Knowledge catalogue
arXiv:2605.08231v1 Announce Type: cross Abstract: Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy
arXiv:2605.08379v1 Announce Type: cross Abstract: This paper proposes a time-warping transfer learning method, a technique for temporally rescaling the learned dynamics of a recurrent neural network (
arXiv:2605.08475v1 Announce Type: cross Abstract: Mechanistic accounts of in-context learning (ICL) have identified iterative algorithms for linear regression and related linear prediction tasks, ofte
arXiv:2605.08633v1 Announce Type: cross Abstract: Earth observation is becoming one of the largest data-producing activities in science, yet current pipelines still treat compression as a storage and
arXiv:2605.08278v1 Announce Type: cross Abstract: GNNs have become a standard tool for learning on relational data, yet they remain highly vulnerable to backdoor attacks. Prior defenses often depend o
Trump had one of his worst mental health episodes yet last night, posting over 55 times in 3 hours. Here is the list: 10:15 PM - Accuses Obama of attempting a coup in 2016 10:15 PM - Says Obama worked
arXiv:2412.13547v3 Announce Type: replace Abstract: Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches,
arXiv:2605.08376v1 Announce Type: new Abstract: Underwater image enhancement (UIE) is a practically important yet underexplored application of spiking neural networks (SNNs), where the dominant degrad
arXiv:2601.08321v3 Announce Type: replace Abstract: With the rapid advancement of image generation, visual text editing using natural language instructions has received increasing attention. The main
arXiv:2605.09276v1 Announce Type: cross Abstract: Spiking transformers have shown strong potential for neuromorphic vision, yet their token processing across multiple spiking steps still introduces su
arXiv:2505.22152v2 Announce Type: replace Abstract: While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings. We addres
arXiv:2605.09600v1 Announce Type: cross Abstract: Accurate skin lesion segmentation is vital for dermoscopic Computer-Aided Diagnosis. However, visual ambiguity and morphological irregularity often de
arXiv:2605.08575v1 Announce Type: cross Abstract: Mixture of Experts (MoE) architecture has become the standard for state-of-the-art large language models, owing to its computational efficiency throug
arXiv:2605.09857v1 Announce Type: cross Abstract: Multicalibration requires predicted scores to agree with label probabilities across rich families of subgroups and score-dependent tests, but existing
arXiv:2405.17642v3 Announce Type: replace-cross Abstract: The growing complexity of AI systems has intensified the need for transparency through Explainable AI (XAI). Counterfactual explanations (CFs)
arXiv:2605.09396v1 Announce Type: cross Abstract: This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate nois
arXiv:2605.10125v1 Announce Type: new Abstract: Artificial intelligence (AI) tools are being incorporated into scientific research workflows with the potential to enhance efficiency in tasks such as d
arXiv:2605.09040v1 Announce Type: new Abstract: Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-spec
arXiv:2509.08150v5 Announce Type: replace Abstract: Reasoning is a fundamentally algorithmic task. Yet current work on LLM-based reasoning relies on free-form generation whose theoretical guarantees (
arXiv:2407.06576v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diver
arXiv:2507.04465v4 Announce Type: replace Abstract: The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research communi
arXiv:2510.10606v4 Announce Type: replace Abstract: Post-training Large Vision-and-Language Models (LVLMs) typically involves Supervised Fine-Tuning (SFT) for knowledge injection or Reinforcement Lear
arXiv:2605.10229v1 Announce Type: new Abstract: Privacy protection has become a critical requirement in the era of ubiquitous visual data sharing, imposing higher demands on efficient and robust priva
arXiv:2605.10696v1 Announce Type: new Abstract: Discrete-time joint acceleration constraints are widely used to enforce position and velocity limits. However, under voltage-constrained electric actuat
arXiv:2501.05614v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are widely deployed in industry, making their intellectual property valuable. However, protecting GNNs from unaut
arXiv:2605.09519v1 Announce Type: new Abstract: We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile met
arXiv:2605.10180v1 Announce Type: new Abstract: The rise of text-to-image (T2I) models has increasingly raised concerns regarding the generation of risky content, such as sexual, violent, and copyrigh
arXiv:2603.04333v2 Announce Type: replace-cross Abstract: Recent work shows that flow matching can be effective for scalar Q-value function estimation in reinforcement learning (RL), but it remains un
arXiv:2605.06033v2 Announce Type: replace-cross Abstract: The extent to which Artificial Intelligence (AI) can trigger generalized paradigm shifts in science is unclear. Although some of these technol
arXiv:2605.08933v1 Announce Type: new Abstract: Muon orthogonalizes matrix updates, but multi-head attention naturally operates at the level of heads. This granularity mismatch raises the question of
arXiv:2605.08710v1 Announce Type: new Abstract: Human-AI teams fail to outperform their best member in 70% of studies, yet no theory specifies when complementarity is achievable. We derive tight bound
arXiv:2605.08234v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by the memory and bandwidth cost of reading large KV caches during decoding. KV compression reduces this co
arXiv:2602.02821v2 Announce Type: replace Abstract: Much recent work has argued that the variation in the languages of the world can be explained from the perspective of efficient communication; in pa
arXiv:2605.09460v1 Announce Type: cross Abstract: Identity-preserved image generation is typically built on many-step diffusion backbones, making personalized generation expensive at deployment time.
