Knowledge catalogue
arXiv:2604.26181v1 Announce Type: new Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input comp
arXiv:2604.26726v1 Announce Type: new Abstract: Languages of the world vary concerning the order of subject, object and verb. The most frequent dominant orders are SOV and SVO, and researchers have ta
arXiv:2604.26102v1 Announce Type: cross Abstract: Large language model agents have achieved remarkable progress on software engineering tasks, yet current approaches suffer from a fundamental context
arXiv:2604.26633v1 Announce Type: cross Abstract: The bottleneck in learning-based industrial defect detection is often limited not by model capacity, but by the scarcity of labeled defect data: defec
arXiv:2505.22910v2 Announce Type: replace Abstract: When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event o
arXiv:2604.26772v1 Announce Type: new Abstract: Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effectively detect AI-generated images (AIGIs) from uns
arXiv:2604.26511v1 Announce Type: cross Abstract: Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences o
arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipl
arXiv:2510.04214v3 Announce Type: replace Abstract: We deploy large language models (LLMs) as business development (BD) agents for persuasive price negotiation in online travel agencies (OTAs). The ag
arXiv:2511.20032v3 Announce Type: replace Abstract: Visual attention serves as the primary mechanism through which MLLMs interpret visual information; however, its limited localization capability ofte
arXiv:2604.26167v1 Announce Type: cross Abstract: Recent work has shown that a model's input word embeddings can serve as effective control variables for steering its behavior toward outputs that sati
arXiv:2604.26361v1 Announce Type: cross Abstract: Globalization of graphic designs such as those used in marketing materials and magazines is increasingly important for communication to broad audience
arXiv:2604.26514v1 Announce Type: cross Abstract: This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facili
arXiv:2603.02259v2 Announce Type: replace-cross Abstract: Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain esse
arXiv:2604.26651v1 Announce Type: cross Abstract: With the increasing availability of online information, recommender systems have become an important tool for many web-based systems. Due to the conti
arXiv:2512.20340v3 Announce Type: replace Abstract: Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing metho
arXiv:2602.03467v2 Announce Type: replace Abstract: Explanations are central to human cognition, yet AI systems often produce outputs that are difficult to understand. While symbolic AI offers a trans
arXiv:2604.26347v1 Announce Type: cross Abstract: Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion requiring em
arXiv:2508.16131v2 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context. It can boost developer productivity while providing
arXiv:2602.06603v3 Announce Type: replace Abstract: Reinforcement learning (RL) is a type of artificial intelligence for making optimal choices. In healthcare, researchers generally use offline RL (OR
arXiv:2509.11295v2 Announce Type: replace Abstract: Developing effective prompts demands significant cognitive investment to generate reliable, high-quality responses from Large Language Models (LLMs)
arXiv:2507.12549v4 Announce Type: replace Abstract: While machine learning has advanced through massive parallelization, we identify a critical blind spot: some problems are fundamentally sequential.
arXiv:2604.26317v1 Announce Type: new Abstract: The vulnerabilities of deep neural networks against singularities have raised serious concerns regarding their deployment in the physical world. One of
arXiv:2604.26460v1 Announce Type: new Abstract: Stylistic personalization - making LLMs write in a specific individual's style, rather than merely adapting to task preferences - lacks evaluation groun
arXiv:2602.11731v2 Announce Type: replace Abstract: Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision para
arXiv:2604.26946v1 Announce Type: new Abstract: Breakthrough progress in vision-based navigation through unknown environments has been achieved by using multimodal large language models (MLLMs). These
arXiv:2505.13963v3 Announce Type: replace Abstract: Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's
arXiv:2603.08182v2 Announce Type: replace-cross Abstract: Large language models often underperform in many European languages due to the dominance of English and a few high-resource languages in train
arXiv:2505.24867v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have made impressive strides in understanding spatio-temporal relationships in videos. Howeve
arXiv:2409.01115v5 Announce Type: replace Abstract: This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches
arXiv:2604.26247v1 Announce Type: cross Abstract: Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user inte
arXiv:2503.04872v3 Announce Type: replace-cross Abstract: The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. Howev
arXiv:2604.26553v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language,
arXiv:2510.14703v2 Announce Type: replace Abstract: Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an in
arXiv:2604.26370v1 Announce Type: new Abstract: Vision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language lea
arXiv:2509.23980v2 Announce Type: replace Abstract: Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to V
arXiv:2604.26020v1 Announce Type: cross Abstract: Usability testing with experts and potential users can assess the effectiveness, efficiency, and user satisfaction of graphical user interfaces (GUIs)
arXiv:2601.03423v3 Announce Type: replace-cross Abstract: Adapting language models to the clinical domain through continued pretraining and instruction tuning requires costly retraining for each new m
arXiv:2511.22972v3 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but suffer from high inference latency due to their autoregressive gene
arXiv:2604.26597v1 Announce Type: cross Abstract: Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis c
arXiv:2604.07692v2 Announce Type: replace Abstract: Large Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Curr
arXiv:2604.26501v1 Announce Type: cross Abstract: Generating sports game reports from structured tables is a complex table-to-text task that demands both precise data interpretation and fluent narrati
arXiv:2604.26951v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters f
arXiv:2412.10679v3 Announce Type: replace Abstract: Blood pressure (BP) measurement is crucial for daily health assessment. Remote photoplethysmography (rPPG), which extracts pulse waves from face vid
arXiv:2506.16742v3 Announce Type: replace Abstract: To be adopted in safety-critical domains like medical image analysis, AI systems must provide human-interpretable decisions. Variational Information
arXiv:2604.26873v1 Announce Type: new Abstract: We propose UAPAR, an Uncertainty-Aware Pedestrian Attribute Recognition framework. To the best of our knowledge, this is the first EDL-based uncertainty
arXiv:2604.26836v1 Announce Type: new Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploratio
arXiv:2604.26360v1 Announce Type: cross Abstract: Reinforcement learning (RL) systems typically optimize scalar reward functions that assume precise and reliable evaluation of outcomes. However, real-
arXiv:2604.26489v1 Announce Type: new Abstract: DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one
arXiv:2604.26694v1 Announce Type: cross Abstract: We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruc
arXiv:2604.26411v1 Announce Type: new Abstract: Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with indepe
arXiv:2604.26837v1 Announce Type: new Abstract: Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing onl
arXiv:2604.26216v1 Announce Type: new Abstract: This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised dis
arXiv:2512.03992v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are essential for embodied AI and safety-critical applications, such as robotics and autonomous systems. However
arXiv:2601.00545v3 Announce Type: replace Abstract: Many problems in robotics involve both continuous and discrete components, and modeling them together for estimation tasks has been a long standing
arXiv:2602.03412v2 Announce Type: replace Abstract: As large language model agents tackle increasingly complex long-horizon tasks, effective post-training becomes critical. Prior work faces fundamenta
arXiv:2508.07852v2 Announce Type: replace-cross Abstract: Recent research on learnable neural representations has been widely adopted in the field of 3D scene reconstruction and neural rendering appli
arXiv:2509.09870v2 Announce Type: replace-cross Abstract: Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such des
arXiv:2604.26218v1 Announce Type: new Abstract: Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward vis
arXiv:2604.26806v1 Announce Type: cross Abstract: Transformer-based architectures have established a dominant paradigm in global semantic perception; however, they remain fundamentally constrained by