Knowledge catalogue
arXiv:2605.03662v1 Announce Type: new Abstract: In this work, a novel method for planar task and motion planning based on hybrid modeling is proposed. By virtue of a discrete variable which models loc
arXiv:2605.02968v1 Announce Type: new Abstract: We introduce a finite-size gradient-transport framework for real language-model training, based on five observables (D,z,eta,elta,v_{rel}) that separate
arXiv:2605.02827v1 Announce Type: new Abstract: Probabilistic values, including Shapley values and semivalues, provide a model-agnostic framework to attribute the behavior of a black-box model to data
arXiv:2605.03588v1 Announce Type: new Abstract: We introduce a general framework for training flow matching models on Riemannian symmetric spaces, a large class of manifolds that includes the sphere,
arXiv:2605.03337v1 Announce Type: new Abstract: The recent surge in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction. While these methods demonstrate remarkable perfor
arXiv:2605.03387v1 Announce Type: new Abstract: Large language models perform well on high-resource pairs but are less reliable for Japanese-Chinese sentences containing noun-modifying clause construc
arXiv:2605.02859v1 Announce Type: cross Abstract: Scientists increasingly rely on sensor-based data, yet transforming raw streams into insights across the edge-to-cloud continuum remains difficult. Pr
arXiv:2601.22678v2 Announce Type: replace Abstract: Full-graph and mini-batch Graph Neural Network (GNN) training approaches have distinct system design demands, making it crucial to choose the approp
arXiv:2605.03372v1 Announce Type: new Abstract: Future imaging spectrometers will increase data volumes by orders of magnitude, requiring automated detection of trace gas point sources. We present a f
arXiv:2605.03669v1 Announce Type: new Abstract: Open-vocabulary semantic mapping enables robots to spatially ground previously unseen concepts without requiring predefined class sets. Current training
arXiv:2605.03109v1 Announce Type: new Abstract: A method is presented for accelerating inference in transformer language models by exploiting the low effective rank of the token activation manifold at
arXiv:2605.03750v1 Announce Type: new Abstract: Evidential Deep Learning (EDL) enables single-pass uncertainty estimation by predicting Dirichlet evidence, but it can remain overconfident and poorly c
arXiv:2605.00843v1 Announce Type: cross Abstract: This paper investigates how generative-artificial intelligence AI is reshaping job requirements, skill compositions and sectoral dynamics across globa
arXiv:2605.03414v1 Announce Type: new Abstract: Determining the geolocation of extreme climate events and disasters in texts is a common problem in climate impact and adaptation research. Named-entity
arXiv:2605.03764v1 Announce Type: new Abstract: Diffusion-based voxel prior modelling is challenging for the reconstruction of large-scale 3D porous microstructures. Due to the demanding requirements
arXiv:2605.03795v1 Announce Type: new Abstract: Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and often affect
arXiv:2605.03497v1 Announce Type: new Abstract: Score-based diffusion models in infinite-dimensional function spaces provide a mathematically principled framework for modelling function-valued data, o
arXiv:2602.10613v2 Announce Type: replace-cross Abstract: The Highly Adaptive Lasso (HAL) is a nonparametric regression method that achieves almost dimension-free convergence rates under minimal smoot
arXiv:2603.17679v2 Announce Type: replace Abstract: Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the
arXiv:2605.03289v1 Announce Type: cross Abstract: In many classification settings, the class of primary interest is underrepresented, leading to imbalanced data problems that arise in applications suc
arXiv:2605.03636v1 Announce Type: new Abstract: Information plane (IP) analysis has been suggested to study the training dynamics of deep neural networks through mutual information (MI) between inputs
arXiv:2605.03266v1 Announce Type: cross Abstract: Effective sample size is a standard summary of Markov chain Monte Carlo output, but it is usually attached to scalar or Euclidean summaries chosen by
arXiv:2605.01400v1 Announce Type: cross Abstract: Educational recommender systems (ERSs) are becoming increasingly important in enhancing educational outcomes and personalizing learning experiences by
arXiv:2507.02974v3 Announce Type: replace-cross Abstract: As major progress in LLM-based long-form text generation enables paradigms such as retrieval-augmented generation (RAG) and inference-time sca
arXiv:2605.01130v1 Announce Type: new Abstract: If a model has some behavioral tendency, such as sycophancy or misalignment, and it is trained on its own outputs, will the tendency be amplified in the
arXiv:2602.00594v2 Announce Type: replace Abstract: A good language model starts with a good tokenizer. Tokenization is especially important for speech modeling, which must handle continuous signals t
arXiv:2512.22317v2 Announce Type: replace-cross Abstract: Short-term precipitation nowcasting is an inherently uncertain and under-constrained spatiotemporal forecasting problem, especially for rapidl
arXiv:2605.04035v1 Announce Type: new Abstract: We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method em
arXiv:2605.03462v1 Announce Type: new Abstract: A fundamental role in decoding human motor intent and enabling intuitive human-computer interaction is played by electromyography (EMG). However, its ge
