Knowledge catalogue
arXiv:2605.10307v1 Announce Type: new Abstract: Dynamic scene reconstruction represents a fundamental yet demanding challenge in computer vision and robotics. While recent progress in 3DGS-based metho
arXiv:2512.02066v2 Announce Type: replace-cross Abstract: Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data d
arXiv:2605.08632v1 Announce Type: cross Abstract: Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verifi
arXiv:2605.09303v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a structural alternative to autoregressive generation: denoising can update tokens in arbitrary orders or in para
arXiv:2605.10791v1 Announce Type: new Abstract: Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent KGQA methods mainly follow the
Pay attention to this one if you build research or knowledge-work agents. Most research-agent systems produce uniform outputs regardless of who is driving them. This new work, NanoResearch, argues tha
arXiv:2605.09636v1 Announce Type: new Abstract: PDE-to-solver code generation aims to automatically synthesize executable numerical solvers from partial differential equation (PDE) specifications. Thi
arXiv:2605.10136v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) train a single neural approximation by minimizing multiple physics- and data-derived losses, but the gradients
arXiv:2605.09422v1 Announce Type: new Abstract: Although Large Multimodal Models (LMMs) have achieved strong performance on general video understanding, their susceptibility to textual prior shortcuts
arXiv:2605.09339v1 Announce Type: cross Abstract: Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured
arXiv:2605.08121v1 Announce Type: cross Abstract: Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Rec
arXiv:2605.09503v1 Announce Type: new Abstract: Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challengin
arXiv:2605.09319v1 Announce Type: new Abstract: With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigatin
arXiv:2605.09043v1 Announce Type: cross Abstract: One partner says 'Fine' meaning resolution; the other hears surrender. The word is shared; the affective uptake is not. We formalize this as affective
arXiv:2605.09552v1 Announce Type: cross Abstract: Recently, Muon and related spectral optimizers have demonstrated strong empirical performance as scalable stochastic methods, often outperforming Adam
arXiv:2604.01532v2 Announce Type: replace Abstract: LLM agents are beginning to invoke industrial asset-management tools through the Model Context Protocol (MCP), yet whether they can act reliably on
arXiv:2605.10391v1 Announce Type: cross Abstract: We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Sing
arXiv:2605.10806v1 Announce Type: cross Abstract: Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern re
arXiv:2605.09538v1 Announce Type: cross Abstract: While existing methods for reconstructing hand-object interactions have made impressive progress, they either focus on rigid or part-wise rigid object
arXiv:2605.10429v1 Announce Type: cross Abstract: Nuclear magnetic resonance (NMR) spectroscopy provides an experimental readout of local chemical environments, but its use in molecular representation
arXiv:2605.08915v1 Announce Type: new Abstract: Deep learning paradigms, such as PINNs and neural operators, have significantly advanced the solving of PDEs. However, they often struggle to capture th
arXiv:2605.08176v1 Announce Type: new Abstract: We introduce Dynamical Physics-Modeled Neural Networks (DynPMNNs), a continuous-time deep learning architecture in which each hidden layer is defined as
arXiv:2605.10046v1 Announce Type: new Abstract: Precipitation nowcasting aims to forecast short-term radar echo sequences for extreme weather warning, where both prediction fidelity and inference effi
arXiv:2605.10118v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hi
arXiv:2605.09638v1 Announce Type: new Abstract: Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world
arXiv:2605.10032v1 Announce Type: new Abstract: Cell-type-specific marker genes are fundamental to plant biology, yet existing resources primarily rely on curated databases or high-throughput studies
arXiv:2605.10084v1 Announce Type: cross Abstract: The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space.
arXiv:2605.09434v1 Announce Type: cross Abstract: Low-cost air quality sensors are becoming ubiquitous in our daily lives as public awareness of air pollution continues to grow, and people take measur
arXiv:2605.10349v1 Announce Type: cross Abstract: Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy
arXiv:2605.09365v1 Announce Type: new Abstract: Enterprise workloads are dominated by deterministic, structured, and knowledge-dependent tasks operating under strict cost, latency, and reliability con
arXiv:2605.09472v1 Announce Type: new Abstract: Positional encoding in transformers is commonly implemented through positional embeddings, attention masks, or bias terms, but formal connections betwee
arXiv:2605.08574v1 Announce Type: new Abstract: As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite s
arXiv:2605.10529v1 Announce Type: new Abstract: Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent
arXiv:2605.10925v1 Announce Type: new Abstract: Large-scale pretraining has made Vision-Language-Action (VLA) models promising foundations for generalist robot manipulation, yet adapting them to downs
arXiv:2605.08651v1 Announce Type: cross Abstract: Video anomaly detection (VAD) systems often prioritize accuracy while overlooking privacy concerns, limiting their suitability for real-world deployme
arXiv:2605.08152v1 Announce Type: cross Abstract: The intersection of Artificial Intelligence (AI) and distributed systems has given rise to Federated Learning (FL), a paradigm that enables decentrali
arXiv:2605.08858v1 Announce Type: new Abstract: Ante-hoc interpretability methods based on prototypes provide highly accurate explanations by utilizing the intuitive 'this looks like that' reasoning p
arXiv:2605.10409v1 Announce Type: new Abstract: Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or
arXiv:2605.10189v1 Announce Type: cross Abstract: Designing proteins with desired functions or properties represents a core goal in synthetic biology and drug discovery. Recent advances in protein lan
arXiv:2605.10748v1 Announce Type: cross Abstract: One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. Howeve
arXiv:2605.09931v1 Announce Type: cross Abstract: Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code in
arXiv:2605.09893v1 Announce Type: cross Abstract: Large language models (LLMs) are often evaluated based on their stated values, yet these do not reliably translate into their actions, a discrepancy t
arXiv:2605.10458v1 Announce Type: new Abstract: Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property predictio
arXiv:2605.08170v1 Announce Type: new Abstract: Neural operators have emerged as a powerful tool for learning mappings between infinite-dimensional function spaces. However, their approximation proper
arXiv:2605.09691v1 Announce Type: new Abstract: Population pharmacokinetic/pharmacodynamic (PK/PD) modeling traditionally relies on classical ordinary differential equations to simulate drug dynamics.
arXiv:2605.09513v1 Announce Type: new Abstract: Tracking points in videos is typically formulated as frame-to-frame correspondence, where each point is matched locally to the next frame. While this wo
arXiv:2605.10730v1 Announce Type: new Abstract: We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a si
arXiv:2503.17182v4 Announce Type: replace Abstract: We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictio
arXiv:2605.09907v1 Announce Type: new Abstract: Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, includi
arXiv:2512.08984v2 Announce Type: replace-cross Abstract: Human Activity Recognition (HAR) underpins applications in healthcare, rehabilitation, fitness tracking, and smart environments, yet existing
arXiv:2605.10789v1 Announce Type: new Abstract: Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. Howeve
arXiv:2605.09457v1 Announce Type: cross Abstract: While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood i
arXiv:2605.08713v1 Announce Type: cross Abstract: In recent years, autonomous parking has made significant advances, yet parking tasks still face challenges in extreme scenarios such as mechanical and
arXiv:2605.08625v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LL
arXiv:2605.08776v1 Announce Type: new Abstract: Reasoning-centric large language models (LLMs) achieve strong performance by generating intermediate reasoning trajectories, but often incur excessive t
arXiv:2605.08142v1 Announce Type: cross Abstract: Reasoning in large language models is predominantly evaluated through labeled benchmarks, conflating task performance with the quality of internal inf
arXiv:2605.08315v1 Announce Type: new Abstract: Existing LLM-based policy optimizers see only scalar rewards: that a policy scored 0.45, but not whether the agent got stuck in a loop, fell into a hole
arXiv:2605.10385v1 Announce Type: cross Abstract: Guided-diffusion black-box optimization (BO) has shown strong empirical performance on structured design problems such as molecules and crystals, but
arXiv:2605.08098v1 Announce Type: new Abstract: Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult b
arXiv:2605.08378v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for improving the capability of intelligent systems, but its practical deployment faces two cent