Knowledge catalogue
arXiv:2601.01311v2 Announce Type: replace-cross Abstract: Distributionally Robust (DR) optimization aims to certify worst-case risk within a Wasserstein uncertainty set. Current certifications typical
arXiv:2604.06198v1 Announce Type: cross Abstract: The rapid rise of generative artificial intelligence (AI) is driving unprecedented growth in global computational demand, placing increasing pressure
arXiv:2604.07019v1 Announce Type: cross Abstract: Neural networks deliver impressive predictive performance across a variety of tasks, but they are often opaque in their decision-making processes. Des
arXiv:2603.14135v3 Announce Type: replace-cross Abstract: This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples
arXiv:2604.06468v1 Announce Type: new Abstract: Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extracto
arXiv:2401.00870v5 Announce Type: replace-cross Abstract: State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud
arXiv:2604.06196v1 Announce Type: cross Abstract: Three-way logical question answering (QA) assigns True/False/Unknown to a hypothesis H given a premise set S. While modern large language models
arXiv:2604.07484v1 Announce Type: cross Abstract: Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering grea
arXiv:2604.07354v1 Announce Type: new Abstract: The accuracy frontier of speech-to-text systems has plateaued on academic benchmarks.1 In contrast, industrial benchmarks and adoption in high-stakes do
arXiv:2604.07885v1 Announce Type: new Abstract: This work explores the connection between (non-)literalness and plausibility at the example of subject-verb-object events in English. We design a system
arXiv:2604.06435v1 Announce Type: cross Abstract: Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare. While VAD has been extensively
arXiv:2604.06926v1 Announce Type: cross Abstract: We study the continuous-time structure of the difference-of-convex algorithm (DCA) for smooth DC decompositions with a strongly convex component. In d
arXiv:2604.06837v1 Announce Type: new Abstract: The problem of solving Markov decision processes under function approximation remains a fundamental challenge, even under linear function approximation
arXiv:2510.18196v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are commonly used as evaluators in various applications, but the reliability of the outcomes remains a challenge.
arXiv:2604.07069v1 Announce Type: cross Abstract: This paper presents an indirect data-driven output feedback controller synthesis for nonlinear systems, leveraging Structured State-space Models (SSMs
arXiv:2601.08950v3 Announce Type: replace Abstract: Despite their growing adoption in education, LLMs remain misaligned with the core principle of effective tutoring: the dialogic construction of know
arXiv:2604.08088v1 Announce Type: new Abstract: Text-to-motion generation has attracted increasing attention in the research community recently, with potential applications in animation, virtual reali
arXiv:2604.03836v2 Announce Type: replace-cross Abstract: Semantics are one of the primary sources of top-down preattentive information. Modern deep object detectors excel at extracting such valuable
arXiv:2601.22451v2 Announce Type: replace-cross Abstract: Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models
arXiv:2604.08519v1 Announce Type: new Abstract: Large language models (LLMs) can struggle to memorize factual knowledge in their parameters, often leading to hallucinations and poor performance on kno
arXiv:2604.08457v1 Announce Type: new Abstract: Cooperative autonomous driving requires traffic scene understanding from both vehicle and infrastructure perspectives. While vision-language models (VLM
arXiv:2604.06202v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed natural language processing, yet their capabilities remain uneven across languages. Most multilingual mo
arXiv:2604.07786v1 Announce Type: new Abstract: Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthe
arXiv:2604.07466v1 Announce Type: new Abstract: Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains
arXiv:2603.22000v2 Announce Type: replace Abstract: We propose a method for non-parametric conditional distribution estimation based on partitioning covariate-sorted observations into contiguous bins
arXiv:2508.04929v4 Announce Type: replace-cross Abstract: As a critical modality for structural biology, cryogenic electron microscopy (cryo-EM) facilitates the determination of macromolecular structu
arXiv:2604.07132v1 Announce Type: cross Abstract: Child Sexual Abuse Imagery (CSAI) classification is an important yet challenging problem for computer vision research due to the strict legal and ethi
arXiv:2604.06616v1 Announce Type: cross Abstract: Hybrid queries combining high-dimensional vector similarity search with spatio-temporal filters are increasingly critical for modern retrieval-augment
arXiv:2603.20114v4 Announce Type: replace Abstract: We aim to examine the extent to which Large Language Models (LLMs) can 'talk much' about grammar modules, providing evidence from syntax core proper
