Knowledge catalogue
arXiv:2604.16058v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Models (LLMs) in software development has made distinguishing AI-generated code from human-written code a cr
arXiv:2604.15671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential for embodied decision-making; however, their application in complex chemical
arXiv:2601.02531v2 Announce Type: replace-cross Abstract: Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural
arXiv:2604.15472v1 Announce Type: cross Abstract: This paper is dedicated to lossless data compression with probability estimation using neural networks. First, we propose a probability estimation arc
arXiv:2604.15707v1 Announce Type: new Abstract: Spatiotemporal Local Binary Pattern (STLBP) is a widely used dynamic texture descriptor, but it suffers from extremely high dimensionality. To tackle th
arXiv:2604.15377v1 Announce Type: cross Abstract: Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, pr
arXiv:2604.15363v1 Announce Type: cross Abstract: Motivated behaviour relies on the brain's capacity to evaluate effort and reward. Dysregulation within these processes contributes to a spectrum of co
arXiv:2604.15618v1 Announce Type: new Abstract: We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies
arXiv:2603.22201v2 Announce Type: replace Abstract: Humanoid robots require diverse motor skills to integrate into complex environments, but bridging the kinematic and dynamic embodiment gap from huma
arXiv:2604.15917v1 Announce Type: new Abstract: Instruction guided image editing has advanced substantially with recent generative models, yet it still fails to produce reliable results across many se
arXiv:2604.14334v2 Announce Type: replace-cross Abstract: Gradient saliency from deep sequence models surfaces candidate biomarkers efficiently, but the resulting gene lists can be contaminated by tis
arXiv:2604.15729v1 Announce Type: cross Abstract: Whole Slide Image (WSI) analysis is pivotal in computational pathology, enabling cancer diagnosis by integrating morphological and architectural cues
arXiv:2604.15360v1 Announce Type: new Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data
arXiv:2604.16175v1 Announce Type: new Abstract: Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. Wh
arXiv:2603.26062v2 Announce Type: replace Abstract: Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings
arXiv:2601.05201v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) are highly capable, yet often hallucinate by favoring textual prompts over visual evidence. We study this
arXiv:2604.16009v1 Announce Type: new Abstract: Metacognition, the ability to monitor and regulate one's own reasoning, remains under-evaluated in AI benchmarking. We introduce MEDLEY-BENCH, a benchma
arXiv:2604.15774v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks. Speci
arXiv:2601.03633v2 Announce Type: replace-cross Abstract: Accurate and high-resolution precipitation nowcasting from radar echo sequences is crucial for disaster mitigation and economic planning, yet
arXiv:2604.14518v2 Announce Type: replace Abstract: We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter m
arXiv:2604.16054v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and
arXiv:2512.03053v2 Announce Type: replace-cross Abstract: We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain
arXiv:2511.13131v2 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for automating complex reasoning and decision-making tasks. In telecommunications, they
arXiv:2510.09065v2 Announce Type: replace-cross Abstract: We introduce MMAudioSep, a generative model for video/text-queried sound separation that is founded on a pretrained video-to-audio model. By l
arXiv:2604.15979v1 Announce Type: new Abstract: Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existi
arXiv:2604.15314v1 Announce Type: cross Abstract: This research aimed to develop an intelligent system to evaluate performance and extract behavioral models for children with ASD and neurotypical (TD)
arXiv:2507.20058v3 Announce Type: replace-cross Abstract: Predicting Parkinson's Disease (PD) progression is crucial for personalized treatment, and voice biomarkers offer a promising non-invasive met
arXiv:2604.15833v1 Announce Type: new Abstract: Spatiotemporal modeling has evolved beyond simple time series analysis to become fundamental in structural time series analysis. While current research
arXiv:2604.16135v1 Announce Type: new Abstract: Recent advances in generative motion synthesis have enabled the production of realistic human motions from diverse input modalities. However, synthesizi
arXiv:2604.15341v1 Announce Type: cross Abstract: Mixed Reality (MR) offers immersive and multimodal opportunities for education but remains difficult for teachers to author without technical expertis
