Knowledge catalogue
arXiv:2602.00122v2 Announce Type: replace Abstract: In recent years, image editing models have made significant progress, enabling users to manipulate visual content in a flexible and interactive mann
arXiv:2605.22570v1 Announce Type: new Abstract: Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisel
arXiv:2605.22650v1 Announce Type: new Abstract: As artificial intelligence (AI) systems become more common in our daily lives, it is important to understand how different stakeholders comprehend and e
arXiv:2605.21801v1 Announce Type: cross Abstract: Post-training has become central to improving reasoning and alignment in large language models, where critic-free models enable scalable learning from
arXiv:2503.02885v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have gained traction in educational settings, often framed as virtual tutors or teaching assistants. Following ea
arXiv:2605.21058v1 Announce Type: new Abstract: Causal representation learning (CRL) and traditional representation learning have largely developed along different trajectories. Traditional representa
arXiv:2605.21313v1 Announce Type: new Abstract: Deep neural networks have achieved impressive performance on a variety of tasks, but their brittleness to distributional shifts remains a significant ba
arXiv:2605.21167v1 Announce Type: cross Abstract: An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existi
arXiv:2410.03296v4 Announce Type: replace Abstract: Instruction-tuned LLMs are able to provide extit{an} explanation about their output to users by generating self-explanations, without requiring the
arXiv:2605.21114v1 Announce Type: new Abstract: Post-hoc explainable AI (XAI) methods typically produce deterministic attribution maps, whereas Bayesian neural networks (BNNs) induce a distribution ov
arXiv:2605.20533v1 Announce Type: new Abstract: Optimization algorithms are core methods by which machine learning models iteratively minimize loss functions, update parameters, learn from data, and i
arXiv:2602.09723v2 Announce Type: replace Abstract: The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification, where agents explore search spaces in 'guess an
arXiv:2605.21481v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs, placing increasi
arXiv:2605.20649v1 Announce Type: cross Abstract: Wi-Fi-based human activity recognition (HAR) has emerged as a promising approach for contactless sensing, leveraging channel state information (CSI) c
arXiv:2605.20967v1 Announce Type: new Abstract: Memes have become a prominent medium of political communication in the Arab world, reflecting how humor, imagery, and text interact to express ideologic
arXiv:2605.20198v1 Announce Type: cross Abstract: Augmented analytics has transformed how business intelligence (BI) systems support managerial decision-making. This is especially true for users witho
arXiv:2605.21301v1 Announce Type: cross Abstract: In biomedical Subgroup Discovery, practitioners are interested in discovering interpretable and homogeneous subgroups within a group of patients. In t
arXiv:2605.21111v1 Announce Type: new Abstract: Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections
arXiv:2503.22693v2 Announce Type: replace-cross Abstract: The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly
arXiv:2603.08155v3 Announce Type: replace Abstract: Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance
arXiv:2602.06500v2 Announce Type: replace Abstract: Scaling inference methods such as Markov chain Monte Carlo to high-dimensional models remains a central challenge in Bayesian deep learning. A promi
arXiv:2512.09806v2 Announce Type: replace Abstract: Deep learning-based methods have recently achieved significant success in image reconstruction problems. However, challenges have emerged, as these
arXiv:2605.20975v1 Announce Type: new Abstract: Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the pr
arXiv:2602.02304v2 Announce Type: replace-cross Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning w
arXiv:2605.20369v1 Announce Type: new Abstract: Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem-solving and code generation. The widely ado
arXiv:2411.01141v2 Announce Type: replace Abstract: There are two shortages in the current Large Language Models (LLMs) era. The first is short of multilingual models, where most LLMs are English-cent
arXiv:2605.20766v1 Announce Type: new Abstract: Point supervision has become a scalable solution to address dense annotation for infrared small target detection, but its performance is limited by two
arXiv:2605.20730v1 Announce Type: new Abstract: In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks through demonstrations, yet it suffers from escalating inference cos
arXiv:2605.20955v1 Announce Type: new Abstract: Text-to-motion generation, which translates textual descriptions into human motions, faces the challenge that users often struggle to precisely convey t
arXiv:2605.21273v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning,
arXiv:2406.14978v2 Announce Type: replace Abstract: Event cameras, known for their high dynamic range, absence of motion blur, and low energy usage, have recently found a wide range of applications th
arXiv:2605.21150v1 Announce Type: new Abstract: LiDAR Inertial Odometry (LIO) is a critical component for many mobile robots that need to navigate without relying on external positioning (e.g., GPS).