arXiv:2512.19173v2 Announce Type: replace Abstract: Current chart-related tasks, such as chart generation (NL2Chart), chart schema parsing, chart data parsing, and chart question answering (ChartQA),
arXiv:2602.15889v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used in research as both tools and objects of study. Much of this work assumes that LLM performa
arXiv:2604.07758v1 Announce Type: new Abstract: Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed-state image remains challenging
arXiv:2604.06899v1 Announce Type: cross Abstract: Data leakage is the inadvertent transfer of information between training and evaluation datasets that poses a subtle, yet critical, risk to the reliab
arXiv:2604.07892v1 Announce Type: new Abstract: Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsiste
arXiv:2604.06520v1 Announce Type: cross Abstract: We address the problems of giving a semantics to- and doing query answering (QA) on a relational database (RDB) that has missing values (MVs). The cau
arXiv:2604.08261v1 Announce Type: new Abstract: The complex and dynamic real-world clinical environment demands reliable deep learning (DL) systems. Out-of-distribution (OOD) detection plays a critica
arXiv:2604.07125v1 Announce Type: cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy
arXiv:2511.19365v2 Announce Type: replace-cross Abstract: Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the t
arXiv:2604.07518v1 Announce Type: new Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add the c
arXiv:2510.20549v2 Announce Type: replace Abstract: Despite advancements in SLAM technologies, robust operation under challenging conditions such as low-texture, motion-blur, or challenging lighting r
arXiv:2604.08527v1 Announce Type: new Abstract: On-policy distillation (OPD) trains student models under their own induced distribution while leveraging supervision from stronger teachers. We identify
arXiv:2604.08495v1 Announce Type: cross Abstract: This paper addresses the decentralized non-uniform area coverage problem for multi-agent systems, a critical task in missions with high spatial priori
arXiv:2604.06193v1 Announce Type: cross Abstract: Depression is underdiagnosed in primary care, yet timely identification remains critical. Recorded clinical encounters, increasingly common with digit
arXiv:2604.07253v1 Announce Type: cross Abstract: In gender-restrictive and surveilled contexts, where access to formal education may be restricted for women, pursuing education involves safety and pr
arXiv:2604.07717v1 Announce Type: new Abstract: Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental
arXiv:2604.06220v1 Announce Type: cross Abstract: Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occ
arXiv:2604.04507v2 Announce Type: replace-cross Abstract: The rapid adoption of low-precision arithmetic in artificial intelligence and edge computing has created a strong demand for energy-efficient
arXiv:2601.08258v3 Announce Type: replace Abstract: Large language models increasingly fail in a way that scalar accuracy cannot diagnose: they produce a sound reasoning trace and then abandon it unde
arXiv:2604.06352v1 Announce Type: cross Abstract: Accurate dietary assessment is critical for precision nutrition, yet most image-based methods rely on a single pre-consumption image and provide only
arXiv:2406.06408v2 Announce Type: replace-cross Abstract: Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tun
arXiv:2604.08504v1 Announce Type: cross Abstract: We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of d
arXiv:2306.14685v5 Announce Type: replace-cross Abstract: We demonstrate that pre-trained text-to-image diffusion models, despite being trained on raster images, possess a remarkable capacity to guide
arXiv:2508.19982v5 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and fle
arXiv:2604.07213v1 Announce Type: new Abstract: High-dimensional data are often modeled as lying near a low-dimensional manifold. We study how to construct diffusion processes on this data manifold in
arXiv:2604.08084v1 Announce Type: new Abstract: Current video captioning methods usually use an encoder-decoder structure to generate text autoregressively. However, autoregressive methods have inhere
arXiv:2604.06863v1 Announce Type: cross Abstract: Skin-toned emojis are crucial for fostering personal identity and social inclusion in online communication. As AI models, particularly Large Language
arXiv:2604.07166v1 Announce Type: cross Abstract: Although visual foundation models like DINOv2 provide state-of-the-art performance as feature extractors, their complex, high-dimensional representati
arXiv:2604.08074v1 Announce Type: new Abstract: Reliable and weather-robust perception systems are essential for safe autonomous driving and typically employ multi-modal sensor configurations to achie
arXiv:2604.07723v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment arbitrary category regions in images using open-vocabulary prompts, necessitating that exis
arXiv:2511.22599v2 Announce Type: replace-cross Abstract: Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless na