Knowledge catalogue
arXiv:2605.26754v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) increasingly underpins high-stakes applications, yet remains vulnerable to Confundo-style poisoning where adversa
arXiv:2605.27116v1 Announce Type: new Abstract: Open-vocabulary object detection (OVD) has made significant progress, enabling detectors to generalize from seen to unseen categories. However, real-wor
arXiv:2405.16417v2 Announce Type: replace Abstract: Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often invo
arXiv:2510.09405v2 Announce Type: replace Abstract: Radio frequency fingerprint identification (RFFI) is a key technique for wireless network security, leveraging intrinsic hardware imperfections to e
arXiv:2605.26514v1 Announce Type: cross Abstract: Confirming Alzheimer's disease (AD) typically relies on positron emission tomography (PET), which remains costly and invasive, motivating the use of s
arXiv:2605.26315v1 Announce Type: cross Abstract: Direct Preference Optimisation (DPO) is widely used for safety alignment in large language models. However, prior work shows it is brittle and exhibit
arXiv:2605.26195v1 Announce Type: cross Abstract: LLM-based agents are increasingly used for cybersecurity tasks, but most existing systems rely on fixed, human-designed scaffolds that struggle to ada
arXiv:2605.26286v1 Announce Type: cross Abstract: Real-world multi-agent reinforcement learning (MARL) systems must often operate under stale observations, stochastic communication delays, and intermi
arXiv:2605.26629v1 Announce Type: new Abstract: Novel-view synthesis and 3D reconstruction from sparse posed images are central to robotics and AR/VR. Yet, feed-forward 3D Gaussian reconstruction fail
arXiv:2605.26496v1 Announce Type: cross Abstract: The Mixture of Experts MoE architecture is highly promising for resource constrained on device deployments yet training these models from scratch incu
arXiv:2605.26451v1 Announce Type: cross Abstract: Producing presentation slides automatically entails coordinating narrative structure with page-level graphic design under strict spatial constraints.
arXiv:2605.26382v1 Announce Type: new Abstract: Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefo
arXiv:2602.08586v3 Announce Type: replace Abstract: Multi-agent LLM systems consistently outperform single-agent baselines, yet practitioners still cannot predict which design works for a new task or
arXiv:2605.26468v1 Announce Type: cross Abstract: Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the fir
arXiv:2605.26949v1 Announce Type: new Abstract: 3D shape completion from partial scans remains challenging for unseen categories and noisy real-world observations, where geometry alone is often insuff
arXiv:2604.00648v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has enabled efficient 3D scene reconstruction from everyday images with real-time, high-fidelity rendering, greatly adv
arXiv:2505.18603v2 Announce Type: replace Abstract: Document understanding aims to perform question answering and information extraction over document images, where the visual content is highly inform
arXiv:2605.26316v1 Announce Type: cross Abstract: Controllable and physically grounded egocentric video generation is essential for embodied agents to reason about how their own and others' actions ma
arXiv:2210.02573v5 Announce Type: replace Abstract: Learning the physical simulation on large-scale meshes with flat Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due
arXiv:2603.13373v3 Announce Type: replace-cross Abstract: In ubiquitous and mobile health systems, computational models infer human states from wearable, behavioral, and physiological sensing data. In
arXiv:2501.06708v5 Announce Type: replace-cross Abstract: Large-scale web-crawled datasets contain noise, bias, and irrelevant information, necessitating data selection techniques. Existing methods de
arXiv:2605.26292v1 Announce Type: cross Abstract: Parameter-efficient adaptation of vision-language foundation models is crucial for precise multimodal understanding of biomedical images, yet existing
arXiv:2605.27045v1 Announce Type: new Abstract: The democratization of LLMs has accelerated the generation and circulation of highly fluent disinformation, making traditional syntax-semantic verificat
arXiv:2603.13282v2 Announce Type: replace-cross Abstract: Federated Learning (FL) with Low-Rank Adaptation (LoRA) has become a standard for privacy-preserving LLM fine-tuning. However, existing person
arXiv:2603.17685v3 Announce Type: replace Abstract: Balancing policy expressiveness with the exploration-exploitation trade-off is a core challenge in online Reinforcement Learning (RL). While Stochas
arXiv:2602.01941v2 Announce Type: replace-cross Abstract: Autoregressive learning of time-stepping operators provides an effective approach to data-driven partial differential equation (PDE) simulatio
arXiv:2605.26508v1 Announce Type: cross Abstract: We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counte