arXiv:2511.10262v3 Announce Type: replace-cross Abstract: Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experienc
arXiv:2604.15959v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing expensive black-box functions with multiple objectives. Howe
arXiv:2604.15757v1 Announce Type: new Abstract: This research note identifies a previously overlooked distinction between multi-objective reinforcement learning (MORL), and more conventional single-ob
arXiv:2509.17292v3 Announce Type: replace-cross Abstract: Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual
arXiv:2604.15929v1 Announce Type: new Abstract: The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages, creating the experi
arXiv:2604.15554v1 Announce Type: cross Abstract: We consider the problem of approximating a function by an element of a nonlinear manifold which admits a differentiable parametrization, typical examp
arXiv:2604.15325v1 Announce Type: cross Abstract: This paper presents the design of NEFFY 2.0, a social robot designed as a haptic slow-paced breathing companion for stress reduction, and reports find
arXiv:2604.15694v1 Announce Type: new Abstract: Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet exi
arXiv:2604.15941v1 Announce Type: new Abstract: Recent years have witnessed the rapid emergence of 3D Gaussian splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. I
arXiv:2604.16170v1 Announce Type: new Abstract: We introduce neuralCAD-Edit, the first benchmark for editing 3D CAD models collected from expert CAD engineers. Instead of text conditioning as in prior
arXiv:2604.16232v1 Announce Type: cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and
arXiv:2604.15718v1 Announce Type: cross Abstract: Visual speaker recognition based on lip motion offers a silent, hands-free, and behavior-driven biometric solution that remains effective even when ac
arXiv:2604.15475v1 Announce Type: new Abstract: Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack
arXiv:2604.15714v1 Announce Type: cross Abstract: Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work se
arXiv:2604.16021v1 Announce Type: cross Abstract: Code localization is a cornerstone of autonomous software engineering. Recent advancements have achieved impressive performance on real-world issue be
arXiv:2604.15668v1 Announce Type: new Abstract: Graph anomaly detection aims to identify irregular patterns in graph-structured data. Most unsupervised GNN-based methods rely on the homophily assumpti
arXiv:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T
arXiv:2510.21783v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated powerful performance in generating high-quality images. A typical example is text-to-image generator like S
arXiv:2509.21623v2 Announce Type: replace-cross Abstract: The expanding long-context capabilities of large language models are constrained by a significant memory bottleneck: the key-value (KV) cache
arXiv:2604.03444v3 Announce Type: replace-cross Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid m
arXiv:2510.20616v2 Announce Type: replace Abstract: Differentially private (DP) transfer learning, i.e., fine-tuning a pretrained model on private data, is the current state-of-the-art approach for tr
arXiv:2604.16146v1 Announce Type: new Abstract: Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned)
arXiv:2604.15455v1 Announce Type: new Abstract: Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to
arXiv:2604.15439v1 Announce Type: cross Abstract: We study dynamic measure transport for generative modelling in the setting of a stochastic process X_ullet whose marginals interpolate between a sourc
arXiv:2509.19104v2 Announce Type: replace Abstract: Reinforcement Learning with Human Feedback (RLHF) has become crucial for aligning Large Language Models (LLMs) with human intent. However, existing
arXiv:2412.04497v5 Announce Type: replace-cross Abstract: Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite the
arXiv:2603.00663v4 Announce Type: replace Abstract: The Moving Target Vehicle Routing Problem (MT-VRP) seeks trajectories for several agents that intercept a set of moving targets, subject to speed, t
arXiv:2604.16235v1 Announce Type: new Abstract: This paper presents a systematic benchmark of state-of-the-art multilingual large language models (LLMs) adapted via token pruning - a compression techn
arXiv:2604.15549v1 Announce Type: new Abstract: We consider the problem of minimizing the convergence time for decentralized federated learning (DFL) in wireless networks under broadcast communication
arXiv:2603.11698v2 Announce Type: replace-cross Abstract: Text-to-video (T2V) generation models have made rapid progress in producing visually high-quality and temporally coherent videos. However, exi