Fabricator or dynamic translator?
arXiv:2604.15165v1 Announce Type: new Abstract: LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overge
Knowledge catalogue
arXiv:2604.15165v1 Announce Type: new Abstract: LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overge
arXiv:2506.14121v2 Announce Type: replace Abstract: Face super-resolution (FSR) under limited computational budgets remains challenging. Existing methods often treat all facial pixels equally, leading
arXiv:2604.14325v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance and have revolutionized NLP, but their lack of explainability keeps them treated as black boxes,
arXiv:2506.23334v3 Announce Type: replace-cross Abstract: Federated learning enables collaborative training of deep learning models across institutions without sharing sensitive patient data. However,
arXiv:2604.06647v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems are typically evaluated under static assumptions, despite being frequently corrected through user or ex
arXiv:2604.14734v1 Announce Type: new Abstract: Morphing is a challenge to face recognition (FR) for which several morphing attack detection solutions have been proposed. We argue that face recognitio
arXiv:2604.13354v1 Announce Type: cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science. Generative models, especially
arXiv:2604.15003v1 Announce Type: new Abstract: The rapid rise of image-to-video (I2V) generation enables realistic videos to be created from a single image but also brings new forensic demands. Unlik
arXiv:2604.15037v1 Announce Type: cross Abstract: Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existi
arXiv:2604.15244v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose outputs that a stronger target mod
arXiv:2604.14884v1 Announce Type: new Abstract: Small object detection remains a significant challenge due to feature degradation from downsampling, mutual occlusion in dense clusters, and complex bac
arXiv:2601.10237v2 Announce Type: replace Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under wor
arXiv:2604.14710v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) aims to retrieve target images by integrating a reference image with a corresponding modification text. CIR requires join
arXiv:2604.14702v1 Announce Type: new Abstract: Multiplicative gating is widely used in neural architectures and has recently been applied to attention layers to improve performance and training stabi
arXiv:2509.02571v2 Announce Type: replace-cross Abstract: This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening
arXiv:2604.15306v1 Announce Type: cross Abstract: Whether language models can systematically generalize remains actively debated. Yet empirical performance is jointly shaped by multiple factors such a
arXiv:2604.14397v1 Announce Type: new Abstract: We study the task of automatically expanding WordNet-style lexical resources to new languages through sense generation. We generate senses by associatin
arXiv:2604.14575v1 Announce Type: new Abstract: Data-driven operations management often relies on parameters estimated from costly human-generated labels. Recent advances in large language models (LLM
arXiv:2604.14933v1 Announce Type: new Abstract: Skeleton-based human action recognition is a powerful approach for understanding human behaviour from pose data, but collecting large-scale, diverse, an
arXiv:2604.14800v1 Announce Type: cross Abstract: Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence
arXiv:2604.14302v1 Announce Type: new Abstract: We tackle a new problem: generating geometrically consistent multi-view scenes from a single freehand sketch. Freehand sketches are the most geometrical
arXiv:2604.14541v1 Announce Type: new Abstract: We present a framework for explicit emotion control in feed-forward, single-image 3D head avatar reconstruction. Unlike existing pipelines where emotion
arXiv:2511.02135v2 Announce Type: replace Abstract: Large language models (LLMs) have become a popular approach for simulating human behaviors, yet it remains unclear if LLMs are necessary for all sim
arXiv:2604.14857v1 Announce Type: new Abstract: Automotive 4D imaging radar is well suited for operation in dusty and low-visibility environments, but scan registration remains challenging due to scan
arXiv:2604.14262v1 Announce Type: new Abstract: GUI grounding models report over 85% accuracy on standard benchmarks, yet drop 27-56 percentage points when instructions require spatial reasoning rathe
arXiv:2604.14724v1 Announce Type: new Abstract: Vision State Space Models (SSMs) like Vim, VMamba, and SiMBA rely on complex scanning strategies to adapt sequential SSMs to process 2D images, introduc
arXiv:2510.02539v2 Announce Type: replace Abstract: Neural document retrieval often treats a corpus as a flat cloud of vectors scored at a single granularity, leaving corpus structure underused and ex
