Knowledge catalogue
arXiv:2608.06170v1 Announce Type: cross Abstract: Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extract
arXiv:2608.06015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch,
arXiv:2608.05230v1 Announce Type: cross Abstract: The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can
arXiv:2608.06340v1 Announce Type: cross Abstract: Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, th
arXiv:2608.05439v1 Announce Type: new Abstract: Translating natural language instructions into machine-interpretable formal specifications enables robots and autonomous systems to plan, reason, and fo
arXiv:2608.05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction. Yet such loc
arXiv:2608.05879v1 Announce Type: new Abstract: Text-driven 3D generation has advanced rapidly in creating large-scale outdoor environments and detailed indoor scenes, but these domains are usually sy
arXiv:2608.06021v1 Announce Type: new Abstract: Effective Visual Localization (VL) requires a map of the environment that combines compactness for efficient scalability with robustness against visual
arXiv:2608.05803v1 Announce Type: new Abstract: Recent advances in generative video modeling have enabled diverse generation, reference-based synthesis, extension, and editing, but existing approaches
arXiv:2608.05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into
arXiv:2608.04872v1 Announce Type: cross Abstract: Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compr
arXiv:2608.05102v1 Announce Type: new Abstract: Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. Ho
arXiv:2608.04651v1 Announce Type: new Abstract: Advanced materials discovery increasingly relies on machine learning and Bayesian optimization to explore large discrete design spaces under limited eva
arXiv:2608.04016v1 Announce Type: cross Abstract: Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We devel
arXiv:2608.04667v1 Announce Type: cross Abstract: Selective inference (SI) provides statistically valid p-values for hypotheses selected by applying an algorithm to the data, correcting for the bias t
arXiv:2608.04292v1 Announce Type: new Abstract: The proliferation of agentic artificial intelligence (AI) systems has raised serious questions about the accountability for tasks performed by AI agents
arXiv:2608.04013v1 Announce Type: cross Abstract: Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs. However, real-world da
arXiv:2608.04302v1 Announce Type: new Abstract: Benchmarking video-language models has largely focused on short clips and single-sentence metrics, leaving open whether current systems can generate acc
arXiv:2608.04333v1 Announce Type: new Abstract: Large language model (LLM) configuration evaluation is challenging due to limited evaluation budgets, varying costs, and multiple competing objectives.
arXiv:2601.20985v2 Announce Type: replace Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory s
arXiv:2608.04677v1 Announce Type: new Abstract: Algorithmic recourse seeks to help individuals reverse unfavorable automated decisions by recommending actionable changes that achieve a desired outcome
arXiv:2509.24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal d
arXiv:2608.04286v1 Announce Type: new Abstract: Large language models (LLMs) are often used in conjunction with external knowledge sources to improve their factual accuracy and decrease hallucinations
arXiv:2508.00285v2 Announce Type: replace Abstract: Objective: Large Language Models (LLMs) have demonstrated strong capabilities in medical text understanding and generation. However, their trustwort
arXiv:2608.04339v1 Announce Type: cross Abstract: Large language models are increasingly used for information seeking, yet semantically equivalent questions phrased in different ways can receive answe
arXiv:2602.19161v2 Announce Type: replace Abstract: Latent diffusion models have enabled high-quality video synthesis, yet their inference remains costly and time-consuming. As diffusion transformers
arXiv:2503.06396v2 Announce Type: replace Abstract: The minimum vertex cover (MVC) problem seeks to identify the smallest set of vertices that cover all edges in an undirected graph. As a fundamental
arXiv:2608.04240v1 Announce Type: cross Abstract: Superhuman game engines in domains like chess have made expert-level evaluations easily accessible, yet they communicate what is true without the natu
arXiv:2505.13099v3 Announce Type: replace Abstract: Vision Foundation Models (VFMs) have made remarkable progress and are increasingly being applied to segmentation tasks in real-world industrial sett
arXiv:2608.04121v1 Announce Type: cross Abstract: Vision-based guidance of unmanned aerial vehicles (UAVs) toward unmanned ground vehicles (UGVs) supports cooperative aerial--ground robotics, but reli