arXiv:2605.20912v1 Announce Type: new Abstract: The increasing volume of scientific research necessitates effective communication across language barriers. Machine translation (MT) offers a promising
arXiv:2509.03526v2 Announce Type: replace Abstract: The recent advancements of Large Language Models (LLMs) have spurred considerable research interest in extending their linguistic capabilities beyon
arXiv:2509.17396v4 Announce Type: replace Abstract: Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long convers
arXiv:2605.20920v1 Announce Type: new Abstract: Recent advances in machine learning and the availability of articulatory datasets allow vocal tract synthesis to be conditioned on phonetic sequences, a
arXiv:2605.20726v1 Announce Type: cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting te
arXiv:2605.20561v1 Announce Type: new Abstract: Isoperimetric robotic trusses can adapt to different tasks and environments because they have a high strength-to-weight ratio, can change their own shap
arXiv:2605.21264v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed learning. However, existing FL methods face a fundamental
arXiv:2605.20787v1 Announce Type: new Abstract: The rapid advancements in generative AI technologies, such as Stable Diffusion, DALL-E, and Midjourney, have significantly transformed the creation of s
arXiv:2605.20761v1 Announce Type: new Abstract: The rapid proliferation of AI-generated text has introduced significant challenges in maintaining the integrity of digital content. Advanced generative
arXiv:2605.20950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference. Existin
Gated DeltaNet has been one of my favorite 'hybrid attention' newcomers in the good old transformer stack. Excited to see Gated DeltaNet-2. Adding it to my reading stack. In the meantime, I have a pri
arXiv:2605.20248v1 Announce Type: new Abstract: In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi-supervised node classificat
arXiv:2605.20669v1 Announce Type: new Abstract: X-ray security inspection requires accurate real-time detection of prohibited items, but existing models often struggle to balance the challenges of sev
arXiv:2605.20600v1 Announce Type: new Abstract: Autoregressive (AR) visual generation has achieved remarkable performance but suffers from high memory usage and low throughput, as it requires caching
arXiv:2605.21460v1 Announce Type: new Abstract: Autonomous manipulation systems have achieved remarkable capabilities, yet the integration of human expertise with diffusion-based policies in shared co
arXiv:2603.26539v2 Announce Type: replace Abstract: How does the extent to which a model is open or closed impact the scientific inferences that can be drawn from research that involves it? In this pa
arXiv:2605.21309v1 Announce Type: new Abstract: Cooperative perception enabled by Vehicle-to-Everything (V2X) communication enhances autonomous driving safety by creating a unified environmental repre
arXiv:2605.21107v1 Announce Type: new Abstract: We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before obs
arXiv:2605.20595v1 Announce Type: new Abstract: Dense low-altitude aerial operations require more than pre-flight route coordination and last-resort collision avoidance. Once aircraft are airborne, di
arXiv:2605.21475v1 Announce Type: new Abstract: Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational
arXiv:2605.20547v1 Announce Type: new Abstract: Many recent flow-matching and diffusion-style generative models rely on auxiliary stochastic dynamics during training: a richer process is simulated to
arXiv:2605.20372v1 Announce Type: new Abstract: Multimodal semantic segmentation benefits remote sensing analysis by combining complementary information from different sensor modalities. In real-world
arXiv:2605.21160v1 Announce Type: new Abstract: The discovery of first integrals is of fundamental scientific importance for understanding conservation laws in dynamical systems. However, existing sym
arXiv:2605.21330v1 Announce Type: new Abstract: In-hand object manipulation is a fundamental yet challenging capability for dexterous robots. Despite significant progress in dexterous manipulation, ex
arXiv:2605.21465v1 Announce Type: new Abstract: In model-driven engineering, metamodel evolution leads to the need to adapt corresponding grammars to maintain consistency, which typically requires ted
arXiv:2605.21123v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is successful for alignment in LLMs but still faces challenges in text-to-image generation. Existing studies are co
arXiv:2605.21086v1 Announce Type: new Abstract: While Large Language Models (LLMs) are increasingly integrated into in-vehicle conversational systems, identifying the optimal model remains challenging
arXiv:2605.21446v1 Announce Type: new Abstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world