A paradox of AI fluency
arXiv:2604.25905v1 Announce Type: new Abstract: How much does a user's skill with AI shape what AI actually delivers for them? This question is critical for users, AI product builders, and society at
Knowledge catalogue
arXiv:2604.25905v1 Announce Type: new Abstract: How much does a user's skill with AI shape what AI actually delivers for them? This question is critical for users, AI product builders, and society at
arXiv:2604.24822v1 Announce Type: cross Abstract: Context: Software vulnerabilities pose significant security threats to software systems, especially as software is increasingly used across many areas
arXiv:2604.24936v1 Announce Type: new Abstract: Techniques for concept extraction, such as sparse autoencoders and transcoders, aim to extract high-level symbolic concepts from low-level nonsymbolic r
arXiv:2408.12974v4 Announce Type: replace Abstract: Semantic segmentation of microscopy cell images by deep learning is a significant technique. We considered that the Transformers, which have recentl
arXiv:2604.25710v1 Announce Type: cross Abstract: In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the fie
arXiv:2604.24906v1 Announce Type: cross Abstract: Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage.
arXiv:2410.24214v3 Announce Type: replace-cross Abstract: Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustne
arXiv:2604.24767v1 Announce Type: cross Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standa
arXiv:2604.25702v1 Announce Type: new Abstract: Contemporary neural machine translation (NMT) systems are almost exclusively built by training on supervised parallel data. Despite the tremendous progr
arXiv:2604.25452v1 Announce Type: new Abstract: Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing a
arXiv:2604.25392v1 Announce Type: new Abstract: This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for b
arXiv:2604.25408v1 Announce Type: new Abstract: Low-level image processing has long been evaluated mainly from the perspective of visual fidelity. However, with the rise of deep learning and generativ
arXiv:2604.25416v1 Announce Type: new Abstract: Model-Based Reinforcement Learning distinguishes between physical dynamics models operating on proprioceptive inputs and latent dynamics models operatin
arXiv:2604.25541v1 Announce Type: cross Abstract: Mobile robots that move between outdoor and indoor environments still struggle with consistent positioning. Satellite-based and terrestrial ranging ea
arXiv:2603.28886v2 Announce Type: replace-cross Abstract: Graph-augmented retrieval combines dense similarity with graph-based relevance signals such as Personalized PageRank (PPR), but these scores h
arXiv:2603.26783v2 Announce Type: replace Abstract: Diffusion models can be challenged in the low signal-to-noise regime, where they have to make pixel-level predictions despite the presence of high n
arXiv:2604.25241v1 Announce Type: new Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of ad
arXiv:2602.01785v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational eff
arXiv:2604.24768v1 Announce Type: new Abstract: In this chapter, we investigate the bending behavior of a perforated nanobeam subjected to sinusoidal loading using an efficient and computationally rob
arXiv:2604.25172v1 Announce Type: cross Abstract: Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at reso
arXiv:2604.24959v1 Announce Type: new Abstract: Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is co
arXiv:2603.12118v2 Announce Type: replace Abstract: Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input
arXiv:2604.24793v1 Announce Type: cross Abstract: We present CRC-SAM, a unified framework for colorectal cancer segmentation across colonoscopy, CT, and histopathology images. Unlike prior single-moda
arXiv:2510.02765v4 Announce Type: replace Abstract: Gradient-based algorithms are a cornerstone of artificial neural network training, yet it remains unclear whether biological neural networks use sim
arXiv:2604.25300v1 Announce Type: new Abstract: Deploying tiny object perception on edge platforms is challenging because practical systems must satisfy both strict compute budgets and end-to-end late
arXiv:2510.19669v4 Announce Type: replace Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility
arXiv:2604.25794v1 Announce Type: cross Abstract: Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in
arXiv:2604.25423v1 Announce Type: new Abstract: Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial ex
arXiv:2604.25533v1 Announce Type: new Abstract: Worldwide image geo-localization aims to infer the geographic location of an image captured anywhere on Earth, spanning street, city, regional, national
