Knowledge catalogue
arXiv:2609.29261v1 Announce Type: new Abstract: Obstacle-aided locomotion is a fundamental capability for snake robots to traverse complex environments. However, conventional rigid-link snake robots o
arXiv:2609.29433v1 Announce Type: cross Abstract: Background: Artificial intelligence (AI)-based glaucoma detection from colour fundus photographs (CFP) offers scalable screening, but performance may
arXiv:2506.19886v3 Announce Type: replace-cross Abstract: Semantic communication enhances transmission efficiency by conveying semantic information rather than raw input symbol sequences. Task-oriente
arXiv:2609.30167v1 Announce Type: new Abstract: Audio language models pass speech and text through a single decoder. We ask whether that decoder represents a distinctive feature in the same direction
arXiv:2609.30151v1 Announce Type: cross Abstract: Asked to choose between candidates and explain the choice, a language model often rejects a rival by naming a fact its profile lacks: no director, no
arXiv:2609.28508v1 Announce Type: cross Abstract: Generative adversarial networks (GANs) are increasingly used to augment medical imaging datasets, but synthetic images do not necessarily provide down
arXiv:2609.29684v1 Announce Type: new Abstract: Despite the strengths of modern anonymization and de-identification techniques, the risk of re-identification remains significant due to the indirect id
arXiv:2609.30187v1 Announce Type: new Abstract: Ego-Exo4D is a large-scale dataset providing synchronized egocentric and multi-view exocentric video, a rich resource for skill learning and assessment,
arXiv:2609.29847v1 Announce Type: cross Abstract: The Brinkman penalisation method embeds boundary-value problems on complex domains into a simple computational box by modeling the solid region as a s
arXiv:2609.29056v1 Announce Type: cross Abstract: Emotion dynamics are critical for understanding crisis-support conversations, yet most computational work treats emotion as static utterance-level lab
arXiv:2509.22738v3 Announce Type: replace Abstract: In autoregressive language models, each token is sampled by conditioning on all the past tokens; the overall string has thus been sampled from the c
arXiv:2609.29945v1 Announce Type: new Abstract: Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compe
arXiv:2609.28749v1 Announce Type: new Abstract: Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and e
arXiv:2609.29496v1 Announce Type: new Abstract: Natural language explanation generation serves as a key mechanism for exposing and evaluating vision-language reasoning. Prior work on explanation-drive
arXiv:2609.29613v1 Announce Type: cross Abstract: We present a modular and multi-level framework for the differential diagnosis of malignant melanoma. Our framework integrates contextual information a
arXiv:2609.29029v1 Announce Type: new Abstract: Onboard vision-language models could enable satellites to answer queries directly, but exhaustive tiled inference over high-resolution imagery is slow a
arXiv:2609.29216v1 Announce Type: cross Abstract: Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinv
arXiv:2609.29076v1 Announce Type: cross Abstract: Predicting blood-brain barrier (BBB) permeability is critical for central nervous system drug discovery. Using the MoleculeNet BBBP dataset (n = 2039)
arXiv:2609.28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged n-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses o
arXiv:2609.29330v1 Announce Type: new Abstract: Identifying the set of monitored websites in mixed encrypted traffic is challenging because an individual flow often provides only partial evidence of w
arXiv:2609.28816v1 Announce Type: new Abstract: Robotic bodies are inherently distributed in sensing and actuation, yet learning-based control still commonly relies on centralized information processi
arXiv:2609.29125v1 Announce Type: new Abstract: In aerial RGB--IR object detection, effectively exploiting complementary information across modalities is critical for robust perception under complex i
arXiv:2609.28487v1 Announce Type: new Abstract: News headlines frame public issues both by what they select and by how they word it, yet computational framing work typically collapses these operations
arXiv:2609.29375v1 Announce Type: cross Abstract: Robust initialization is crucial for online systems. In the letter, a high-frequency and resilient initialization framework is designed for LiDAR-iner
arXiv:2609.29879v1 Announce Type: new Abstract: Generative modeling approaches often focus on recovering broad statistical characteristics from the training data. In the context of graph generation, t
arXiv:2609.28477v1 Announce Type: cross Abstract: Claims data can show that provider behavior changed but cannot by itself explain why. FWA (fraud, waste, and abuse) review requires identifying materi
arXiv:2609.30047v1 Announce Type: cross Abstract: Resistive switching in oxide-based devices is widely governed by stochastic defect processes, yet a predictive link between fabrication conditions and
arXiv:2609.29327v1 Announce Type: cross Abstract: Selective classification enables a model to abstain from predictions on uncertain instances, but existing approaches typically reject them through con
arXiv:2609.29546v1 Announce Type: cross Abstract: The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the
arXiv:2609.29491v1 Announce Type: cross Abstract: Voxel-based soft robots (VSRs) present a promising avenue for developing artificial organisms with lifelike intelligence. However, the vast design spa
