Knowledge catalogue
arXiv:2605.12969v1 Announce Type: cross Abstract: RLVR has become a widely adopted paradigm for improving LLMs' reasoning capabilities, and GRPO is one of its most representative algorithms. In this p
arXiv:2605.13501v1 Announce Type: cross Abstract: LLM-based generation of SystemVerilog Assertions (SVA) is often reported as nearing saturation, with the strongest specialized model reaching {sim}76%
arXiv:2605.13129v1 Announce Type: cross Abstract: Recent 3D generative models can synthesize high-quality assets, but their outputs are typically static: they lack the skeletal rigs, joint hierarchies
arXiv:2605.12895v1 Announce Type: cross Abstract: Aggregate accuracy metrics dominate the evaluation of clinical AI decision-support systems but do not detect deployment-phase failures of input reliab
arXiv:2603.20527v3 Announce Type: replace Abstract: Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of t
arXiv:2605.13775v1 Announce Type: cross Abstract: The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision-language
arXiv:2605.13665v1 Announce Type: new Abstract: Quadruped robots demonstrate exceptional potential for navigating complex terrain in critical applications such as search and rescue missions and infras
arXiv:2507.00990v3 Announce Type: replace-cross Abstract: This work introduces Robots Imitating Generated Videos (RIGVid), a system that enables robots to perform complex manipulation tasks--such as p
arXiv:2605.13730v1 Announce Type: cross Abstract: Transthoracic echocardiography (TTE) is the first-line imaging modality for diagnosing bicuspid aortic valve (BAV), yet diagnostic performance varies
arXiv:2506.00982v3 Announce Type: replace Abstract: Deep multi-agent reinforcement learning (MARL) has been demonstrated effectively in simulations for multi-robot problems. For autonomous vehicles, t
arXiv:2605.12899v1 Announce Type: cross Abstract: Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on cor
arXiv:2605.13093v1 Announce Type: new Abstract: Generalizable 3D Gaussian Splatting has recently emerged as an efficient approach for novel-view synthesis, enabling feed-forward synthesis from only a
arXiv:2605.13403v1 Announce Type: cross Abstract: Latent Action Models (LAMs) have emerged as an effective paradigm for handling heterogeneous datasets during Vision-Language-Action (VLA) model pretra
arXiv:2605.13391v1 Announce Type: new Abstract: The rise of multi-modal large language models (MLLMs) is shifting remote sensing (RS) intelligence from 'see' to 'action', as OpenClaw-style frameworks
arXiv:2605.13695v1 Announce Type: cross Abstract: LLM-as-a-judge is now the default measurement instrument for open-ended generation, but on the public JudgeBench benchmark even strong instruction-tun
arXiv:2605.12651v1 Announce Type: new Abstract: Runtime monitoring of autonomous systems traditionally relies on mapping continuous sensor observations to discrete logical propositions defined over lo
arXiv:2605.13302v1 Announce Type: new Abstract: This paper extends safety guarantees for multi-task Bayesian optimization with uncertain correlation matrices from intrinsic co-reginalization models to
arXiv:2509.23800v3 Announce Type: replace-cross Abstract: Modern generative AI models, such as diffusion and flow matching models, can sample from rich data distributions. However, many applications,
arXiv:2605.13681v1 Announce Type: new Abstract: Flow Language Models (FLMs) are a recently introduced class of language models which adapt continuous flow matching for one-hot encoded token sequences.
