Knowledge catalogue
arXiv:2503.14858v5 Announce Type: replace Abstract: Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement l
arXiv:2610.04022v1 Announce Type: new Abstract: Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices
arXiv:2610.04364v1 Announce Type: new Abstract: Designing effective reward functions in RL typically requires substantial expertise and trial and error. Recent work automates this process with LLM-bas
arXiv:2610.05130v1 Announce Type: cross Abstract: An electromagnetic inversion scheme that integrates stochastic optimization (STO) and plug-and-play (PNP) regularization into contrast-source inversio
arXiv:2610.04336v1 Announce Type: new Abstract: Recent advances in generative world models have increased interest in digital models that reproduce both the appearance of real objects and their respon
arXiv:2610.06542v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning, yet a fundamental practical question remains unresolved:
arXiv:2610.06508v1 Announce Type: new Abstract: Illuminant estimation is a fundamental problem in computational photography, as it enables the correction of color shifts induced by varying lighting co
arXiv:2610.04300v1 Announce Type: new Abstract: The semantic information of objects in remote sensing images is typically invariant to geometric transformations from the dihedral group D4. However, SA
arXiv:2610.04939v1 Announce Type: new Abstract: We ask whether physically meaningful correspondences discovered inside a trained scientific model remain meaningful outside the conditions under which t
arXiv:2610.04256v1 Announce Type: new Abstract: Exactly computing the maximum function is a standard test case for studying depth in ReLU networks. Two hidden layers are known to suffice for up to twe
arXiv:2610.04343v1 Announce Type: cross Abstract: Reservoir computing (RC) is a machine learning technique for data-driven modeling of dynamics for forecasting and control, primarily studied in comput
arXiv:2610.04368v1 Announce Type: new Abstract: Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quant
arXiv:2507.18104v3 Announce Type: replace Abstract: Brain encoding models predict time-resolved neural activity from computational representations of ongoing experience, providing a principled framewo
arXiv:2610.05749v1 Announce Type: new Abstract: This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliabili
arXiv:2610.06464v1 Announce Type: new Abstract: As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip int
arXiv:2610.06798v1 Announce Type: cross Abstract: Machine-learning emulators of chaotic and stochastic systems are usually validated on forecast skill and long-run statistics. Neither certifies that a
arXiv:2610.05166v1 Announce Type: cross Abstract: A safe action is not necessarily a viable one. Under a frozen vision-language-action (VLA) policy, an action can be likely and locally admissible yet
arXiv:2610.06701v1 Announce Type: cross Abstract: A recurring design principle in modern optimizers is to decouple update magnitude from the raw gradient norm, yet its consequences for learning-curve
arXiv:2610.05779v1 Announce Type: new Abstract: AI-generated videos are rapidly increasing in volume, duration, and resolution, creating growing demands for efficient storage and transmission. Unlike
arXiv:2610.05971v1 Announce Type: new Abstract: Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a locat
arXiv:2610.05058v1 Announce Type: cross Abstract: Pointwise Mutual Information (PMI) is a core measure of testing word association, and Skip-gram with Negative Sampling (SGNS) is essentially a method
arXiv:2610.05413v1 Announce Type: new Abstract: Evaluating vision encoders requires metrics that reliably predict their downstream performance in multimodal large language models (MLLMs). Although rec
arXiv:2610.06427v1 Announce Type: new Abstract: As robotic teams tackle increasingly complex tasks in dynamic and unstructured environments, effective coordination requires agents to maintain accurate
arXiv:2610.05794v1 Announce Type: cross Abstract: We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a mo
arXiv:2610.04052v1 Announce Type: new Abstract: Shape from shading using a single image of a Lam- bertian surface is inherently ambiguous. When the light source direction is known, the surface normal
arXiv:2610.05758v1 Announce Type: new Abstract: Holes in LiDAR scans of environments with glass windows remain an unresolved problem in three-dimensional reconstruction. This study presents a pipeline
arXiv:2610.04949v1 Announce Type: new Abstract: Reinforcement learning controllers for underwater vehicles are usually trained against one physics representation and deployed against another, so repor
arXiv:2610.05269v1 Announce Type: new Abstract: We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support foreca
arXiv:2006.08426v5 Announce Type: replace-cross Abstract: Descent directions are fundamental to first-order methods for constrained convex optimization. In this work, we provide a unified perspective
