Knowledge catalogue
arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re
arXiv:2610.17066v1 Announce Type: new Abstract: Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to matc
arXiv:2607.09020v2 Announce Type: replace-cross Abstract: Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic
arXiv:2610.18485v1 Announce Type: new Abstract: Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio
arXiv:2610.14014v1 Announce Type: new Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely da
arXiv:2610.14028v1 Announce Type: new Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse tasks.
arXiv:2604.04690v3 Announce Type: replace Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setup
arXiv:2610.16066v1 Announce Type: new Abstract: Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target
arXiv:2607.15655v2 Announce Type: replace-cross Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternativ
arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets
arXiv:2610.17605v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) codecs report reconstruction quality after compression, and give users little control over the distortion of indiv
arXiv:2605.24273v2 Announce Type: replace Abstract: Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantifica
arXiv:2509.19774v3 Announce Type: replace Abstract: Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the
arXiv:2610.16809v1 Announce Type: cross Abstract: This study addresses the prediction of audio popularity on an online sharing platform by leveraging both metadata and acoustic feature representations
arXiv:2610.16983v1 Announce Type: cross Abstract: Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which c
arXiv:2610.14904v1 Announce Type: new Abstract: We study prediction with expert advice over T rounds with K experts under heavy-tailed losses whose (1+epsilon)-moment is bounded by heta>0 for some eps
arXiv:2610.15709v1 Announce Type: new Abstract: Linear attention and recurrent models compress past tokens into a fixed-size state that is updated at every step. However, storing these states in high
arXiv:2610.15983v1 Announce Type: new Abstract: In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flo
arXiv:2610.14942v1 Announce Type: new Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem
arXiv:2610.16327v1 Announce Type: new Abstract: Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensi
arXiv:2601.13155v3 Announce Type: replace-cross Abstract: Long-context inference enhances the reasoning capability of Large Language Models (LLMs), but incurs significant computational overhead. Token
arXiv:2610.16287v1 Announce Type: new Abstract: Automated visual inspection is essential for efficient repair and recycling of metallic components. AI-based defect detection is a strong fit. However,
arXiv:2610.16134v1 Announce Type: new Abstract: Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples. For skeleton-based action recognitio
arXiv:2610.14654v1 Announce Type: new Abstract: Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.
arXiv:2610.15286v1 Announce Type: new Abstract: The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the t
arXiv:2610.17265v1 Announce Type: new Abstract: Federated learning allows hospitals to train a shared mortality model without pooling their records, but published comparisons of aggregation strategies
arXiv:2610.17936v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) provides an efficient framework for dynamic scene reconstruction. Most existing approaches model scene dynamics wit
arXiv:2610.14840v1 Announce Type: new Abstract: We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggre
arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe
arXiv:2610.16390v1 Announce Type: cross Abstract: A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining ex
arXiv:2610.17829v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Ne
arXiv:2511.05124v2 Announce Type: replace Abstract: Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved int
arXiv:2604.07379v2 Announce Type: replace-cross Abstract: From butterfly wings to remnants of nuclear detonation, aperiodic order repeatedly emerges in nature, often exhibiting reduced sensitivity to
arXiv:2610.16851v1 Announce Type: new Abstract: Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches ofte
arXiv:2610.15101v1 Announce Type: new Abstract: Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this me
arXiv:2610.14751v1 Announce Type: new Abstract: Random Fourier features approximate a shift-invariant kernel in R^d, such as the Gaussian kernel exp(-frac12|x-y|_2^2), using independent samples from i
arXiv:2610.18164v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) fundamentally lack the ability to capture structural properties defined by node self-interactions, such as cycle counts, su
arXiv:2610.17971v1 Announce Type: cross Abstract: The semantic entropy of a language model is the entropy of the meanings of its sampled answers. The meanings are never observed, and the entropy is es
arXiv:2506.19549v2 Announce Type: replace-cross Abstract: Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large a
arXiv:2406.07169v3 Announce Type: replace Abstract: Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion method
arXiv:2610.18462v1 Announce Type: new Abstract: Readout scaling changes what a sparse autoencoder (SAE) learns, but the benefit of removing it depends on the data. We use readout-canonicalized decisio
arXiv:2610.18488v1 Announce Type: new Abstract: Many related prediction problems, which we call questions, come with few labels each. What information can be shared across them, and what is actually n
arXiv:2610.16409v1 Announce Type: new Abstract: Repetitive structures can give an incorrect pose candidate more inlier support than an accurate one, causing visual localization to select it. History-G
arXiv:2610.18388v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when t
arXiv:2610.14205v1 Announce Type: new Abstract: Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient
arXiv:2610.16248v1 Announce Type: new Abstract: We study sampling from pi(x)proptoe^{-V(x)} on R^d, where V is alpha-strongly convex and eta-smooth with condition number kappa=eta/alpha. For this prob
arXiv:2610.16082v1 Announce Type: new Abstract: Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions
arXiv:2610.15079v1 Announce Type: new Abstract: Squeeze-and-Excitation (SE) modules improve convolutional networks by modeling channel-wise dependencies, but global average pooling collapses spatial s
arXiv:2610.17623v1 Announce Type: new Abstract: Rubric-based reinforcement learning grades a response against many written criteria but collapses them into one reward, which discards the detail the ru
arXiv:2610.18292v1 Announce Type: new Abstract: Kernel Mean Matching (KMM) estimates risk under covariate shift by reweighting labelled source samples so that their kernel mean embedding matches that
arXiv:2610.16022v1 Announce Type: new Abstract: Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipe
arXiv:2610.16427v1 Announce Type: new Abstract: Recursive reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and
arXiv:2610.16347v1 Announce Type: new Abstract: Forward-Forward learning replaces end-to-end backpropagation with local, layer-wise training objectives. Its variants are evaluated primarily by classif
arXiv:2610.14757v1 Announce Type: cross Abstract: Human-in-the-loop optimization refers to systems that present choices to a human decision-maker and query their judgment to optimize system parameters
arXiv:2610.15005v1 Announce Type: new Abstract: Transformer architectures have achieved strong results in natural language processing and computer vision, but their deployment is limited by high compu
arXiv:2610.18139v1 Announce Type: new Abstract: We study causal error-feedback coefficients as design variables for model quantization. Modeling instantaneous quantization errors as independent, zero-
arXiv:2610.14269v1 Announce Type: new Abstract: Humanoid face robots can now move their lips in sync with speech and show detailed facial expressions. Give such a robot the face of a real person and d
arXiv:2610.15761v1 Announce Type: cross Abstract: For languages with limited resources, Sentiment Analysis remains a challenging problem due to limited annotated data and strong domain variability acr
arXiv:2610.14030v1 Announce Type: new Abstract: Looped transformers reuse one shared block across many recurrent steps, so depth becomes a quantity that can be spent at runtime rather than a stack of
arXiv:2610.15742v1 Announce Type: new Abstract: Scene classification of remote sensing imagery with multimodal large language models (MLLMs) is formulated as a single-forward-pass decision problem: ca