Knowledge catalogue
arXiv:2609.29717v1 Announce Type: new Abstract: Automated segmentation of cryo-electron tomograms routinely produces masks that are voxel-accurate but topologically broken: membranes fragment, organel
arXiv:2609.30245v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spat
arXiv:2609.29099v1 Announce Type: cross Abstract: Multimodal training relies on image-text corpora collected from external sources, creating opportunities for attackers to poison the data. Stealthy at
arXiv:2609.30026v1 Announce Type: new Abstract: Detecting which holds a climber uses, and when, underpins automated scoring, movement analysis, and assistive systems for sport climbing. Existing appro
arXiv:2609.28654v1 Announce Type: new Abstract: Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a paradigmatic class of curren
arXiv:2609.29060v1 Announce Type: cross Abstract: Transformers achieve remarkable performance by jointly learning broad families of tasks during pretraining and adapting to unseen tasks from only a sh
arXiv:2609.29733v1 Announce Type: new Abstract: Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While th
arXiv:2609.26758v2 Announce Type: replace Abstract: Typed decision models are built for settings where model outputs are consumed directly by software. Instead of generating free-form text, they retur
arXiv:2609.28578v1 Announce Type: new Abstract: The increasing adoption of electric vehicles (EVs) and rooftop photovoltaic (PV) systems is reshaping residential electricity demand and creating new ch
arXiv:2508.18192v2 Announce Type: replace Abstract: Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging
arXiv:2609.29553v1 Announce Type: cross Abstract: Automatic stress recognition from facial video provides a non-contact approach for affective monitoring. However, most existing video-based methods di
arXiv:2609.29650v1 Announce Type: new Abstract: Tabular-to-image encoding methods enable the application of models based on both convolutional neural networks and vision transformers to tabular data,
arXiv:2609.29555v1 Announce Type: new Abstract: Navigation World Models (NWMs) predict action-conditioned visual futures for planning. Two practical challenges are central to their design: selecting a
arXiv:2601.19753v2 Announce Type: replace Abstract: Underwater 3D reconstruction and appearance restoration remain challenging due to the complex optical properties of water, such as wavelength-depend
arXiv:2609.29396v1 Announce Type: new Abstract: This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulato
arXiv:2609.29507v1 Announce Type: new Abstract: Two accounts of neural text degeneration coexist. One locates the cause in the training data -- repetition in the corpus produces repetition in the outp
arXiv:2609.28832v1 Announce Type: new Abstract: Reconstruction-based unsupervised learning can fail in two opposing ways: a model may reconstruct anomalies too accurately or discard valid nominal vari
arXiv:2609.28565v1 Announce Type: cross Abstract: Post hoc explanation methods such as SHAP and LIME are widely used to interpret text classifiers, but their visualizations are mainly designed for lef
arXiv:2609.29312v1 Announce Type: new Abstract: Communities often respond to potentially AI-assisted work by asking three questions: Was AI used? Was that use disclosed? Can hidden use be detected? Th
arXiv:2609.29048v1 Announce Type: new Abstract: Unified vision-language models (VLMs) that tokenize images through a vector-quantized (VQ) codebook routinely hallucinate objects on grounded yes/no ben
arXiv:2609.28947v1 Announce Type: cross Abstract: Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without ide
arXiv:2609.29845v1 Announce Type: cross Abstract: While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inp
arXiv:2609.26923v1 Announce Type: cross Abstract: Cricket is one of the most celebrated sports world-wide, and technological advancement has become deeply embedded in how the modern game is analyzed a
arXiv:2509.10367v2 Announce Type: replace Abstract: Given a dataset of finitely many elements T = {mathbf{x}_i}_{i = 1}^N, the goal of dataset condensation (DC) is to construct a synthetic dataset S =
arXiv:2609.27139v1 Announce Type: cross Abstract: Surgical procedures follow a phase-to-step hierarchy, yet the video-language models used to recognize them are evaluated with flat per-level metrics t
arXiv:2609.27863v1 Announce Type: new Abstract: Knowledge graph completion is evaluated by ranking observed triples above randomly corrupted ones, which treats every unobserved fact as false. When the
arXiv:2609.27325v1 Announce Type: cross Abstract: In this work, we propose a hybrid iterative deep Ritz method (H-IDRM) for a class of interface problems for second-order elliptic operators. It is bas
arXiv:2609.26848v1 Announce Type: cross Abstract: Postoperative acute kidney injury (AKI) after major non-cardiac surgery carries substantial morbidity, yet early intraoperative risk stratification re
arXiv:2609.27466v1 Announce Type: new Abstract: Industrial control electronics such as programmable logic controllers, servo drives and operator panels are routinely repaired in plant maintenance, but
arXiv:2609.27240v1 Announce Type: new Abstract: In this paper, we present the Berkeley QUAD (Quasi-direct-drive, Underactuated, Asymmetric Design) Hand, a four-finger anthropomorphic robotic hand with
