Knowledge catalogue
arXiv:2406.09250v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically de
arXiv:2507.12455v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated con
arXiv:2605.23350v1 Announce Type: new Abstract: Autonomous exploration of multi-floor buildings remains challenging for ground robots because conventional 2D and 2.5D maps cannot represent overlapping
arXiv:2505.17015v2 Announce Type: replace-cross Abstract: Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single ima
arXiv:2602.20210v2 Announce Type: replace-cross Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks, including crystal structure prediction (CSP) and de novo ge
arXiv:2605.23482v1 Announce Type: cross Abstract: Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increas
arXiv:2511.14286v2 Announce Type: replace Abstract: In computer- and robot-assisted orthopedic surgery (CAOS), patient-specific surgical plans derived from preoperative imaging define target locations
arXiv:2602.13473v2 Announce Type: replace Abstract: Although foundation models have demonstrated remarkable success in general domains, the application of these models to electroencephalography (EEG)
Dropped this morning by the Vatican: Magnifica Humanitas of His Holiness Pope Leo XIV on Safeguarding the Human Person in the Time of Artificial Intelligence. This is a very interesting document. It's
arXiv:2511.11051v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) fusion enables the composition of subject and style representations for controllable generation without retraining. Howev
arXiv:2512.19199v2 Announce Type: replace-cross Abstract: The paper establishes generalization bounds for multitask deep neural networks using operator-theoretic techniques. The authors propose a tigh
arXiv:2510.16335v4 Announce Type: replace Abstract: This paper investigates the recently emerged problem of Language-assisted Image Clustering (LaIC), where textual semantics are leveraged to improve
arXiv:2512.15436v2 Announce Type: replace-cross Abstract: We introduce an extension of the partitioned local depth (PaLD) algorithm that is adapted to online applications such as semi-supervised predi
arXiv:2605.23037v1 Announce Type: new Abstract: Data-driven modeling is becoming central to multiphase transport, electronics cooling, acoustic diagnostics, and thermal-fluid digital twins, but progre
arXiv:2605.23712v1 Announce Type: cross Abstract: Reconstructing flow fields from sparse measurements is a fundamental problem in fluid mechanics with broad implications for modeling, control, and des
arXiv:2605.23726v1 Announce Type: new Abstract: We prove optimal sampling bounds achieving (1pmarepsilon)-relative error for a broad class of Lipschitz continuous classification loss functions under v
arXiv:2605.23219v1 Announce Type: cross Abstract: Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on determ
arXiv:2605.23883v1 Announce Type: cross Abstract: Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this wo
arXiv:2605.23478v1 Announce Type: cross Abstract: Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for
arXiv:2605.22856v1 Announce Type: cross Abstract: Channel foundation models assume access to fully observed channels, an assumption that fails in deployment. We introduce PilotWiMAE, a self-supervised
arXiv:2503.06684v3 Announce Type: replace Abstract: Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing
arXiv:2605.23531v1 Announce Type: new Abstract: Low-light images exhibit severe noise, contrast loss, and semantic ambiguity, making enhancement a joint problem of denoising and detail recovery. We pr
arXiv:2603.21437v2 Announce Type: replace Abstract: Transformer-based embedding models frequently exhibit geometric pathologies, such as anisotropy and length-induced representation collapse, which ca
arXiv:2605.23170v1 Announce Type: cross Abstract: Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not cont
arXiv:2605.04118v2 Announce Type: replace-cross Abstract: Recent advances in de novo protein binder design have enabled increasing experimental validation, yet reported in silico metrics remain diffic
arXiv:2605.23285v1 Announce Type: cross Abstract: How network structure determines function is a fundamental question, and it can be investigated by graph ensembles with precisely controlled structura
arXiv:2605.23747v1 Announce Type: new Abstract: Material segmentation, the pixel-wise classification of physical surface properties, remains a challenging problem in computer vision, requiring physico
arXiv:2605.22986v1 Announce Type: cross Abstract: Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects o
arXiv:2605.23175v1 Announce Type: cross Abstract: Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collecting input-o
arXiv:2605.23115v1 Announce Type: new Abstract: Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This pape
arXiv:2605.23406v1 Announce Type: new Abstract: End-to-end autonomous driving solutions, which directly process multimodal sensory data and output fine-grained control commands, have gradually become
arXiv:2403.12401v2 Announce Type: replace Abstract: Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks
arXiv:2605.05704v2 Announce Type: replace-cross Abstract: Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning and
arXiv:2605.23157v1 Announce Type: new Abstract: The attack surface of a multimodal large language model (MLLM) is language-dependent in ways that reveal the mechanistic structure of alignment failures
arXiv:2605.22997v1 Announce Type: new Abstract: In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks such as 3D object detection.
arXiv:2605.22878v1 Announce Type: new Abstract: The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmen
arXiv:2605.22894v1 Announce Type: cross Abstract: Controlling physics-based humanoids from natural-language instructions is a critical step toward general-purpose embodied agents. However, existing me
arXiv:2605.23753v1 Announce Type: new Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph expans
arXiv:2605.23378v1 Announce Type: cross Abstract: Ambulance response is time-critical in out-of-hospital cardiac arrest (OHCA), where dispatchers must balance timely arrivals with limited fleet capaci
arXiv:2605.23239v1 Announce Type: new Abstract: Defending Graph Neural Networks (GNNs) against adversarial attacks requires balancing accuracy and robustness, a trade-off often mishandled by tradition
arXiv:2605.23477v1 Announce Type: new Abstract: Diffusion-based policies have established a new standard for precise robotic manipulation but face a critical scalability bottleneck: high-performance m
arXiv:2605.23240v1 Announce Type: new Abstract: This paper investigates continuous-time motion planning under Signal Temporal Logic (STL) specifications. The goal is to generate smooth robot trajector
arXiv:2605.23898v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes an
arXiv:2605.23288v1 Announce Type: new Abstract: Recent Open-Vocabulary Action Recognition (OVAR) methods typically aggregate visual features into a global representation before computing text alignmen
arXiv:2605.23137v1 Announce Type: cross Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured vision--
arXiv:2605.23597v1 Announce Type: new Abstract: Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex env
arXiv:2412.19098v4 Announce Type: replace Abstract: Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding ta
arXiv:2602.01665v2 Announce Type: replace-cross Abstract: The design of environments plays a critical role in shaping the development and evaluation of cooperative multi-agent reinforcement learning (
arXiv:2502.07295v2 Announce Type: replace Abstract: Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properti
arXiv:2601.21500v2 Announce Type: replace Abstract: In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values,
arXiv:2601.21692v2 Announce Type: replace Abstract: Fine-Tuning-as-a-Service (FTaaS) facilitates the customization of Multimodal Large Language Models (MLLMs) but introduces critical backdoor risks vi
arXiv:2602.08404v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) have recently gained significant attention due to their inherent support for parallel decoding. Building on
arXiv:2605.23257v1 Announce Type: cross Abstract: Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Y
arXiv:2605.23464v1 Announce Type: new Abstract: We consider a decentralized setup in which the participants collaboratively train and serve a large neural network, and where each participant only proc
arXiv:2507.23372v2 Announce Type: replace Abstract: Emotional understanding and generation are often treated as separate tasks, yet they are inherently complementary and can mutually enhance each othe
arXiv:2503.12868v2 Announce Type: replace Abstract: Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However
arXiv:2408.03085v3 Announce Type: replace-cross Abstract: As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directl
arXiv:2507.05064v4 Announce Type: replace-cross Abstract: Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalabili
arXiv:2602.07399v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations r
arXiv:2602.12579v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning,