Knowledge catalogue
arXiv:2608.15310v1 Announce Type: cross Abstract: Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for eff
arXiv:2510.04927v2 Announce Type: replace-cross Abstract: Automatic modulation classification (AMC) is a core enabler of cognitive wireless systems, providing spectrum awareness and supporting adaptiv
arXiv:2608.14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy. A major
arXiv:2608.15381v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a tension betwe
arXiv:2608.15343v1 Announce Type: new Abstract: Reconstructing three-dimensional computed tomography (CT) from severely constrained projections is highly ill-posed. Sparse angular sampling, restricted
arXiv:2608.16385v1 Announce Type: new Abstract: Early time-series classification (ETSC) aims to make accurate predictions from partially observed time series as early as possible. Although various sto
arXiv:2608.15976v1 Announce Type: new Abstract: A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future
arXiv:2608.15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational
arXiv:2608.15727v1 Announce Type: cross Abstract: Diffusion models have recently shown strong potential for multivariate time-series anomaly detection by learning the distribution of normal data throu
arXiv:2608.16523v1 Announce Type: new Abstract: Event cameras generate asynchronous, high-frequency data streams offering spatially sparse information at lower latency than traditional cameras.In prin
arXiv:2602.13185v2 Announce Type: replace Abstract: Effective and generalizable control in video generation remains a significant challenge. While many methods rely on ambiguous or task-specific signa
arXiv:2608.16853v1 Announce Type: new Abstract: Multi-segment suction-based soft robots are promising for inspection and maintenance in confined or fragile environments, but existing approaches still
arXiv:2608.15410v1 Announce Type: cross Abstract: Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offeri
arXiv:2608.14550v1 Announce Type: new Abstract: AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. W
arXiv:2608.15818v1 Announce Type: new Abstract: Music-driven dance video synthesis aims to animate a reference person according to a given music clip. The task is challenging because it requires a mod
arXiv:2608.15602v1 Announce Type: cross Abstract: While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlo
arXiv:2608.15296v1 Announce Type: new Abstract: Audio-driven 3D facial animation is essential for advancing immersion and interactivity in virtual experiences. Although recent advances have shown prom
arXiv:2509.06461v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in complex visu
arXiv:2507.15502v1 Announce Type: cross Abstract: Postoperative follow-up plays a crucial role in monitoring recovery and identifying complications. However, traditional approaches, typically involvin
arXiv:2608.15009v1 Announce Type: cross Abstract: Embodied intelligent ultrasound scanning enables the automation and standardization of the ultrasound examination process by integrating perception, d
arXiv:2608.16273v1 Announce Type: cross Abstract: Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research p
arXiv:2603.15427v2 Announce Type: replace-cross Abstract: Robots are increasingly deployed across diverse domains and designed for multi-purpose operation. As robotic systems grow in complexity and op
arXiv:2608.14563v1 Announce Type: cross Abstract: Forward-Pass-Only MLP training (FPO) adapts large language models without a backward pass through the model body, achieving 2.7--3.2x the throughput o
arXiv:2608.14636v1 Announce Type: cross Abstract: Fractional optimization methods and fractal activation functions are two independent directions for improving neural network training. Fractional opti
arXiv:2604.02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular cla
arXiv:2608.15054v1 Announce Type: new Abstract: Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land cover information
arXiv:2603.17555v2 Announce Type: replace-cross Abstract: Diffusion-based image-to-video (I2V) models are increasingly effective, yet they struggle to scale to ultra-high-resolution inputs (e.g., 4K).
arXiv:2608.15536v1 Announce Type: new Abstract: In value-based argumentation, an audience's ordering of values decides which attacks succeed as defeats. In many settings the deciding factor is not the
arXiv:2608.14937v1 Announce Type: new Abstract: The increasing dynamic demands of modern robotic joints require current controllers to achieve high bandwidth over wide operating ranges of speed, accel
arXiv:2608.14740v1 Announce Type: new Abstract: Unified image and video creation requires a model to follow diverse instructions while preserving identity, geometry, and temporal structure from visual
arXiv:2608.14567v1 Announce Type: new Abstract: This paper presents a targeted narrative review establishing the historical and theoretical foundations for computational belief change implementation.