arXiv:2610.04787v1 Announce Type: new Abstract: Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is lim
arXiv:2610.03896v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms
arXiv:2508.06084v3 Announce Type: replace Abstract: Vision-language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due t
arXiv:2610.06693v1 Announce Type: new Abstract: While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the li
arXiv:2610.04264v1 Announce Type: new Abstract: The Gromov-Wasserstein (GW) problem compares structured distributions without requiring a shared feature space or known correspondences, but its nonconv
arXiv:2610.06019v1 Announce Type: new Abstract: With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from sc
arXiv:2610.06089v1 Announce Type: new Abstract: Diffusion- and flow-based robot policies have recently become widespread in robotic Imitation Learning (IL) due to their high performance and ability to
arXiv:2610.04039v1 Announce Type: cross Abstract: Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically k
arXiv:2610.05835v1 Announce Type: cross Abstract: Tactile Internet (TI) security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We stud
arXiv:2610.05774v1 Announce Type: new Abstract: Block drafters such as DSpark propose ranked candidates for several positions in one forward pass, and a tree verifier checks them in one pass of the ta
arXiv:2610.05098v1 Announce Type: new Abstract: Many latent neural operators represent input and output fields in a stationary latent chart. In particular, common latent routing mechanisms use fixed o
arXiv:2610.06643v1 Announce Type: cross Abstract: Robot learning in simulation depends on the objects the simulator offers. Many tasks need objects with separate parts, joints that allow the required
arXiv:2610.05334v1 Announce Type: cross Abstract: Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model see
arXiv:2610.04134v1 Announce Type: cross Abstract: The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing,
arXiv:2610.05782v1 Announce Type: cross Abstract: Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces signific
arXiv:2610.04391v1 Announce Type: new Abstract: Vision-language-action policies may predict a transferable manipulation strategy yet fail to realize it reliably on the encountered object: objects comp
arXiv:2610.04425v1 Announce Type: new Abstract: Agricultural visual models are typically evaluated for either recognition or localization, but reliable diagnosis requires identifying what is present a
arXiv:2610.04801v1 Announce Type: cross Abstract: A useful model of AI inference infrastructure must specify the system state, the information available to an observer, and the decisions the model is
arXiv:2610.05367v1 Announce Type: cross Abstract: Proof auto-formalization translates natural-language (NL) theorems and proofs into a formal language (FL) such as Lean, enabling mechanical verificati
arXiv:2610.06685v1 Announce Type: new Abstract: Clinical questions often depend on linking a patient's multimodal evidence to external biomedical knowledge, yet existing predictive systems rarely repr
arXiv:2603.13790v2 Announce Type: replace Abstract: Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundame
arXiv:2610.04523v1 Announce Type: cross Abstract: We propose a distribution-free metric to rate individual skill in ``player-versus-environment'' settings, where participants face heterogeneous tasks
arXiv:2610.04198v1 Announce Type: cross Abstract: Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a p
arXiv:2601.08172v2 Announce Type: replace Abstract: Bayesian optimization (BO) of expensive black-box functions is traditionally addressed with Gaussian processes (GPs), which scale cubically with obs
arXiv:2610.04285v1 Announce Type: cross Abstract: We develop an amortized, grid-free implementation of continuous Langevin dynamics based policy-value iteration for entropy-regularized, infinite-horiz
arXiv:2610.04612v1 Announce Type: new Abstract: Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulti
arXiv:2610.05558v1 Announce Type: new Abstract: The Dobrushin coefficient of each attention-rollout factor satisfies kappa(frac12(I+A))lefrac12(1+kappa(A)), and multiplying these inequalities over lay
arXiv:2610.05676v1 Announce Type: cross Abstract: Groups of individuals can solve collective problems more accurately than any single member, by aggregating their opinions. Recent theoretical work has
arXiv:2610.05600v1 Announce Type: new Abstract: High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the `
arXiv:2610.06596v1 Announce Type: new Abstract: Short-wave infrared (SWIR) imaging has emerged as a promising modality for autonomous driving, yet its practical benefits over RGB remain poorly charact
arXiv:2610.06837v1 Announce Type: new Abstract: Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on i