arXiv:2609.27866v1 Announce Type: cross Abstract: Sharing a deep encoder does not, by itself, fix the central confound of task-comparison scores. We show that cross-evaluated heads on a frozen shared
arXiv:2609.28230v1 Announce Type: new Abstract: Visual grounding in remote sensing images aims to locate objects described by referring expressions. Most existing methods predict horizontal bounding b
arXiv:2505.12576v3 Announce Type: replace-cross Abstract: A key factor in effective Self-Supervised learning (SSL) is preventing dimensional collapse, where higher-dimensional representation spaces (R
arXiv:2601.18932v2 Announce Type: replace-cross Abstract: Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success
arXiv:2609.27022v1 Announce Type: new Abstract: In this paper, we investigate the impact of adversarial attacks on identity encoders within a realistic de-identification framework. Our experiments sho
This week, world leaders descended on Manhattan for the UN General Assembly. It’s also New York Climate Week—investors, policymakers, advocates, and journalists are colliding at panels, talks, and fan
arXiv:2609.27901v1 Announce Type: new Abstract: Video is a rich representation of a physical event, capturing appearance, geometry, motion, and temporal evolution. Other modalities, such as 3D body mo
arXiv:2609.21798v1 Announce Type: cross Abstract: Architectures with periodic activation functions have already been shown to be beneficial in comparison to monotonic counterparts for a wide range of
arXiv:2609.27015v1 Announce Type: new Abstract: We developed an anatomy-aware deep learning framework to synthesize post-contrast breast MRI from pre-contrast images, emphasizing tumor and background
arXiv:2609.27038v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring assumes that the reasoning a model writes reflects the computation that directly produces its answer. Previous faithfu
arXiv:2609.27371v1 Announce Type: cross Abstract: Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake a
arXiv:2609.27373v1 Announce Type: new Abstract: Recurrent language models repeatedly apply shared network blocks to refine latent representations, but standard inference recomputes global attention at
arXiv:2609.26868v1 Announce Type: new Abstract: Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model pre
arXiv:2609.27760v1 Announce Type: cross Abstract: Federated learning enables distributed training of a shared model without requiring clients to share their raw data. However, its reliance on the inte
Banger paper introducing Jev-as-a-Judge. The overall finding is that you want to use a cheap judge for most of your evals and send only the uncertain calls to a frontier model. This paper measures how
arXiv:2609.28339v1 Announce Type: new Abstract: Pretrained generative Diffusion Transformers (DiTs) capture rich pixel-level visual and language-conditioned structure through large-scale image and vid
arXiv:2609.27356v1 Announce Type: new Abstract: A region can overlap a target object yet contribute more to another class. We test regions selected by Cluster-based Concept Importance (CCI) in frozen
arXiv:2603.19713v2 Announce Type: replace Abstract: Even when it is difficult to assign absolute class labels to individual instances, relational information may still be available, such as whether tw
arXiv:2609.27468v1 Announce Type: new Abstract: Action-chunked vision-language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accum
arXiv:2609.27242v1 Announce Type: cross Abstract: LLMs can generate programs for ARC-AGI-2 tasks, but the provided compute only allows a small number of attempts to generate, debug and validate soluti
arXiv:2609.28250v1 Announce Type: new Abstract: At test time, large language models (LLMs) can encode historical information in activation memory (i.e., KV caches) and parametric memory (i.e., updated
arXiv:2609.27439v1 Announce Type: new Abstract: Delayed acoustic positioning packets constrain historical navigation states, but a current-time update evaluates them against a mismatched state, wherea
arXiv:2609.28165v1 Announce Type: new Abstract: Multimodal learning (MML) falls into the optimization dilemma due to the modality imbalance phenomenon, leading to suboptimal overall performance in pra
arXiv:2609.28427v1 Announce Type: new Abstract: Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We ex
arXiv:2609.28471v1 Announce Type: new Abstract: Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify
arXiv:2609.27381v1 Announce Type: new Abstract: Contact detection during robotic manipulation allows robots to recognize unexpected contact and adapt their motion accordingly. However, in low-cost rob
arXiv:2609.28430v1 Announce Type: new Abstract: This work addresses continuous depression-severity score prediction from clinical interview transcripts under data scarcity. We propose a sequential low
arXiv:2609.27115v1 Announce Type: new Abstract: A face recognition model links two images of a person recorded on separate occasions when their embedding similarity exceeds an operating threshold. We
arXiv:2609.28159v1 Announce Type: new Abstract: Standard contrastive learning frameworks are mainly designed from a semantic perspective, yet learning 2D visual representations that preserve 3D spatia
arXiv:2609.27099v1 Announce Type: new Abstract: Surgical robots require highly dexterous and compact robotic systems capable of operating effectively within confined anatomical spaces. However, due to