Knowledge catalogue
arXiv:2610.15323v1 Announce Type: cross Abstract: Longitudinal health data often exhibit within-subject correlation, subject-specific heterogeneity, and complex nonlinear predictor--response relations
arXiv:2610.16273v1 Announce Type: cross Abstract: Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains
arXiv:2610.16546v1 Announce Type: new Abstract: Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks
arXiv:2610.17316v1 Announce Type: cross Abstract: Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These meth
arXiv:2610.17680v1 Announce Type: cross Abstract: Building a co-registered SAR-optical corpus for self-supervised Earth Observation is above all an acquisition problem, yet the main multi-modal datase
arXiv:2610.16597v1 Announce Type: new Abstract: Expressive flow policies can represent multiple high-value actions, but sampling them with few integration steps while retaining tractable action densit
arXiv:2610.14497v1 Announce Type: new Abstract: EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstrea
arXiv:2610.14854v1 Announce Type: new Abstract: Contact-rich manipulation poses a stage-dependent sensory conflict: vision and proprioception are essential for localization and approach, when force is
arXiv:2610.17751v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) are commonly organized around two coordination regimes. Centralized coordination provides e
arXiv:2610.14710v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language unders
arXiv:2610.15794v1 Announce Type: new Abstract: Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action
arXiv:2610.15185v1 Announce Type: new Abstract: Test-time memorization of sequential information is a foundational capability for any intelligent system that needs to rapidly store and retrieve data,
arXiv:2610.17666v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) for multi-turn agents relies on sparse terminal rewards that say little about individual steps. Wi
arXiv:2610.14685v1 Announce Type: new Abstract: Recent advances in video diffusion enable novel-view video synthesis from a single input video, supporting world modeling, embodied intelligence, and ro
arXiv:2606.13106v2 Announce Type: replace Abstract: Latent chain-of-thought compresses reasoning through continuous hidden-state recurrence, but existing approaches are difficult to optimize with stan
arXiv:2610.18556v1 Announce Type: new Abstract: Deep learning for medical image restoration is often constrained by the need for paired training data. Acquiring perfectly registered clean-noisy radiog
arXiv:2610.16570v1 Announce Type: new Abstract: Bronchoscopy inserts a camera through the airways to diagnose endoluminal disease. Locating the camera along the airway usually requires additional equi
arXiv:2610.17214v1 Announce Type: new Abstract: What information does a reinforcement learning policy omit? At each state, a standard policy stores one probability per action. It has no place to recor
arXiv:2609.14421v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement infor
arXiv:2610.14873v1 Announce Type: cross Abstract: Aspect Sentiment Quadruple Prediction (ASQP) is fundamentally a relational reasoning problem: identifying aspect-opinion-category-sentiment quadruples
arXiv:2610.18809v1 Announce Type: new Abstract: Residual connections propagate information through a network by additively accumulating layer outputs. Recently proposed alternatives, such as Manifold-
arXiv:2610.16278v1 Announce Type: new Abstract: Existing-image self-tiling aims to transform a given image into a seamlessly repeatable tile while preserving its original semantics and local details.
arXiv:2610.16604v1 Announce Type: new Abstract: Humanoids increasingly demand lightweight leg mechanisms for agile, dynamic, and energy-efficient locomotion. Reducing distal inertia through proximal a
arXiv:2610.15001v1 Announce Type: new Abstract: Multi-robot systems that combine the complementary capabilities of specialised platforms are critical for complex missions in challenging, inaccessible
arXiv:2610.14436v1 Announce Type: cross Abstract: Multimodal learning offers the potential to improve predictions by integrating complementary information from heterogeneous data sources. However, mul
arXiv:2610.16463v1 Announce Type: new Abstract: Vision-based tactile sensors enable fine-grained perception for dexterous robotic manipulation, but current marker-based designs tightly couple surface
arXiv:2610.16598v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning enables multimodal large reasoning models (MLRMs) to solve complex visual problems, but also exposes them to CoT hijack
arXiv:2610.18575v1 Announce Type: new Abstract: Post-training INT8 quantization is typically validated by checking accuracy retention, which requires ground-truth labels often unavailable in low-resou
arXiv:2609.15308v1 Announce Type: cross Abstract: We present a deterministic machine-learning method for denoising candidate signals from all-sky searches for continuous gravitational waves from rotat
arXiv:2610.18621v1 Announce Type: new Abstract: Accurate uncertainty quantification is critical for many time series forecasting applications, and recent studies have increasingly adopted generative m