arXiv:2605.03059v1 Announce Type: new Abstract: Medical experts often manually segment images to obtain diagnostic statistics and discard the resulting annotations. We aim to train segmentation models
arXiv:2605.03413v1 Announce Type: new Abstract: What does it mean to understand the world? Contemporary world models often operationalize understanding as accurate future prediction in latent or obser
arXiv:2605.01164v1 Announce Type: new Abstract: This position paper argues that LLMs should not yet be credited with decision explanation. This matters because recent work increasingly treats accurate
arXiv:2511.01680v3 Announce Type: replace-cross Abstract: Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.g., estimating descriptive statistics
arXiv:2601.06543v2 Announce Type: replace Abstract: Queueing simulation studies often require substantial manual effort to translate conceptual system descriptions into executable programs and to veri
arXiv:2605.03343v1 Announce Type: new Abstract: Medical image super-resolution (MedSR) is essential for improving diagnostic precision across diverse imaging modalities such as MRI, CT, X-ray, Ultraso
arXiv:2605.02199v1 Announce Type: new Abstract: Long-term LLM agents must compress streams of past interactions into persistent memory before future queries are known. Existing evaluations usually mea
arXiv:2605.03866v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forg
arXiv:2605.03482v1 Announce Type: cross Abstract: Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We for
arXiv:2506.23566v2 Announce Type: replace Abstract: The acquisition of high-resolution satellite imagery is often constrained by the spatial and temporal limitations of satellite sensors, as well as t
arXiv:2603.06194v2 Announce Type: replace Abstract: Reinforcement learning (RL) for large language models (LLMs) has shown strong performance in single-turn tasks, but extending it to multi-turn inter
arXiv:2605.02918v1 Announce Type: new Abstract: Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervi
arXiv:2605.03432v1 Announce Type: new Abstract: Magnetic Resonance Imaging (MRI) acquisition remains a time-intensive and patient-straining process, as prolonged scan dura- tions increase the likeliho
arXiv:2506.01982v3 Announce Type: replace-cross Abstract: This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influ
arXiv:2605.01189v1 Announce Type: new Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transpar
arXiv:2605.03144v1 Announce Type: new Abstract: In computational pathology, nuclear instance segmentation is a fundamental task with many downstream clinical applications. With the advent of deep lear
arXiv:2605.03869v1 Announce Type: new Abstract: We investigate the effectiveness of adaptive zeroth-order (ZO) optimization for memory-constrained fine-tuning of large language models (LLMs). Contrary
arXiv:2605.03839v1 Announce Type: cross Abstract: We study the problem of approximating the total variation distance between two mixtures of product distributions over an n-dimensional discrete domain
arXiv:2605.02907v1 Announce Type: new Abstract: Softmax attention maps every query--key interaction into a probability distribution, but the underlying structure remains largely unexplored. We define
arXiv:2605.02255v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While at
arXiv:2605.02045v1 Announce Type: cross Abstract: Using reinforcement learning, we optimize for practical hardware constraints, including limited FIR filter taps at the transmitter and receiver, mean
arXiv:2508.17466v3 Announce Type: replace-cross Abstract: This paper presents a deep learning framework designed to enhance the grasping capabilities of quadrupeds equipped with arms, with a focus on
arXiv:2605.03252v1 Announce Type: cross Abstract: LoRA fine-tuning of diffusion transformers (DiT) on multi-style data suffers from style bleed: a single low-rank residual cannot represent several dis
Over 10 million Americans will lose health care coverage because of Republican cuts to Medicaid and Obamacare subsidies. That amounts to half of the total number of people who gained health insurance
arXiv:2605.03520v1 Announce Type: cross Abstract: We propose a neural parameterization of convex sets by learning sublinear (positively homogeneous and convex) functions. Our networks implicitly repre
arXiv:2408.01914v4 Announce Type: replace-cross Abstract: Several forms for constructing novel physics-informed neural-networks (PINN) for the solution of partial-differential-algebraic equations base
arXiv:2605.03267v1 Announce Type: cross Abstract: Causality is a central topic in scientific inquiry, yet for complex systems, the identification and analysis of synergistic causation remain a challen
arXiv:2605.03268v1 Announce Type: new Abstract: Here we introduce Partially Observed Structural Causal Models (POSCMs) that formalize causal systems where latent contexts co-determine both the interac
arXiv:2605.03548v1 Announce Type: new Abstract: Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are tra
arXiv:2603.20562v2 Announce Type: replace Abstract: Large language models (LLMs) are now widely used as judges, yet their decisions can change under presentation choices that should be irrelevant. We
arXiv:2605.03929v1 Announce Type: cross Abstract: Stem retrieval, the task of matching missing stems to a given audio submix, is a key challenge currently limited by models that discard temporal infor