In July, NVIDIA joined more than 200 companies and organizations in signing “Open Weights and American AI Leadership,” an open letter arguing that AI leadership will be measured not by any single fron
arXiv:2608.04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult. While condensing information seems straightforward, real-world enterprise summarization of support ticket
arXiv:2603.00828v2 Announce Type: replace Abstract: In recent years, various methods have been proposed for mesh analysis, each offering distinct advantages and often excelling on different object cla
arXiv:2506.02260v4 Announce Type: replace-cross Abstract: Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental c
arXiv:2608.04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimod
arXiv:2608.04776v1 Announce Type: new Abstract: The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems. While existing research ha
arXiv:2607.17607v2 Announce Type: replace Abstract: We study whether stochastic nonconvex optimization can be reduced to ordinary static regret minimization in online convex optimization in a black-bo
arXiv:2608.04818v1 Announce Type: new Abstract: Modern diffusion and flow-based models are increasingly moving toward few-step, latent-free generation to bypass the computational overhead of multi-ste
arXiv:2608.04442v1 Announce Type: cross Abstract: Robustness to natural corruptions remains a fundamental challenge for deep neural networks. In this paper, we identify a robustness fading phenomenon
arXiv:2608.04060v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn abstract states by predicting target embeddings from context embeddings, but their transition models ar
arXiv:2608.04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both sp
arXiv:2608.05112v1 Announce Type: cross Abstract: The Subspace Constrained Mean Shift (SCMS) algorithm is a popular nonparametric method for extracting density ridges, which serve as a low-dimensional
arXiv:2608.04606v1 Announce Type: new Abstract: Understanding complex surgical scenes requires recognizing multiple interdependent entities, such as instruments, actions, and targets, while maintainin
arXiv:2607.19322v2 Announce Type: replace Abstract: Rubric-based evaluation of open-ended generation faces a fundamental tension between expressiveness and reliability. Authoring a faithful rubric req
arXiv:2608.04055v1 Announce Type: cross Abstract: The minimum change principle provides an information-theoretic characterization of the Bayes reversal channel in classical probability theory and has
arXiv:2608.04052v1 Announce Type: cross Abstract: Backdoor attacks in multimodal contrastive learning (MCL) have garnered growing attention in recent years, as many downstream tasks critically depend
arXiv:2608.04393v1 Announce Type: new Abstract: Scientific machine learning often relies on proxy targets computed from known domain factors when direct observations are limited. When those same facto
arXiv:2608.03002v1 Announce Type: new Abstract: This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact
arXiv:2608.03677v1 Announce Type: new Abstract: Supportive continuum robots (SCRs) enhance the load-bearing capability of an operative continuum robot by mechanically coupling it with a supportive arm
arXiv:2608.02868v1 Announce Type: new Abstract: Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for e
arXiv:2608.03779v1 Announce Type: new Abstract: Video anomaly understanding (VAU) focuses on comprehensively interpreting abnormal events in videos, requiring models to identify anomalous occurrences,
arXiv:2608.03358v1 Announce Type: new Abstract: Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emot
arXiv:2502.11140v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through na
arXiv:2608.03086v1 Announce Type: cross Abstract: Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specifi
arXiv:2608.02618v1 Announce Type: new Abstract: Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized con
arXiv:2608.03198v1 Announce Type: new Abstract: Realistic handwritten text generation plays an important role in numerous applications, such as font design, biometric authentication, and robotic calli
arXiv:2608.02886v1 Announce Type: new Abstract: Safely navigating polytopic environments while respecting the dynamics, control, and exact geometry of the underlying system is a challenge in robotics.
arXiv:2608.03051v1 Announce Type: new Abstract: Model Predictive Control (MPC) delivers constraint-aware control, but its reliance on online optimization limits its use on systems with fast dynamics,
arXiv:2608.03655v1 Announce Type: cross Abstract: Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control. We propose a modular generation-and-
arXiv:2608.03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneo