Knowledge catalogue
arXiv:2608.14205v1 Announce Type: new Abstract: Load imbalance poses a major bottleneck to the efficiency of expert parallelism in distributed inference of Mixture-of-Experts (MoE) models. The most he
arXiv:2608.13675v1 Announce Type: cross Abstract: Between October 2018 and July 2026 AI models progressed from simple systems like BERT to massive agents that solve complex math and write software. Th
arXiv:2608.13856v1 Announce Type: cross Abstract: Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optical GeoAI
arXiv:2502.19507v2 Announce Type: replace Abstract: In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a mod
arXiv:2608.14120v1 Announce Type: cross Abstract: Lagrangian modeling is vital to fluid dynamics, as it characterizes particle transport and complements the Eulerian description.However, Lagrangian tr
arXiv:2608.13787v1 Announce Type: new Abstract: AI agents increasingly act on their users' behalf, handling tasks such as scheduling meetings, comparing offers, and haggling over prices. These princip
arXiv:2608.13581v1 Announce Type: cross Abstract: Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantial
arXiv:2608.14405v1 Announce Type: cross Abstract: Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation. While generative
arXiv:2608.13741v1 Announce Type: new Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation. However, existing text-co
arXiv:2608.14389v1 Announce Type: cross Abstract: Existing palm presentation attack detection (PAD) datasets are often limited by static imagery, restricted acquisition conditions, or insufficient mul
arXiv:2608.14496v1 Announce Type: cross Abstract: Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets.
arXiv:2502.07580v4 Announce Type: replace Abstract: We present a novel view of diffusion-like generative modeling from the perspective of iterative Gaussian posterior inference. By treating the genera
arXiv:2608.13866v1 Announce Type: cross Abstract: Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some
arXiv:2608.14428v1 Announce Type: new Abstract: 3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable p
arXiv:2602.23459v2 Announce Type: replace Abstract: Psychiatric questionnaires are highly context sensitive and often only weakly predict subsequent symptom severity, which makes the prognostic relati
arXiv:2602.21956v2 Announce Type: replace Abstract: Text Image Machine Translation (TIMT) aims to translate text embedded in images in the source-language into target-language, requiring synergistic i
arXiv:2608.13723v1 Announce Type: new Abstract: Object-goal navigation requires an agent to reason over object relationships and prioritize target-relevant objects for efficient decision making in uns
arXiv:2608.14252v1 Announce Type: new Abstract: Recent work suggests that some large language model representations have content or reference. Grounding can secure either without supplying live routes
arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for i
arXiv:2608.14032v1 Announce Type: cross Abstract: Existing multimodal RAG methods often flatten structured documents into isolated text and image units, weakening the source organization and local tex
arXiv:2608.14528v1 Announce Type: new Abstract: This study investigates the methodological and theoretical properties of session handover in applications that use large language models. A task may con
arXiv:2608.13601v1 Announce Type: new Abstract: Active learning can reduce labeling cost by selecting informative examples, but the most uncertain examples may also be the hardest to label correctly.
arXiv:2608.13951v1 Announce Type: new Abstract: Scaling agent capability has largely focused on improving the model, yet an interactive agent acts through a runtime harness that mediates context, tool
arXiv:2608.14055v1 Announce Type: new Abstract: Authoritative scientific knowledge in geoscience remains largely trapped in legacy monographs and historical literature, where unstructured text and com
arXiv:2608.13827v1 Announce Type: new Abstract: Machine-learned physical surrogate models have become promising alternatives to mesh-based numerical solvers. Among them, graph neural networks (GNNs) a
arXiv:2608.14136v1 Announce Type: cross Abstract: Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptiv
arXiv:2503.06821v2 Announce Type: replace Abstract: The exploration of Bird's-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving
arXiv:2608.13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime. Large-scale language-conditioned policies have
arXiv:2608.13617v1 Announce Type: new Abstract: Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either par
arXiv:2608.14210v1 Announce Type: cross Abstract: Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious
arXiv:2608.13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for cent
arXiv:2608.14004v1 Announce Type: new Abstract: In-context learning is commonly formalized as inference from examples of a function. Partial orders instead combine transitivity, antisymmetry, and inco
arXiv:2608.13958v1 Announce Type: new Abstract: How do we govern AI systems whose reasoning we cannot fully inspect? Governance does not require understanding a system's reasoning. It requires stating
arXiv:2512.14338v4 Announce Type: replace Abstract: Many learning problems are organized by group symmetries. While invariance is often imposed through architectures or group averaging, we ask when it
arXiv:2608.13564v1 Announce Type: new Abstract: Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable e
arXiv:2608.14457v1 Announce Type: new Abstract: The majority of work on summarization evaluation focuses on general summary quality (e.g., ROUGE, BERTScore) or specific desired properties (e.g., reada
arXiv:2608.14070v1 Announce Type: new Abstract: Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person's clothing while strictly preserving the
arXiv:2608.14317v1 Announce Type: cross Abstract: This research developed a neural network-based model to extract various information from 2D floor plans. We detect lighting symbols, identify the appr
arXiv:2608.13575v1 Announce Type: cross Abstract: Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, includin
arXiv:2608.14290v1 Announce Type: new Abstract: We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) t
arXiv:2204.13805v5 Announce Type: replace-cross Abstract: Gender gaps in how scientific work is evaluated are well documented, but their sources remain debated. We ask whether an overlooked factor---t
arXiv:2608.14394v1 Announce Type: new Abstract: Perception is a fundamental component of autonomous driving systems. While LiDAR-based methods have achieved remarkable progress in object detection, th
arXiv:2608.13588v1 Announce Type: cross Abstract: Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation syste
arXiv:2608.13580v1 Announce Type: cross Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric la
arXiv:2608.13863v1 Announce Type: new Abstract: Deep neural network (DNN) inference on mobile devices often incurs high latency and energy consumption due to limited computing and memory resources. To
arXiv:2608.14425v1 Announce Type: new Abstract: LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a
arXiv:2608.13878v1 Announce Type: new Abstract: In mixed traffic, decision-making for autonomous vehicles (AVs) confronts three interrelated challenges. First, physics-based priors incorporated into r
arXiv:2608.13769v1 Announce Type: new Abstract: Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such
arXiv:2608.14191v1 Announce Type: cross Abstract: The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods
arXiv:2608.13562v1 Announce Type: new Abstract: Modern operational systems face uncertainty even in routine conditions, where rare, bursty, and self-exciting events emerge from both exogenous covariat
arXiv:2608.13807v1 Announce Type: cross Abstract: Peripheral nerves buried beneath intact tissue are difficult to visualize during surgery and remain inaccessible to white light wide-field imaging and
arXiv:2608.13695v1 Announce Type: cross Abstract: Large language model providers routinely cite multilingual safety benchmarks spanning a dozen or more languages as evidence that their models are safe
arXiv:2602.12314v2 Announce Type: replace-cross Abstract: We present LatentAM, an online 3D Gaussian Splatting (3DGS) mapping framework that builds scalable latent feature maps from streaming RGB-D ob
arXiv:2608.14150v1 Announce Type: new Abstract: The second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge evaluates two tasks over complete, unsegmented multilingual conversatio
arXiv:2602.11903v3 Announce Type: replace-cross Abstract: No-reference video quality assessment (NR-VQA) for gaming videos is challenging due to limited human-rated datasets and unique content charact
arXiv:2608.13684v1 Announce Type: new Abstract: This paper describes a neurosymbolic architecture for learning to assemble novel structures using evidence from embodied conversations and task demonstr
arXiv:2608.14281v1 Announce Type: new Abstract: Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming sy
arXiv:2604.06452v2 Announce Type: replace Abstract: When a colleague starts explaining something you already understand, you interrupt them. This simple act, a listener taking control of the conversat
arXiv:2608.14114v1 Announce Type: new Abstract: As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomou
arXiv:2608.14511v1 Announce Type: cross Abstract: High-order multiple-input multiple-output (MIMO) detection requires efficient search over a large discrete symbol space while producing reliable soft