arXiv:2608.14771v1 Announce Type: new Abstract: Making language models solve constraint problems reliably often means having them translate the problem into a formal specification and delegating the s
arXiv:2608.15580v1 Announce Type: new Abstract: Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morpho
arXiv:2608.15812v1 Announce Type: new Abstract: This paper documents a frozen engineering project on personalized Chinese handwriting. The project started from approximately 200 real handwriting image
arXiv:2608.15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and
arXiv:2209.01432v4 Announce Type: replace-cross Abstract: In this paper we study probabilistic and neural network approximations for solutions to Poisson equation subject to Holder data in general bou
arXiv:2608.14787v1 Announce Type: cross Abstract: Speculative decoding is a leading technique to reduce the cost of autoregressive generation by using a small drafter to propose several tokens, which
arXiv:2506.16697v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering psychological research both as tools and as objects of inquiry. Yet many studies apply human instrum
arXiv:2608.14574v1 Announce Type: cross Abstract: The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-dr
arXiv:2608.16002v1 Announce Type: cross Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ
arXiv:2604.05635v2 Announce Type: replace Abstract: Numerical preprocessing remains a critical component of tabular deep learning, as the representation of continuous features can strongly affect down
arXiv:2608.15163v1 Announce Type: new Abstract: Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to stu
arXiv:2608.14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or
arXiv:2608.16303v1 Announce Type: new Abstract: Long-term emotional-support agents require memory mechanisms for personalized understanding across sessions. However, emotional-support dialogue is ofte
arXiv:2410.15921v3 Announce Type: replace Abstract: Existing source-seeking algorithms for robot swarms typically require either direct gradient measurements or rigid geometric formations, limiting th
arXiv:2608.15952v1 Announce Type: cross Abstract: Differences between high-energy event generators can arise at several stages of the collision simulation, from the hard scattering through parton show
arXiv:2608.15065v1 Announce Type: new Abstract: Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inference is a prere
arXiv:2506.03096v2 Announce Type: replace Abstract: Contrastive language-image pre-training aligns features of text-image pairs in a common latent space via distinct encoders for each modality. While
arXiv:2604.02583v2 Announce Type: replace Abstract: We propose FusionBERT, a novel multi-view visual fusion framework for image--3D multimodal retrieval. Existing image--3D representation learning met
arXiv:2608.15004v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and Computed Tomography (CT) is a primary imaging tool for screen
arXiv:2608.15707v1 Announce Type: new Abstract: Correcting robot manipulation policies through human intervention holds great promise for real-world deployment, yet human operators are inherently impe
arXiv:2608.15428v1 Announce Type: cross Abstract: Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question. Measuring that gap takes care:
arXiv:2608.15022v1 Announce Type: new Abstract: Language models hold latent quantities in a form they can report on, and more of a quantity is present in that form when the task requires reusing it fl
arXiv:2608.15084v1 Announce Type: cross Abstract: Test-time augmentation (TTA) has proven effective for improving model robustness and uncertainty estimation in computer vision, yet its application to
arXiv:2608.15651v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance. Fixed-budget encoders consum
arXiv:2608.16234v1 Announce Type: new Abstract: Driving World Models (DWMs) have recently advanced rapidly with generative models, yet most existing methods mainly focus on conditional scene generatio
arXiv:2608.14986v1 Announce Type: cross Abstract: Long-horizon robotic manipulation fundamentally relies on persistent spatial memory. However, existing 3D memory systems function merely as passive re
arXiv:2608.15645v1 Announce Type: new Abstract: Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference. The theory of transportabi
arXiv:2608.15848v1 Announce Type: cross Abstract: We study generalized linear bandits with memory, an endogenous non-stationary setting in which rewards depend on past actions through a finite memory
arXiv:2608.14804v1 Announce Type: new Abstract: Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in, text out, o