Knowledge catalogue
arXiv:2604.08602v1 Announce Type: cross Abstract: Background: Server-based screening tools impose subscription costs, while open-source alternatives require coding skills. Objectives: We developed a b
arXiv:2602.03342v2 Announce Type: replace-cross Abstract: Text-conditioned diffusion models have advanced image and video super-resolution by using prompts as semantic priors, and modern super-resolut
arXiv:2604.09220v1 Announce Type: new Abstract: Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruct
arXiv:2604.09439v1 Announce Type: cross Abstract: In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanatio
arXiv:2603.01400v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pru
arXiv:2602.16821v2 Announce Type: replace Abstract: We propose TopoFlow (Topography-aware pollutant Flow learning), a physics-guided neural network for efficient, high-resolution air quality predictio
arXiv:2604.09057v1 Announce Type: new Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible moti
arXiv:2604.08945v1 Announce Type: new Abstract: Accurate object geometry estimation is essential for many downstream tasks, including robotic manipulation and physical interaction. Although vision is
arXiv:2604.08780v1 Announce Type: cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient planning a
arXiv:2604.09519v1 Announce Type: new Abstract: World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertai
arXiv:2603.27817v3 Announce Type: replace-cross Abstract: Street-level imagery contains personally identifiable information (PII), some of which is context-dependent. Existing anonymization methods ei
arXiv:2604.07720v2 Announce Type: replace Abstract: Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive r
arXiv:2604.09038v1 Announce Type: cross Abstract: Robust geo-localization in changing environmental conditions is critical for long-term aerial autonomy. While visual place recognition (VPR) models pe
arXiv:2604.09008v1 Announce Type: cross Abstract: The widespread use of English as a Second or Foreign Language (ESFL) has sparked a paradigm shift: ESFL is not seen merely as a deviation from standar
arXiv:2604.08815v1 Announce Type: new Abstract: Medical vision-language models (VLMs) show strong performance on radiology tasks but often produce fluent yet weakly grounded conclusions due to over-re
arXiv:2604.08800v1 Announce Type: cross Abstract: Stepping-stone intrusions (SSIs) are a prevalent network evasion technique in which attackers route sessions through chains of compromised intermediat
arXiv:2603.08146v3 Announce Type: replace Abstract: Existing frameworks for gradient-based training of spiking neural networks face a trade-off: discrete-time methods using surrogate gradients support
arXiv:2604.09227v1 Announce Type: cross Abstract: Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to pro
arXiv:2510.00491v3 Announce Type: replace-cross Abstract: Learning diverse manipulation skills for real-world robots is severely bottlenecked by the reliance on costly and hard-to-scale teleoperated d
arXiv:2604.09320v1 Announce Type: cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond indi
arXiv:2604.02183v2 Announce Type: replace Abstract: Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data di
arXiv:2604.09159v1 Announce Type: new Abstract: Maximum entropy reinforcement learning (MaxEnt RL) has become a standard framework for sequential decision making, yet its standard Gaussian policy para
arXiv:2604.07248v2 Announce Type: replace-cross Abstract: Developing optical systems for free-space applications requires simulation tools that accurately capture turbulence-induced wavefront distorti
arXiv:2604.09041v1 Announce Type: cross Abstract: AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and
arXiv:2604.02241v2 Announce Type: replace Abstract: Embodied visual tracking is crucial for Unmanned Aerial Vehicles (UAVs) executing complex real-world tasks. In dynamic urban scenarios with complex
arXiv:2604.09321v1 Announce Type: cross Abstract: Considering efficiency, ultra-high-definition (UHD) low-light image restoration is extremely challenging. Existing methods based on Transformer archit
arXiv:2604.09442v1 Announce Type: new Abstract: UI-to-Code generation requires vision-language models (VLMs) to produce thousands of tokens of structured HTML/CSS from a single screenshot, making visu
arXiv:2604.08550v1 Announce Type: cross Abstract: Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactio
arXiv:2604.08885v1 Announce Type: new Abstract: Transformers have had a profound impact on the field of artificial intelligence, especially on large language models and their variants. However, as was
arXiv:2604.08560v1 Announce Type: cross Abstract: Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open-set text cla