arXiv:2602.09789v3 Announce Type: replace Abstract: Scaling up model parameters has long been a prevalent training paradigm driven by the assumption that larger models yield superior generation capabi
arXiv:2605.09030v1 Announce Type: new Abstract: Raw cosine in the 768-dimensional output space of the Contrastive Style Descriptor (CSD) is now widely read as an absolute, calibrated style-fidelity sc
arXiv:2512.08875v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice,
arXiv:2605.10605v1 Announce Type: new Abstract: Expressions with an aspectual variant of a light verb, e.g. 'take on debt' vs. 'have debt', are frequent in texts but often difficult to classify betwee
arXiv:2507.03167v3 Announce Type: replace-cross Abstract: Chat models without chain-of-thought (CoT) reasoning must decide whether to refuse a harmful request before generating their first response to
arXiv:2605.08200v1 Announce Type: new Abstract: A pervasive intuition holds that vision-language models (VLMs) are most trustworthy when their attention maps look sharp: concentrated attention on the
arXiv:2502.10760v2 Announce Type: replace Abstract: Frontier models can be prompted or conditioned to do many tasks, but finding good prompts is not always easy, nor is understanding some performant p
arXiv:2605.10714v1 Announce Type: cross Abstract: Cross-lingual transfer has become a central paradigm for extending natural language processing (NLP) technologies to low-resource languages. By levera
arXiv:2605.09167v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) performs well for high-resource languages with abundant paired audio-transcript data, but its accuracy degrades sha
arXiv:2605.10704v1 Announce Type: cross Abstract: This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN) Near Real-Time Radio In
arXiv:2605.08842v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models organize knowledge into explicitly routed expert modules, making expert-level representations traceable and ana
arXiv:2605.09639v1 Announce Type: cross Abstract: While U-Net architectures remain the gold standard for medical image segmentation, their deployment in resource-constrained environments demands aggre
arXiv:2605.09981v1 Announce Type: cross Abstract: Multimodal models that jointly reason over protein sequences, structures, and function annotations within a unified representation hold immense potent
arXiv:2605.06231v2 Announce Type: replace Abstract: This paper presents our system for SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization, which identifies p
arXiv:2605.08560v1 Announce Type: cross Abstract: We present ZAYA1-VL-8B, a compact mixture-of-experts vision-language model built upon our in-house language model, ZAYA1-8B. Despite its compact size,
arXiv:2603.16025v2 Announce Type: replace-cross Abstract: The exchange interaction is a foundational building block for the operation of spin-based quantum processors. Extracting the exchange interact
arXiv:2605.05876v2 Announce Type: replace-cross Abstract: We present 3D Surface Splatting (3DSS), the first differentiable surface splatting renderer for physically-based inverse rendering from multi-
arXiv:2605.08059v1 Announce Type: new Abstract: In this paper, we propose a modular framework for 6D pose estimation based on keypoint heatmap regression. Our approach combines YOLOv10m for object det
arXiv:2605.07052v1 Announce Type: cross Abstract: We generalize Jan Willems' behavioral approach to a class of discrete-time nonlinear systems in a vector-valued reproducing kernel Hilbert space (RKHS
arXiv:2605.07811v1 Announce Type: new Abstract: This paper presents a comparative study of classical machine learning and deep learning methods for sentiment classification on the IMDb movie reviews d
arXiv:2509.12047v2 Announce Type: replace-cross Abstract: Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. Howev
arXiv:2602.17472v2 Announce Type: replace Abstract: Recent advances in automated vehicles have focused on improving perception performance under adverse weather conditions; however, research on physic
arXiv:2605.07424v1 Announce Type: new Abstract: Modern online mass spectrometry generates multi-terabyte data streams critical for understanding Earth's environmental systems. However, extracting acti
arXiv:2603.22586v2 Announce Type: replace Abstract: In-context learning (ICL) enables task adaptation at inference time by conditioning on demonstrations rather than updating model parameters. Althoug
arXiv:2410.18103v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are becoming increasingly popular for EEG-based depression detection. However, previous GNN-based methods fail to
arXiv:2605.06762v1 Announce Type: cross Abstract: Robust genotype-to-phenotype (G2P) prediction is essential for accelerating breeding decisions and genetic gain. However, it remains challenging to me