arXiv:2311.11841v4 Announce Type: replace-cross Abstract: We consider the stochastic gradient method with random reshuffling (mathsf{RR}) for tackling smooth nonconvex optimization problems. mathsf{RR
arXiv:2604.14632v1 Announce Type: new Abstract: Conventional RGB-based high dynamic range (HDR) imaging faces a fundamental trade-off between motion artifacts in multi-exposure captures and irreversib
arXiv:2602.20091v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effec
'I don't think people are sufficiently prepared notwithstanding what happened in 2021, for the possibility that he will try to fuck with this election. And he will. He's already basically telling us t
I don't understand why we automatically give people credit for 'sincere views.' Like who gives a fuck? If I have a sincere view that a transdimensional vampire attack is imminent it doesn't make it sa
arXiv:2501.09331v3 Announce Type: replace Abstract: A machine that learns a task from observations must encounter and process uncertainty and novelty, especially when it is to maintain performance whe
arXiv:2604.14837v1 Announce Type: new Abstract: Alzheimer's disease (AD) confirmation often relies on positron emission tomography (PET) or cerebrospinal fluid (CSF) analysis, which are costly and inv
arXiv:2506.14844v2 Announce Type: replace-cross Abstract: Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. Th
arXiv:2604.14925v1 Announce Type: new Abstract: Recently, sparse autoencoders (SAEs) have emerged as a promising technique for interpreting activations in foundation models by disentangling features i
arXiv:2411.09209v5 Announce Type: replace Abstract: Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, th
arXiv:2604.05158v2 Announce Type: replace Abstract: Large language models encode extensive world knowledge valuable for zero-shot named entity recognition. However, their causal attention mechanism, w
arXiv:2407.00809v3 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kern
arXiv:2604.15141v1 Announce Type: new Abstract: Higher-order learning is fundamentally rooted in exploiting compositional features. It clearly hinges on enriching the representation by more elaborate
arXiv:2506.00433v4 Announce Type: replace Abstract: High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preserv
arXiv:2604.01197v2 Announce Type: replace-cross Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study
arXiv:2604.14931v1 Announce Type: cross Abstract: Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels
arXiv:2501.11711v2 Announce Type: replace Abstract: The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of co
arXiv:2604.13348v1 Announce Type: new Abstract: We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI
arXiv:2604.15216v1 Announce Type: cross Abstract: Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams
arXiv:2604.14574v1 Announce Type: new Abstract: With the rapid advancement of deep learning in image generation, facial forgery techniques have achieved unprecedented realism, posing serious threats t
arXiv:2604.14582v1 Announce Type: new Abstract: High-resolution (HR) land-cover mapping is often constrained by the high cost of dense HR annotations. We revisit this problem from the perspective of m
arXiv:2602.07529v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequ
arXiv:2604.14218v1 Announce Type: new Abstract: Hate speech detection in Devanagari-scripted social media memes presents compounded challenges: multimodal content structure, script-specific linguistic
arXiv:2604.15107v1 Announce Type: cross Abstract: Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in
arXiv:2603.22564v2 Announce Type: replace Abstract: Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A ke
arXiv:2604.14208v1 Announce Type: cross Abstract: This work develops machine learning approaches to classify structured light wave beams developing random speckle disturbances as they propagate throug
arXiv:2604.14957v1 Announce Type: cross Abstract: Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their per
arXiv:2604.14565v1 Announce Type: new Abstract: Embodiment is a significant keyword in recent machine learning fields. This study focused on the passive nature of the body of a biped robot to generate
arXiv:2604.15076v1 Announce Type: new Abstract: To navigate a space, the brain makes an internal representation of the environment using different cells such as place cells, grid cells, head direction
arXiv:2604.14595v1 Announce Type: new Abstract: This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness
arXiv:2604.15063v1 Announce Type: new Abstract: Gradient inversion attacks threaten client privacy in federated learning by reconstructing training samples from clients' shared gradients. Gradients ag
arXiv:2604.14816v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automati
arXiv:2604.14501v1 Announce Type: new Abstract: We study the expressive power and limitations of multi-layer state-space models (SSMs). First, we show that multi-layer SSMs face fundamental limitation