompanying Benzmuller and Scott's study of Godel's m
arXiv:2609.27278v1 Announce Type: new Abstract: Learning a sparse graph from scarce data is practically important but challenging. Motivated by the desirable combination of local sparsity and strong g
arXiv:2609.27597v1 Announce Type: new Abstract: The growing demand for automation in the mining industry, particularly for the autonomous operation of articulated dump trucks (ADTs), has drawn increas
arXiv:2609.27821v1 Announce Type: new Abstract: Bounding-box scores on RefCOCO-family grounding leave little room to distinguish frontier vision-language systems, yet boxes discard object shape. We in
arXiv:2609.27353v1 Announce Type: new Abstract: Web agents are usually evaluated in live environments, where environment state and judge models drift between runs, so the same checkpoint rarely reprod