Knowledge catalogue
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:2609.29328v1 Announce Type: new Abstract: Understanding how Large Language Models (LLMs) encode linguistic structures remains a fundamental challenge in interpretability research. While diagnost
arXiv:2609.30147v1 Announce Type: new Abstract: Large Language Models (LLMs) typically exhibit a performance profile where reliability degrades as task complexity increases. We address the challenge o
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:2609.28859v1 Announce Type: new Abstract: Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality s
arXiv:2609.29457v1 Announce Type: new Abstract: Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward multimodal inspection systems that can describe, ex
arXiv:2609.28818v1 Announce Type: cross Abstract: Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. H
arXiv:2609.28697v1 Announce Type: new Abstract: Scientific tasks specify a desired capability, but realizing it often requires building a computational system tailored to the task---acquiring data, de
arXiv:2609.29350v1 Announce Type: new Abstract: Explicit geometric references offer a direct way to structure self-supervised representations. Existing adversarial distribution-matching formulations,
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:2609.29988v1 Announce Type: new Abstract: Synthetic data can scale training supervision when real-world data are limited, but noise and distribution mismatch can reduce its value. Existing synth
arXiv:2609.29785v1 Announce Type: new Abstract: Accurate brain tumor segmentation from Magnetic Resonance Imaging is essential for diagnosis, treatment planning, and surgical guidance. Although Convol
arXiv:2609.29581v1 Announce Type: new Abstract: Although diffusion-based methods have substantially improved the controllability of multimodal face synthesis, their semantic alignment remains suboptim
arXiv:2609.28851v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong visual reasoning performance, yet subtle changes from routine image capture and processing can alter their
arXiv:2609.28684v1 Announce Type: new Abstract: Encoding input coordinates with sinusoidal functions into multi-layer perceptrons (MLPs) has proven effective for implicit neural representations (INRs)
arXiv:2609.28836v1 Announce Type: cross Abstract: While various multimodal methods combining imaging and tabular data for Alzheimer's disease (AD) diagnosis were proposed, they are often limited in ge
arXiv:2603.06471v3 Announce Type: replace Abstract: Acquiring per-frame annotations for ultrasound videos is costly and requires clinical expertise, limiting learning-based analysis. We study cross-vi
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:2609.29015v1 Announce Type: new Abstract: Decentralized LLM-based multi-agent systems coordinate through local interactions, but an agent can remain responsive while its task-solving quality per
arXiv:2609.29913v1 Announce Type: new Abstract: Many-shot in-context learning (ICL) enables large language models (LLMs) to adapt to complex tasks by conditioning on thousands of demonstration example
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:2609.29940v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) achieve strong performance on visual reasoning tasks, yet remain prone to hallucinations and over-reliance on
arXiv:2510.27065v2 Announce Type: replace Abstract: We present MLPerf Automotive, the first standardized public performance benchmark for evaluating Machine Learning systems that are deployed for AI a
arXiv:2609.28793v1 Announce Type: new Abstract: Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acousti
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:2609.28660v1 Announce Type: new Abstract: Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents chall
arXiv:2609.30104v1 Announce Type: cross Abstract: High Performance Computing (HPC) and Quantum Computing (QC) systems are increasingly converging towards unified High Performance Computing-Quantum Com
arXiv:2609.29855v1 Announce Type: cross Abstract: We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency an
arXiv:2609.28860v1 Announce Type: new Abstract: Textual descriptions can reduce ambiguity in medical image segmentation by specifying the finding and location to be delineated. Existing text-guided me
arXiv:2609.30130v1 Announce Type: cross Abstract: Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of in
arXiv:2609.29317v1 Announce Type: cross Abstract: Time series analysis is fundamental in domains such as finance, healthcare, and meteorology. Real-world time series often exhibit multiscale character
arXiv:2609.28539v1 Announce Type: new Abstract: Gaussian head avatars typically model intrinsic facial appearance as temporally static, omitting subtle cardiac-induced skin-color variation. We propose
arXiv:2609.29959v1 Announce Type: new Abstract: Fine-grained object detectors are commonly evaluated with confusion matrices, which show where the model is confused but not why, nor whether the confus
arXiv:2609.29687v1 Announce Type: new Abstract: Automated driving systems (ADSs) are increasingly operating on public roads, raising safety concerns, yet reliable prediction of crash injury severity r
arXiv:2609.29985v1 Announce Type: new Abstract: Underwater 3D reconstruction is critical for marine exploration, ecological monitoring, and subsea infrastructure inspection, yet remains challenging at
arXiv:2609.28878v1 Announce Type: new Abstract: Sim-to-real transfer has made substantial progress, but can still produce controllers that remain stable and functional on hardware while suffering from
arXiv:2609.29876v1 Announce Type: new Abstract: Neurosymbolic research typically assumes a pre-existing symbolic specification, leaving the upstream challenge of acquiring and formalizing requirements
arXiv:2609.30115v1 Announce Type: cross Abstract: Modern deep neural networks treat parameters as static floating-point matrices stored in physical memory, incurring Von Neumann memory bottlenecks and
arXiv:2609.29788v1 Announce Type: new Abstract: Despite recent advancements in 3D generation, models often struggle to produce assets with high visual fidelity. To bridge this gap, we propose OREO, an
Pay attention to System One models if you are building custom harnesses and agentic workflows. Great to see DSPy integrate Jev and now support System One models for calibrating agentic workflows. I'm
arXiv:2609.28645v1 Announce Type: new Abstract: Recent advancements in 3D Gaussian Splatting (3DGS) have extended its capabilities to multi-scale segmentation. Existing methods reconstruct a scene wit
arXiv:2609.30226v1 Announce Type: cross Abstract: Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, such as human alignment, correctness, or instruction
arXiv:2609.29443v1 Announce Type: new Abstract: Head-pose variation introduces substantial appearance transformations in visual speech recognition (VSR), making pose-aware feature modulation desirable
arXiv:2609.28693v1 Announce Type: new Abstract: Large Language Model (LLM) agents struggle to scale safely when exposed to vast enterprise toolsets. Providing an agent with access to every internal to
arXiv:2609.28727v1 Announce Type: new Abstract: Contextual biasing improves rare-word recognition in speech large language models (SpeechLLMs), but efficiently exploiting large bias lists remains chal
arXiv:2609.29592v1 Announce Type: cross Abstract: Adapting large pretrained vision models under limited data and frozen-backbone constraints remains a central challenge in transfer learning. While lig
arXiv:2609.29394v1 Announce Type: new Abstract: General-purpose robot agents must learn from experience, transfer to new tasks, and act efficiently. Code as Policies (CaP) methods generate and repair
arXiv:2609.29714v1 Announce Type: cross Abstract: The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynam
arXiv:2609.30079v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have become a prominent approach for developing fast, topology-aware surrogates in electric power systems, supporting tas
arXiv:2609.30082v1 Announce Type: new Abstract: Robust dexterous grasping requires maintaining physical stability despite contacts interactively evolving across the entire hand. A precomputed force di
arXiv:2609.29678v1 Announce Type: cross Abstract: Semi-supervised fine-grained visual recognition is highly vulnerable to overconfident pseudo-label errors: visually similar categories frequently prod
arXiv:2609.30023v1 Announce Type: new Abstract: Imitation learning enables robots to acquire manipulation skills from demonstrations, but the resulting policies can fail outside the training data, whi
arXiv:2609.29389v1 Announce Type: new Abstract: Foundation vision-language models (VLMs) understand objects, instructions, and spatial relations, yet translating this capability into robotic manipulat
arXiv:2609.29706v1 Announce Type: new Abstract: Accurate pedestrian trajectory prediction is important for proactive road-safety applications, particularly at urban intersections where pedestrian moti
arXiv:2609.30192v1 Announce Type: new Abstract: Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises f
arXiv:2603.16407v4 Announce Type: replace Abstract: Transferring heavy payloads in maritime settings relies on efficient crane operation, limited by hazardous double-pendulum payload sway. This sway m
arXiv:2609.30177v1 Announce Type: new Abstract: Query understanding (QU) plays a critical role in production search systems, translating raw user queries into search execution plans that drive downstr
arXiv:2609.29073v1 Announce Type: new Abstract: Vision-Language Models (VLMs) describe scenes well but reason poorly about metric 3D structure such as absolute distances, physical sizes, or egocentric
arXiv:2609.29384v1 Announce Type: cross Abstract: Online handwriting provides a non-invasive and low-cost behavioral biomarker for Alzheimer's disease (AD) detection, as it reflects both cognitive pla