arXiv:2605.27178v1 Announce Type: cross Abstract: We address the challenging task of 3D object segmentation in complex scene point clouds without relying on any scene-level human annotations during tr
arXiv:2605.27352v1 Announce Type: new Abstract: Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, but, especially for uniform-rate models, they o
arXiv:2605.26755v1 Announce Type: new Abstract: Multilingual fact verification requires evidence that is both relevant and sufficiently complete for reliable factuality prediction. However, existing s
arXiv:2605.26403v1 Announce Type: new Abstract: A long-standing goal of the research community is to develop highly interactive LLM-based dialogue agents. Recent research focuses on optimizing policie
arXiv:2605.26408v1 Announce Type: new Abstract: Causal discovery in time series is increasingly performed using nonlinear machine-learning models, yet the resulting causal relationships are almost alw
arXiv:2605.26121v1 Announce Type: cross Abstract: LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, optimal mixing is hindered by categorization flaws:
arXiv:2605.26857v1 Announce Type: new Abstract: Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector tran
arXiv:2605.26823v1 Announce Type: new Abstract: Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic
arXiv:2504.00307v2 Announce Type: replace Abstract: Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially o
arXiv:2605.27013v1 Announce Type: new Abstract: Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimiz
arXiv:2605.26600v1 Announce Type: cross Abstract: Standard Self-Supervised Learning (SSL) for Automatic Modulation Recognition (AMR) struggles with ineffective isotropic augmentations, spectral instab
arXiv:2603.09551v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reason
arXiv:2411.02355v4 Announce Type: replace-cross Abstract: Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across differen
arXiv:2601.22384v2 Announce Type: replace-cross Abstract: Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is
arXiv:2602.02518v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many real-world knowledge sources are organize
arXiv:2605.27204v1 Announce Type: new Abstract: Scientific paper evaluation often involves not only assessing a manuscript itself, but also relating it to contemporaneous research and prior literature
arXiv:2605.27309v1 Announce Type: new Abstract: The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emission
arXiv:2602.19206v3 Announce Type: replace Abstract: Zero-shot 3D Anomaly Detection is an emerging task that aims to detect anomalies in a target dataset without any target training data, which is part
arXiv:2605.27354v1 Announce Type: cross Abstract: Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering la
arXiv:2605.26835v1 Announce Type: new Abstract: LLM-based multi-agent systems have been widely adopted for knowledge retrieval and report generation, synthesizing known information through web search
arXiv:2510.01336v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates LLM inference by using a smaller draft model to speculate tokens that a larger target model verifies. Verific
arXiv:2507.20758v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. We analyze CoT's oper
arXiv:2605.26190v1 Announce Type: cross Abstract: This paper presents the HRVConformer, a novel deep learning architecture for the classification of hypoxic-ischemic encephalopathy (HIE) using the ins
arXiv:2605.26807v1 Announce Type: cross Abstract: LLMs can now produce full HTML pages, but many of those pages are only superficially correct: they render once, then fail under scroll, hover, click,
arXiv:2605.27138v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific data from trained language models. In real-world deployments, unlearning requests often arri
arXiv:2605.26902v1 Announce Type: cross Abstract: Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansi
arXiv:2605.27132v1 Announce Type: new Abstract: Multilevel image thresholding is widely used for segmentation in applications ranging from medical imaging to remote sensing. Classical objective functi
arXiv:2605.26786v1 Announce Type: cross Abstract: Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and mach
A manager describes shifting their leadership approach by providing agents (team members) with decision-making criteria rather than directly answering their questions, enabling them to develop problem
arXiv:2605.26808v1 Announce Type: cross Abstract: Hallucination is a central limitation of large language models (LLMs), and substantial effort has been devoted to understanding and mitigating it. Tow
arXiv:2502.06963v3 Announce Type: replace-cross Abstract: The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external
Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation http://pub.sakana.ai/diffusionblocks What if we didn’t have to hold an entire neural network in memory to t
arXiv:2510.00902v2 Announce Type: replace Abstract: Transfer learning is crucial for medical imaging, yet the selection of source datasets often relies on researchers' intuition rather than systematic