Knowledge catalogue
arXiv:2604.26454v1 Announce Type: new Abstract: Monocular depth estimation (MDE) is a fundamental yet inherently ill-posed task. Recent vision foundation models (VFMs), particularly DINO-based transfo
arXiv:2603.06752v2 Announce Type: replace Abstract: The ensemble Kalman filter (EnKF) is widely used for data assimilation in high-dimensional systems, but its performance often deteriorates for stron
arXiv:2604.26235v1 Announce Type: cross Abstract: We introduce LATTICE, a benchmark for evaluating the decision support utility of crypto agents in realistic user-facing scenarios. Prior crypto agent
arXiv:2604.26444v1 Announce Type: new Abstract: We prove that any continuous function f from [0,1]^n to R representable by a finite computation tree with N internal nodes and compositional sparsity s
arXiv:2604.25985v1 Announce Type: cross Abstract: General-relativistic magnetohydrodynamic (GR-MHD) simulations are essential for studying black hole accretion, relativistic jets, and magnetic reconne
arXiv:2604.26932v1 Announce Type: cross Abstract: The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depen
arXiv:2604.26740v1 Announce Type: new Abstract: Accurate BRDF acquisition is important for realistic rendering, but dense gonioreflectometer measurements are slow and expensive. We study how to select
arXiv:2409.00557v4 Announce Type: replace-cross Abstract: Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of task
arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agen
arXiv:2604.26566v1 Announce Type: cross Abstract: Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumpti
arXiv:2603.01999v2 Announce Type: replace-cross Abstract: Reliable obstacle avoidance in industrial settings demands 3D scene understanding, but widely used 2D LiDAR sensors perceive only a single hor
arXiv:2604.26182v1 Announce Type: cross Abstract: World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are hi
arXiv:2604.26201v1 Announce Type: new Abstract: Reliable backup localization for unmanned aerial vehicles (UAVs) operating in GNSS-denied nighttime conditions remains an open challenge due to the seve
arXiv:2604.25980v1 Announce Type: cross Abstract: In the context of quantum secure scenarios, existing research on mobile edge devices and intelligent computing and edge (ICE) systems based on the Non
arXiv:2603.06198v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving docum
arXiv:2604.26569v1 Announce Type: new Abstract: Deploying a neuro-symbolic task planner on a new domain today requires significant manual effort: a domain expert must author relaxation and complementa
arXiv:2604.25934v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) as interactive agents has exposed a category of behavioral failure that prevailing terminology, princip
arXiv:2604.25929v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to generate pictures, texts, music, videos, and other works that have traditionally required human cr
arXiv:2507.01449v3 Announce Type: replace Abstract: Speculative decoding (SD), where a small draft model is employed to propose draft tokens in advance and then the target model validates them in para
arXiv:2505.21190v2 Announce Type: replace-cross Abstract: Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation
arXiv:2604.26516v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavi
arXiv:2504.18662v3 Announce Type: replace-cross Abstract: Temporal action segmentation (TAS) has long been a key area of research in both robotics and computer vision. In robotics, algorithms have pri
arXiv:2601.11923v2 Announce Type: replace Abstract: The integration of complementary medicine into oncology represents a paradigm shift that has seen to increasing adoption of Traditional Chinese Medi
arXiv:2604.26591v1 Announce Type: cross Abstract: Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimizati
arXiv:2507.01544v2 Announce Type: replace Abstract: Predictive applications of machine learning often rely on small (sub 1 Bn parameter) specialized models tuned to particular domains or modalities. S
arXiv:2604.25926v1 Announce Type: new Abstract: The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and b
arXiv:2604.26283v1 Announce Type: cross Abstract: High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic memory experts instantly invoke during
arXiv:2604.26774v1 Announce Type: cross Abstract: Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories. Recent methods
arXiv:2604.26799v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical
arXiv:2604.26390v1 Announce Type: cross Abstract: Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades
arXiv:2604.26244v1 Announce Type: cross Abstract: We study generative super-resolution (SR) in real-world scenarios where content and degradations vary across domains, genres, and segments. For exampl
arXiv:2604.25978v1 Announce Type: cross Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying g
arXiv:2604.25968v1 Announce Type: cross Abstract: Viruses represent the most abundant biological entities on Earth and play a pivotal role in microbial ecosystems, yet, as prominent human pathogens, t
arXiv:2506.02494v2 Announce Type: replace-cross Abstract: Evaluation is important for multimodal generation tasks, while traditional multimodal evaluation metrics suffer from several limitations. With
arXiv:2604.26138v1 Announce Type: new Abstract: Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestri
arXiv:2604.26143v1 Announce Type: cross Abstract: First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational
arXiv:2604.26097v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have emerged as a versatile and efficient option for modeling the dynamic behavior of deformable materials. While GNNs ge
arXiv:2604.26866v1 Announce Type: new Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of
arXiv:2604.26321v1 Announce Type: new Abstract: Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimen
arXiv:2604.26517v1 Announce Type: new Abstract: Accurate quantification of the geometry of curvilinear biological structures is essential for understanding cellular mechanics and disease-related morph
arXiv:2604.25974v1 Announce Type: new Abstract: This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but c
arXiv:2604.26251v1 Announce Type: cross Abstract: We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the h
arXiv:2604.26568v1 Announce Type: new Abstract: Speech Sound Disorders (SSD) affect roughly five percent of children, yet speech-language pathologists face severe staffing shortages and unmanageable c
arXiv:2604.26888v1 Announce Type: new Abstract: This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework,
arXiv:2604.26261v1 Announce Type: new Abstract: Zero-shot 3D Visual Grounding (3DVG) is a critical capability for open-world embodied AI. However, existing methods are fundamentally bottlenecked by th
arXiv:2604.26456v1 Announce Type: cross Abstract: The digitisation of classical Sanskrit literature is impeded by a scarcity of annotated resources, particularly for Named Entity Recognition. While re
arXiv:2604.26446v1 Announce Type: new Abstract: We study three problems that involve identifying homogeneous halfspaces under Gaussian distributions: agnostic learning, one-sided reliable learning, an
arXiv:2509.25236v3 Announce Type: replace Abstract: A core challenge in causal artificial intelligence is the principled coordination of multiple, imperfect, and subjective causal perspectives arising
arXiv:2404.09688v2 Announce Type: replace Abstract: Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination ma
arXiv:2604.26080v1 Announce Type: cross Abstract: Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatil
arXiv:2602.03169v2 Announce Type: replace-cross Abstract: Clustering functional data in the presence of phase variation is challenging, as temporal misalignment can obscure intrinsic shape differences
arXiv:2604.26297v1 Announce Type: new Abstract: Optimization algorithms are fundamental to modern deep learning, yet most widely used methods rely on update rules based primarily on local gradient sta
arXiv:2604.04135v2 Announce Type: replace Abstract: This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and
arXiv:2603.07529v2 Announce Type: replace Abstract: Concept erasure aims to remove unwanted attributes, such as social or demographic factors, from learned representations, while preserving their task
arXiv:2604.26070v1 Announce Type: new Abstract: Causal inference in continuous-time sequential decision problems is challenged by hidden confounders. We show that, in latent state-space models with ti
arXiv:2604.25984v1 Announce Type: cross Abstract: The marginal likelihood, also known as the evidence, is regarded as a mathematical embodiment of Occam's razor, enabling model selection that avoids o
arXiv:2604.26622v1 Announce Type: new Abstract: Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended h
arXiv:2604.26211v1 Announce Type: new Abstract: In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that
arXiv:2601.02731v3 Announce Type: replace-cross Abstract: Training a unified model integrating video-to-audio (V2A), text-to-audio (T2A), and joint video-text-to-audio (VT2A) generation offers signifi
arXiv:2604.26252v1 Announce Type: new Abstract: Predicting social media popularity requires understanding both the intrinsic appeal of content and the external context that determines how it is expose