Knowledge catalogue
arXiv:2604.14540v1 Announce Type: new Abstract: Detecting slow-moving landslides directly from wrapped Interferometric Synthetic Aperture Radar (InSAR) interferograms is crucial for efficient geohazar
arXiv:2604.06168v2 Announce Type: replace Abstract: World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model t
arXiv:2601.04442v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) have exhibited strong reasoning capabilities through chain-of-thought mechanisms that generate step-by-st
arXiv:2507.16005v2 Announce Type: replace-cross Abstract: Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations an
arXiv:2604.13705v1 Announce Type: new Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agen
Introduction Advances in AI model-powered exploitation have demonstrated that general-purpose AI models can excel at vulnerability discovery, even without being purpose-built for the task. Eventually,
arXiv:2510.20169v3 Announce Type: replace Abstract: Traveling Salesman Problem (TSP) is a classic NP-hard problem that has garnered significant attention from both academia and industry. While neural-
arXiv:2506.23640v2 Announce Type: replace-cross Abstract: Recently, researchers have explored ML-based Traffic Engineering (TE), leveraging neural networks to solve TE problems traditionally addressed
arXiv:2510.02213v2 Announce Type: replace Abstract: Density map estimation enables accurate object counting in heavily occluded, and densely packed scenes where detection-based counting fails. In mult
arXiv:2604.13981v1 Announce Type: new Abstract: Interpretability is essential for deploying object detection systems in critical applications, especially under low-quality imaging conditions that degr
arXiv:2604.13546v1 Announce Type: new Abstract: Conventional neural networks strictly separate learning and inference because if parameters are updated during inference, outputs become unstable and ev
Motif-Video-2B is a 2-billion-parameter open-source video generation diffusion transformer released by Motif Technologies in April 2026, capable of both text-to-video and image-to-video generation fro
arXiv:2604.14054v1 Announce Type: cross Abstract: Deep search agents have emerged as a promising paradigm for addressing complex information-seeking tasks, but their training remains challenging due t
arXiv:2512.14234v2 Announce Type: replace Abstract: Human communication is inherently multimodal and social: words, prosody, and body language jointly carry intent. Yet most prior systems model human
arXiv:2501.16154v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. Although these models sho
arXiv:2604.12647v1 Announce Type: cross Abstract: Automated respiratory audio analysis promises scalable, non-invasive disease screening, yet progress is limited by scarce labeled data and costly expe
arXiv:2505.17086v4 Announce Type: replace Abstract: Large Language Models (LLMs) equipped with modern Retrieval-Augmented Generation (RAG) systems often employ multi-turn interaction pipelines to inte
arXiv:2604.12513v1 Announce Type: new Abstract: We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence. Our mo
Databricks' AI Gateway provides a centralized governance and control layer designed to manage agentic AI systems, enabling organizations to enforce policies, monitor usage, and maintain oversight acro
Databricks AI Gateway provides a secure mechanism for connecting AI agents to external Model Context Protocol (MCP) servers, enabling agents to access external tools and data sources without exposing
Today, we’re announcing the general availability of Claude Opus 4.7 on Vertex AI. What’s new: Anthropic’s newest Opus model delivers advanced performance across coding, long-running agents, and profes
arXiv:2601.21297v2 Announce Type: replace Abstract: We introduce Deep QP Safety Filter, a fully data-driven safety layer for black-box dynamical systems. Our method learns a Quadratic-Program (QP) saf
arXiv:2603.29148v2 Announce Type: replace-cross Abstract: Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for larg
arXiv:2604.12776v1 Announce Type: new Abstract: Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence. In particul
arXiv:2604.11929v1 Announce Type: new Abstract: Our ability to predict, control, and ultimately understand complex systems rests on discovering the equations that govern their dynamics. Identifying th
arXiv:2604.12630v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have exhibited remarkable performance in various visual tasks, yet still struggle with spatial reasoning. Rec
'Hey Grok' is a voice command feature that allows Tesla users to ask questions or set location-based reminders by saying the wake phrase. The feature can be enabled through the vehicle's Controls menu
arXiv:2604.12665v1 Announce Type: new Abstract: Motion reasoning serves as the cornerstone of multi-object tracking (MOT), as it enables consistent association of targets across frames. However, exist