arXiv:2609.28741v1 Announce Type: new Abstract: Multi-modal multitasking models have shown strong performance on remote sensing datasets. However, because these models are trained on heterogeneous dat
arXiv:2609.29024v1 Announce Type: new Abstract: Natural load-bearing and transport networks are not assembled in a single step; they emerge through a temporally ordered process of growth, branching, r
arXiv:2609.28931v1 Announce Type: new Abstract: Driving world models provide a promising route toward scalable counterfactual data generation and interactive simulation beyond recorded driving logs. R
arXiv:2609.29914v1 Announce Type: new Abstract: Many electrostatic actuators require multi-kilovolt drive voltages at sub-milliamp currents, a task poorly suited for conventional switching devices. As
arXiv:2609.29551v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose LLM activations into sparse dictionary atoms, so that each distinct concept gets its own feature. One recurring beh
arXiv:2609.29398v1 Announce Type: new Abstract: Multimodal attributed graphs connect entities, visual content, language, and observed relations. Learning one foundation across such graphs requires mor
arXiv:2609.29193v1 Announce Type: new Abstract: The strong performance that modern semantic correspondence methods achieve at standard thresholds plateaus sharply at fine-grained thresholds. We argue
arXiv:2609.29931v1 Announce Type: new Abstract: Radiology AI systems increasingly inform clinical decisions such as triage, follow-up imaging, and treatment planning. For these decisions to be made sa
arXiv:2609.28732v1 Announce Type: cross Abstract: Primary bone tumors are rare but clinically aggressive neoplasms whose diagnosis from radiographs is challenged by heterogeneous morphology, subtle le
arXiv:2604.01683v2 Announce Type: replace-cross Abstract: Scaled dot-product attention forms its queries and keys as independent linear projections, so the two never interact before the dot product th
arXiv:2609.29663v1 Announce Type: cross Abstract: White blood cells (WBCs) present on every Giemsa-stained thick blood smear share visual properties with early-stage Plasmodium falciparum ring-form tr
arXiv:2609.29252v1 Announce Type: new Abstract: A generalist vision encoder must capture semantic, spatial, language-aligned, and action-relevant cues within a unified representation, yet softmax atte
arXiv:2609.29769v1 Announce Type: new Abstract: We ask whether Jev, a typed classifier that returns probabilities over permitted answers without generating text, can replace an LLM rubric judge. We co
arXiv:2609.28761v1 Announce Type: cross Abstract: We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB multimodal DBMS. KathDB-FAO takes as input a query in n
arXiv:2609.28530v1 Announce Type: new Abstract: A general-purpose vision-language model can understand a task goal without knowing how a particular robot's motion and functional parts produce the inte
arXiv:2609.28920v1 Announce Type: new Abstract: Conventional model-based diffusion (MBD) achieves effective trajectory optimization by leveraging noise annealing. However, its high computational cost,
arXiv:2609.28603v1 Announce Type: cross Abstract: Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for dec
arXiv:2609.29383v1 Announce Type: new Abstract: Climate data downscaling is the task of increasing the spatial resolution of climate data, typically by generating fine-resolution regional climate data
arXiv:2609.29390v1 Announce Type: new Abstract: LLM evaluation is commonly performed either by prompting models to produce answers or by scoring candidate outputs with likelihood-based metrics. In mul
arXiv:2609.07746v2 Announce Type: replace Abstract: Even though backdoors in LLMs have been a growing concern, their inner workings are still under heavy scrutiny. Trigger-based backdoors are easy to
arXiv:2602.04489v2 Announce Type: replace Abstract: Temporarily ambiguous garden-path sentences ('While the team trained the striker wondered... ') are known to cause processing difficulty, which can
arXiv:2609.29409v1 Announce Type: cross Abstract: In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired b
arXiv:2609.28562v1 Announce Type: cross Abstract: Aggregation theory has traditionally focused on operators defined over vectors. However, many applications-including Multi-Criteria Decision Making, G
arXiv:2602.11323v2 Announce Type: replace Abstract: Traditional monocular Visual-Inertial Odometry (VIO) systems struggle in low-texture environments where sparse visual features are insufficient for
arXiv:2609.29156v1 Announce Type: cross Abstract: Long-tailed chest X-ray classification requires visual representations that capture both common abnormalities and subtle, infrequent findings. We prop
arXiv:2603.26797v2 Announce Type: replace Abstract: Large language models are increasingly used to generate financial alpha signals, but many have memorized the historical data in their training corpo
arXiv:2609.29779v1 Announce Type: new Abstract: Vessel segmentation is commonly optimized as voxel-wise classification, but small local errors can strongly disrupt vascular connectivity while having l
arXiv:2609.29863v1 Announce Type: new Abstract: Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. Th
arXiv:2609.29386v1 Announce Type: new Abstract: The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set
arXiv:2609.29603v1 Announce Type: cross Abstract: Social virtual reality (VR) creates a paradox. A user's body motion is a high-entropy biometric: head and hand trajectories alone re-identify users am