arXiv:2602.15828v2 Announce Type: replace-cross Abstract: Learning generalist policies capable of accomplishing a wide range of everyday tasks remains an open challenge in dexterous manipulation. In p
arXiv:2610.14691v1 Announce Type: new Abstract: Recent advances in 3D hand-object interaction (HOI) perception have opened new opportunities for learning dexterous robotic skills from human videos. Ho
arXiv:2610.14372v1 Announce Type: cross Abstract: Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisi
arXiv:2610.17208v1 Announce Type: cross Abstract: Cutting a piece of food looks effortless in human hands. For a robot, however, every cut is a high-contact interaction with a deformable, elasto-plast
arXiv:2610.17789v1 Announce Type: cross Abstract: A long-horizon manipulation task consists of primitive skills and the transitions connecting them. Learning skills that can be executed in sequence re
arXiv:2607.09507v2 Announce Type: replace Abstract: Global Structure-from-Motion (SfM) is an efficient paradigm for recovering camera poses and sparse 3D structure from unordered images. However, its
arXiv:2610.17882v1 Announce Type: new Abstract: Flow-based image generators achieve strong semantic synthesis but still produce localized structural distortions, such as malformed hands and limbs. Exi
arXiv:2610.14296v1 Announce Type: cross Abstract: High benchmark scores in learning-based vulnerability detection mask severe degradation under distribution shifts. This paper demonstrates that calibr
arXiv:2610.16458v1 Announce Type: cross Abstract: Preference alignment enables language models to better reflect human values, but the preference data contains sensitive information, especially when d
arXiv:2502.05505v4 Announce Type: replace-cross Abstract: Differentially private (DP) synthetic data enables the use of sensitive datasets while providing strong privacy guarantees. Recently, Private
arXiv:2610.14999v1 Announce Type: cross Abstract: Steering candidate generation toward user-defined objectives is central to inverse materials design, yet remains difficult when objectives are black-b
arXiv:2610.15932v1 Announce Type: new Abstract: Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something convent
arXiv:2610.14473v1 Announce Type: cross Abstract: We propose a new framework for banner ad auctions, leveraging diffusion models to allow for multiple bidders to co-win a single banner ad opportunity,
arXiv:2605.06538v2 Announce Type: replace Abstract: Diffusion-based posterior samplers draw from measurement- or reward-conditioned posteriors using pretrained diffusion priors. Though widely used for
arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more
arXiv:2610.17015v1 Announce Type: new Abstract: Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit dimini
arXiv:2610.16139v1 Announce Type: new Abstract: Learned world models for autonomous driving operate in unconstrained latent spaces, yet the physical systems they represent are governed by strict non-h
arXiv:2610.17644v1 Announce Type: new Abstract: Knowledge distillation (KD) frameworks place teacher and student on the same GPUs, so per-GPU memory limits the teachers that fit. DisaggKD separates th
arXiv:2610.15006v1 Announce Type: cross Abstract: We present DisCo algebra---a compositional framework for joint probability built from a symmetric cooperad (disintegration) and a symmetric operad (co
arXiv:2610.14013v1 Announce Type: cross Abstract: Drug development is an expensive and lengthy process requiring developers to select drugs for human trials and diseases to target. Computational tools
arXiv:2610.14853v1 Announce Type: new Abstract: Optimal transport (OT) is widely used to align distributions and transfer labels between unpaired source and target data, but source-target composition
arXiv:2610.14159v1 Announce Type: cross Abstract: Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited
arXiv:2610.14571v1 Announce Type: cross Abstract: Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions. Although carbon-aware scheduling can reduce these emis
arXiv:2610.15996v1 Announce Type: new Abstract: In this paper, we propose the Dissipative Lagrangian Neural Network (dLNN), a continuous-time system identification technique that separately learns the
arXiv:2610.17576v1 Announce Type: cross Abstract: Answering questions from radiology reports involves two decisions: whether the report contains an answer and which span gives it. A false answer prese
arXiv:2602.02928v2 Announce Type: replace Abstract: Modern generative models based on diffusion or flow matching transport noise toward the data manifold, without representing the data manifold explic
arXiv:2610.18130v1 Announce Type: cross Abstract: The distance of a quantum code is a fundamental metric that dictates its error-correction capability. While quantum low-density parity-check (qLDPC) f
arXiv:2602.00865v2 Announce Type: replace Abstract: While multi-view 3D reconstruction has shifted toward large-scale foundation models capable of inferring globally consistent geometry, their relianc
arXiv:2610.15739v1 Announce Type: new Abstract: A deterministic method for the problem of distributed area coverage path planning subject to the inclusion of a headland path is presented. Given a, in
arXiv:2610.16142v1 Announce Type: cross Abstract: This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or E