I'm going all in on Hermes (@NousResearch, @Teknium1) as my entire agent and coding stack. Six profiles. One shared self-hosted memory store. Zero hosted-coder dependencies. The fleet: - pmax-mousa —
arXiv:2604.12202v1 Announce Type: new Abstract: This study leverages large-scale travel surveys for over 200,000 residents across Boston, Chicago, Hong Kong, London, and Sao Paulo. With rich individua
Load balancing and scaling LLM serving involves distributing inference requests across multiple model instances or GPUs to prevent bottlenecks and ensure consistent response times under varying traffi
arXiv:2604.13036v1 Announce Type: new Abstract: Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, t
arXiv:2604.05164v2 Announce Type: replace-cross Abstract: As LLM reasoning performance plateau, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces
arXiv:2604.12526v1 Announce Type: cross Abstract: Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on e
arXiv:2604.12271v1 Announce Type: new Abstract: Multimodal attributed graphs (MAGs) combine multimodal node attributes with structured relations. However, existing methods usually perform shared messa
Vultr Global Load Balancers is a managed service that distributes incoming traffic across multiple regions and backend servers worldwide, helping businesses improve application availability, reduce la
arXiv:2604.12102v1 Announce Type: new Abstract: We introduce compute-grounded reasoning (CGR), a design paradigm for spatial-aware research agents in which every answerable sub-problem is resolved by
arXiv:2602.22394v2 Announce Type: replace Abstract: Vision Transformers (ViTs), when pre-trained on large-scale data, provide general-purpose representations for diverse downstream tasks. However, art
arXiv:2604.05818v2 Announce Type: replace-cross Abstract: Multi-modal Retrieval-Augmented Generation (RAG) has emerged as a highly effective paradigm for Knowledge-Based Visual Question Answering (KB-
arXiv:2604.12140v1 Announce Type: new Abstract: We present XANE(3), a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly f
arXiv:2604.11048v1 Announce Type: cross Abstract: Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capa
arXiv:2604.10210v1 Announce Type: new Abstract: Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyrami
arXiv:2511.18082v3 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remain
arXiv:2604.10547v1 Announce Type: new Abstract: We introduce Agent^2 RL-Bench, a benchmark for evaluating agentic RL post-training -- whether LLM agents can autonomously design, implement, and run com
arXiv:2604.11135v1 Announce Type: cross Abstract: Pretrained video generation models provide strong priors for robot control, but existing unified world action models still struggle to decode reliable
arXiv:2603.18492v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires stori
arXiv:2604.10702v1 Announce Type: cross Abstract: Multi-parametric prostate MRI -- combining T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted sequences -- is central to
arXiv:2604.11202v1 Announce Type: cross Abstract: We present CapBench, a fully reproducible, multi-PDK dataset for capacitance extraction. The dataset is derived from open-source designs, including si
arXiv:2604.10305v1 Announce Type: cross Abstract: Cooperative perception allows connected vehicles and roadside infrastructure to share sensor observations, creating a fused scene representation beyon
arXiv:2604.09746v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environmen
arXiv:2601.07224v2 Announce Type: replace Abstract: While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effectiv
arXiv:2604.11389v1 Announce Type: new Abstract: Reliable recognition of standard cine cardiac MRI views is essential because each view determines which cardiac anatomy is visualized and which quantita
arXiv:2601.01822v2 Announce Type: replace-cross Abstract: Since floorplan data is readily available, long-term persistent, and robust to changes in visual appearance, visual Floorplan Localization (FL
arXiv:2604.11680v1 Announce Type: new Abstract: Optical microrobots actuated by optical tweezers (OT) are important for cell manipulation and microscale assembly, but their autonomous operation depend
arXiv:2510.10925v2 Announce Type: replace-cross Abstract: Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. Ho
SenseNova's NEO-unify is an encoder-free unified multimodal model built on a Mixture-of-Transformers (MoT) backbone that eliminates the need for traditional VAE encoders in image generation. The 2B NE
arXiv:2604.10332v1 Announce Type: new Abstract: We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative
Understanding the relationships within your data is crucial for uncovering hidden insights and building intelligent applications. However, managing operational (OLTP) and analytical (OLAP) graph workl
arXiv:2604.10470v1 Announce Type: cross Abstract: Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-qu
arXiv:2604.11104v1 Announce Type: new Abstract: This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through