arXiv:2604.08701v1 Announce Type: new Abstract: We introduce Unified Multimodal Uncertain Inference (UMUI), a multimodal inference task spanning text, audio, and video, where models must produce calib
arXiv:2604.09169v1 Announce Type: new Abstract: Semi-supervised semantic segmentation in computational pathology remains challenging due to scarce pixel-level annotations and unreliable pseudo-label s
arXiv:2504.12758v3 Announce Type: replace-cross Abstract: In this paper, we show that an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) wireless system with appropriate analog combining co
arXiv:2510.03548v3 Announce Type: replace-cross Abstract: AI-based talking-head videoconferencing systems reduce bandwidth by sending a compact pose-expression latent and re-synthesizing RGB at the re
arXiv:2604.08902v1 Announce Type: new Abstract: Background: Limited data utilization in low-resource settings poses a barrier to the vaccine delivery ecosystem, undermining efforts to achieve equitabl
arXiv:2604.09036v1 Announce Type: new Abstract: Scaling Vision-Language-Action (VLA) models requires massive datasets that are both semantically coherent and physically feasible. However, existing sce
arXiv:2604.09330v1 Announce Type: cross Abstract: Recent advances in robot foundation models trained on large-scale human teleoperation data have enabled robots to perform increasingly complex real-wo
arXiv:2604.09305v1 Announce Type: new Abstract: Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance
arXiv:2604.09374v1 Announce Type: cross Abstract: We propose a Hybrid Quantum-Classical Physics-Informed Neural Network (HQC-PINN) that integrates parameterized variational quantum circuits into the P
arXiv:2604.03058v2 Announce Type: replace-cross Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like 'am I in the wrong?' rather than providing genuine assessment.
arXiv:2604.08549v1 Announce Type: cross Abstract: We introduce VerifAI, an open-source expert system for biomedical question answering that integrates retrieval-augmented generation (RAG) with a novel
arXiv:2604.08613v1 Announce Type: new Abstract: In this report, we present our champion solution for the NTIRE 2026 Challenge on Video Saliency Prediction held in conjunction with CVPR 2026. To exploi
arXiv:2604.09197v1 Announce Type: cross Abstract: Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed a
arXiv:2604.09531v1 Announce Type: cross Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contr
arXiv:2508.13792v2 Announce Type: replace Abstract: The intrinsic dynamics of an object governs its physical behavior in the real world, playing a critical role in enabling physically plausible intera
arXiv:2604.09508v1 Announce Type: cross Abstract: Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex qu
arXiv:2604.09349v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the
arXiv:2604.09529v1 Announce Type: cross Abstract: Large Vision Language Models (LVLMs) achieve strong multimodal reasoning but frequently exhibit hallucinations and incorrect responses with high certa
arXiv:2604.08639v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep learning models in safety critical applications, yet no consensus exists on which UQ m
arXiv:2508.06869v4 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) demonstrate exceptional performance in vision-language tasks, yet their processing of long videos is
arXiv:2604.08558v1 Announce Type: cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically
arXiv:2604.09048v1 Announce Type: cross Abstract: While the large energy consumption of Large Language Models (LLMs) is recognized by the community, system operators lack guidance for energy-efficient
arXiv:2604.08763v1 Announce Type: cross Abstract: We extend the Weak Adversarial Neural Pushforward Method to the Wigner transport equation governing the phase-space dynamics of quantum systems. The c
arXiv:2510.06499v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a tra
arXiv:2604.08716v1 Announce Type: new Abstract: Virtual Try-On (VTON) has seen rapid advancements, providing a strong foundation for generative fashion tasks. However, the inverse problem, Virtual Try
arXiv:2602.17667v2 Announce Type: replace-cross Abstract: In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, tradit
arXiv:2510.07517v5 Announce Type: replace Abstract: Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their o
arXiv:2602.22918v2 Announce Type: replace Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language proc
arXiv:2512.12641v2 Announce Type: replace Abstract: The Unigram tokenization algorithm offers a probabilistic alternative to the greedy heuristics of Byte-Pair Encoding. Despite its theoretical elegan
arXiv:2603.03099v5 Announce Type: replace-cross Abstract: Despite Adam demonstrating faster empirical convergence than SGD in many applications, much of the existing theory yields guarantees essential