Knowledge catalogue
arXiv:2607.06919v1 Announce Type: new Abstract: Artificial intelligence (AI) analysis of micro-ultrasound (muUS) has shown promise for prostate cancer (PCa) detection. However, most existing AI method
arXiv:2603.16551v2 Announce Type: replace-cross Abstract: Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that ge
arXiv:2607.07060v1 Announce Type: cross Abstract: Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The margi
arXiv:2607.07129v1 Announce Type: new Abstract: Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Levera
arXiv:2607.06940v1 Announce Type: cross Abstract: The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their respon
arXiv:2607.06893v1 Announce Type: cross Abstract: The Stochastic-Oracle Turing Machine (SOTM) framework models AI-augmented computation as the interaction of a probabilistic Turing machine with an ora
arXiv:2607.07026v1 Announce Type: new Abstract: Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function ca
arXiv:2508.00554v4 Announce Type: replace-cross Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy
arXiv:2607.07574v1 Announce Type: new Abstract: Robotic surface swabbing requires sustained interaction between a compliant tool and heterogeneous environments, where accurate estimation of tip-level
arXiv:2607.07532v1 Announce Type: new Abstract: Accurate mapping of informal settlements remains a major challenge in Sub-Saharan African (SSA) cities because optical imagery often fails to distinguis
arXiv:2607.07209v1 Announce Type: cross Abstract: Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interac
arXiv:2607.07622v1 Announce Type: new Abstract: The elephant trunk is a dexterous and versatile manipulator whose performance is still unmatched in robotics. In previous works, modularity was prioriti
arXiv:2411.17077v2 Announce Type: replace-cross Abstract: As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applicat
arXiv:2607.06887v1 Announce Type: new Abstract: Most self-supervised image clustering models, actually almost all deep learning approaches, are based on gradient descent: In order to calculate the los
arXiv:2607.06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary
arXiv:2607.06622v1 Announce Type: cross Abstract: Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of
arXiv:2607.07521v1 Announce Type: cross Abstract: AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and archite
arXiv:2606.15967v2 Announce Type: replace Abstract: Anisotropic volumetric acquisitions are common in clinical MRI and volume electron microscopy (vEM), where sparse through-plane sampling creates thi
arXiv:2607.06628v1 Announce Type: cross Abstract: Which properties of a partially trained network are causally portable to a different, independently trained network? Single-trajectory interventions s
arXiv:2607.06609v1 Announce Type: cross Abstract: We propose D2PO (Dynamic Direct Preference Optimization), a principled framework for optimizing diffusion sampling policies with respect to timestep s
arXiv:2603.15685v2 Announce Type: replace-cross Abstract: Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make in
arXiv:2412.03405v3 Announce Type: replace-cross Abstract: Motivated by dynamic risk measures and conditional g-expectations, in this work we propose a numerical method to approximate the solution oper
arXiv:2607.06610v1 Announce Type: cross Abstract: Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dy
arXiv:2607.07409v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash furthe
arXiv:2607.06766v1 Announce Type: cross Abstract: At Amazon Prime Video, we face the critical operational challenge of managing code deployments during live events and rapid feature releases without c
arXiv:2607.07669v1 Announce Type: cross Abstract: Large language models increasingly understand dialectal English, yet still produce only standard, US-leaning English, leaving dialectal generation, th
arXiv:2607.07195v1 Announce Type: new Abstract: Perceptual quality enhancement of severely compressed videos remains challenging due to complex artifact patterns and substantial information loss. Rece
arXiv:2607.06646v1 Announce Type: cross Abstract: This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and str
arXiv:2512.20685v3 Announce Type: replace-cross Abstract: Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of lat
arXiv:2607.06681v1 Announce Type: cross Abstract: Knowledge workers switch between applications thousands of times per day, spending nearly a tenth of the work year transitioning between digital appli
arXiv:2607.07232v1 Announce Type: cross Abstract: Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models
arXiv:2607.07486v1 Announce Type: new Abstract: 3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these m
arXiv:2607.07003v1 Announce Type: cross Abstract: Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect. While sycophancy is often tr
arXiv:2511.23347v2 Announce Type: replace Abstract: An associative memory (AM) enables cue-response recall, and it has recently been recognized as a key mechanism underlying modern neural architecture
arXiv:2607.07128v1 Announce Type: new Abstract: Language models perform a wide range of tasks at varying levels of abstraction with the capacity to flexibly infer tasks from context, execute multiple
arXiv:2607.07139v1 Announce Type: new Abstract: Underwater robots often operate near delicate targets where high-power thrusters resuspend sediments and induce turbulence, degrading image quality at t
arXiv:2607.06603v1 Announce Type: cross Abstract: The notion of algorithmic fairness has been actively explored from various aspects of fairness, such as counterfactual fairness (CF) and group fairnes
arXiv:2607.07504v1 Announce Type: new Abstract: Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests
arXiv:2607.07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the
arXiv:2607.07670v1 Announce Type: new Abstract: Large language models hallucinate most about entities they have never seen. We ask whether a model's activations betray entity familiarity before a sing
arXiv:2607.06632v1 Announce Type: new Abstract: Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy syste
arXiv:2607.06653v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterogen
arXiv:2607.07608v1 Announce Type: cross Abstract: Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling wi
arXiv:2603.19742v2 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of transformer-based large language models (LLMs) is crucial for their reliable deployment and effective
arXiv:2606.15346v2 Announce Type: replace Abstract: Spatio-temporal prediction supports radar/satellite nowcasting and city-scale traffic monitoring, but modern models are often too expensive for real
arXiv:2607.06631v1 Announce Type: cross Abstract: Video Diffusion Models (VDMs) have demonstrated superior generation quality but suffer from prohibitive computational costs. While recent few-step dis
arXiv:2607.06896v1 Announce Type: cross Abstract: Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environme
arXiv:2607.07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG
arXiv:2508.21556v3 Announce Type: replace Abstract: Modeling human-object interactions (HOI) from an egocentric perspective is a critical yet challenging task, particularly when relying on sparse sign
arXiv:2607.06982v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of
arXiv:2607.07187v1 Announce Type: new Abstract: Local editing of 3D objects remains a long-standing challenge. When interacting with 3D content, humans naturally tend to specify a coarse region of int
arXiv:2603.21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github. Yet long-horizon t
arXiv:2607.06776v1 Announce Type: new Abstract: We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive pre
arXiv:2607.06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.g., Adam and Muon) by meta-learning small neural network optimizers over a distrib
arXiv:2607.07001v1 Announce Type: new Abstract: Anticipating human motion from an egocentric perspective is fundamental for proactive assistance in AR/VR, human-robot collaboration, and embodied AI. W
arXiv:2607.07459v1 Announce Type: cross Abstract: We present EmbodiedGen V2, a generative 3D world engine for building executable sim-ready environments for embodied intelligence. Sim-ready 3D asset g
arXiv:2602.23802v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture th
arXiv:2607.06964v1 Announce Type: cross Abstract: Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) a
arXiv:2607.06796v1 Announce Type: cross Abstract: Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing. Howe
arXiv:2607.06872v1 Announce Type: new Abstract: The increasing accessibility of artificial intelligence has led to a rapid rise in AI-generated videos, making it more difficult to distinguish between