arXiv:2310.07844v2 Announce Type: replace Abstract: We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localization And Mapping (SLAM) algorithms against
arXiv:2605.12506v1 Announce Type: cross Abstract: Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when consid
arXiv:2604.01690v2 Announce Type: replace Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a
arXiv:2605.13684v1 Announce Type: new Abstract: We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensi
arXiv:2605.13075v1 Announce Type: cross Abstract: Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of
arXiv:2605.12715v1 Announce Type: new Abstract: As language models scale, the amount of data they require grows -- yet many target data sources, such as low-resource languages or specialized domains,
arXiv:2510.18245v3 Announce Type: replace-cross Abstract: Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM)
arXiv:2605.13534v1 Announce Type: new Abstract: Deep search agents have proven effective in enhancing LLMs by retrieving external knowledge during multi-step reasoning. However, existing methods often
arXiv:2605.13667v1 Announce Type: new Abstract: Scene graph generation provides a compact structured representation for visual perception, but accurate and fast graph prediction from images and videos
arXiv:2605.13725v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent simulation offers a powerful testbed for studying social opinion dynamics. Yet current approaches often ado
arXiv:2605.12662v1 Announce Type: new Abstract: High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these datasets deter
arXiv:2512.07091v2 Announce Type: replace Abstract: Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human in
arXiv:2605.13117v1 Announce Type: cross Abstract: Achieving reliable robotic manipulation, such as dexterous grasping, requires a synergy between physically stable interactions and semantic task guida
arXiv:2605.12953v1 Announce Type: cross Abstract: Language-guided segmentation transcends the scope limitations of traditional semantic segmentation, enabling models to segment arbitrary target region
arXiv:2605.11505v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards helps reasoning, but GRPO-style methods stall on hard prompts where all sampled rollouts fail. SORT a
arXiv:2509.21543v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong promise for robotic task planning, particularly through the automatic generation of symbolic planning
arXiv:2605.13554v1 Announce Type: cross Abstract: Contrastive reinforcement learning (CRL) learns goal-conditioned Q-values through a contrastive objective over state-action and goal representations,
arXiv:2510.12837v3 Announce Type: replace-cross Abstract: Cultural evolution allows ideas and technologies to accumulate across generations, reaching their most complex and open-ended form in humans.
arXiv:2605.13737v1 Announce Type: new Abstract: When an omnimodal large language model accepts a question whose textual premise contradicts what it actually sees or hears, does the failure lie in perc
arXiv:2605.12945v1 Announce Type: new Abstract: Shortcut features are often invoked to explain out-of-distribution (OOD) failure, but training correlation, learned shortcut use, and test-time failure
arXiv:2511.00066v4 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a practical route to improve large language model reasoning, and Group Relative Pol
arXiv:2605.12916v1 Announce Type: cross Abstract: Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing sp
arXiv:2605.13340v1 Announce Type: new Abstract: Shortcut mitigation strategies commonly rely on training data annotations, group-balanced held-out data or the presence of all groups, i.e., all combina
arXiv:2605.13428v1 Announce Type: new Abstract: Generalizing robotic manipulation across object poses, viewpoints, and dynamic disturbances is difficult, especially with only a few demonstrations. End
arXiv:2605.12748v1 Announce Type: cross Abstract: Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI
arXiv:2510.11303v2 Announce Type: replace Abstract: Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semant
arXiv:2605.13223v1 Announce Type: new Abstract: Text-to-image (T2I) generation has advanced rapidly, making reliable evaluation critical as performance differences between models narrow. Existing eval
arXiv:2508.09479v2 Announce Type: replace Abstract: Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS)
arXiv:2605.12872v1 Announce Type: new Abstract: Despite the recent success of Multimodal Foundation Models (FMs), their reliance on massive paired datasets limits their applicability in low-data and r
arXiv:2312.04110v2 Announce Type: replace-cross Abstract: The COVID-19 pandemic has exerted a profound impact on the global economy and continues to exact a significant toll on human lives. The COVID-
arXiv:2601.13359v2 Announce Type: replace-cross Abstract: Prefill attacks are an effective and low-cost jailbreaking method, as they directly insert an acceptance sequence (e.g., 'Sure, here is how to
arXiv:2605.12792v1 Announce Type: new Abstract: There is recently a serious issue that Deep Neural Networks (DNNs) training uses more and more unauthorized data. A clean-label generalization attack, o
arXiv:2605.12513v1 Announce Type: cross Abstract: Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics. We propose SP-GC
arXiv:2605.13600v1 Announce Type: new Abstract: 3D Language Gaussian Splatting (3DLGS) augments 3D Gaussian Splatting with language-aligned visual features for open-vocabulary 3D scene understanding.
arXiv:2602.07458v4 Announce Type: replace Abstract: Online Reinforcement Learning (RL) offers a promising avenue for complex image editing but is currently constrained by the scarcity of reliable and
arXiv:2605.13566v1 Announce Type: new Abstract: Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LS
arXiv:2605.12709v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) model continuous signals using multilayer perceptrons (MLPs), enabling compact, differentiable, and high-fidelity
arXiv:2605.13079v1 Announce Type: cross Abstract: Muon orthogonalizes the momentum buffer before each update, replacing its singular values with ones via Newton-Schulz iterations. This simple change l
arXiv:2511.16084v2 Announce Type: replace-cross Abstract: Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practica
arXiv:2605.12992v1 Announce Type: cross Abstract: Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single
arXiv:2602.00616v3 Announce Type: replace Abstract: Text-to-Image (T2I) diffusion models enable high quality open ended synthesis, but practical use requires suppressing unsafe generations while prese