arXiv:2604.25021v1 Announce Type: new Abstract: We study online regression with the square loss in a reproducing kernel Hilbert space under a dynamic regret criterion. The learner is compared with a t
arXiv:2604.25267v1 Announce Type: new Abstract: This paper addresses the Dynamic UGV-UAV Cooperative Path Planning (DUCPP) problem involving one unmanned ground vehicle (UGV) assisted by one or more u
arXiv:2604.25319v1 Announce Type: new Abstract: The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth f
arXiv:2604.24770v1 Announce Type: new Abstract: Despite recent progress in automatic speech recognition (ASR), elderly ASR (EASR) remains challenging due to limited training data and the distinct acou
arXiv:2604.25481v1 Announce Type: cross Abstract: We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an en
arXiv:2604.25550v1 Announce Type: new Abstract: SignSGD compresses each stochastic gradient coordinate to a single bit, offering substantial memory and communication savings, but its 1-bit quantizatio
arXiv:2604.25119v1 Announce Type: new Abstract: Auditing the fine-tunes of open-weight generative models for harmful specialization has become a new governance challenge for model hosting platforms. T
arXiv:2602.13864v2 Announce Type: replace-cross Abstract: Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data impu
arXiv:2604.25885v1 Announce Type: cross Abstract: Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performa
arXiv:2506.07435v3 Announce Type: replace-cross Abstract: Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we
Sumitomo Mitsui Financial Group (SMFG) has partnered with Sakana AI to develop a system that can deliver strategic business proposals to corporate clients within hours, leveraging AI technology to acc
For years, voice AI has been stuck in a rigid loop: think, then speak. But real human conversation is messy, overlapping, and asynchronous. In our new #ICASSP2026 work, we built a tandem architecture
arXiv:2604.25145v1 Announce Type: cross Abstract: Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but e
arXiv:2507.07847v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and redu
arXiv:2604.25372v1 Announce Type: cross Abstract: While it is generally understood that zeroth-order (ZO) algorithms have an extra dependency on their number of iterations for any choice of parameters
arXiv:2604.25866v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, yet little is known about how emotion recognition is
arXiv:2604.24766v1 Announce Type: new Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controll
arXiv:2604.25390v1 Announce Type: cross Abstract: Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity.
arXiv:2412.08835v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message p
arXiv:2503.06100v5 Announce Type: replace Abstract: High-precision dichotomous image segmentation (DIS) is a task of extracting fine-grained objects from high-resolution images. Existing methods trade
arXiv:2604.25164v1 Announce Type: new Abstract: Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. Howeve
arXiv:2604.25464v1 Announce Type: new Abstract: Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key ch
arXiv:2603.28750v3 Announce Type: replace Abstract: For three decades online recurrent learning has been assumed to require propagating a Jacobian tensor through the network's dynamics at O(n^4) per s
arXiv:2604.25795v1 Announce Type: new Abstract: Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teacher) to a smaller network (student) with little sacri
arXiv:2604.25563v1 Announce Type: new Abstract: 3D-printed artificial skins are a scalable approach to whole-body tactile and proximity coverage, but prior implementations have been limited to unimoda
arXiv:2604.24942v1 Announce Type: new Abstract: Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxelwise approaches are
arXiv:2604.25809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit strong performance in instruction following and open-ended vision-language reasoning, yet they frequently generate
arXiv:2604.24804v1 Announce Type: cross Abstract: Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models
arXiv:2507.15707v2 Announce Type: replace Abstract: Large Language Models (LLMs) have been evaluated using diverse question types, e.g., multiple-choice, true/false, and short/long answers. This study
Today, nuclear energy enjoys a rare moment of support across the political spectrum in the US. Interest from tech companies that are scrambling to meet demand for massive data centers has sparked a re
arXiv:2604.25196v1 Announce Type: new Abstract: Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive