Knowledge catalogue
arXiv:2610.14730v1 Announce Type: cross Abstract: Adapting embedding models to new tasks requires training data that reflect task-specific information needs and relevance criteria. However, manual dat
arXiv:1307.2965v3 Announce Type: replace Abstract: The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilag
arXiv:2610.15467v1 Announce Type: new Abstract: Image-computable low-level image saliency has shaped perceptual psychology and computer science, but is limited by its inability to capture human high-l
arXiv:2610.15515v1 Announce Type: new Abstract: Adaptive image tokenizers compress an image into a variable number of tokens matched to its complexity, and recent work makes this allocation controllab
arXiv:2610.13999v1 Announce Type: new Abstract: Rule decisions such as content moderation, advertising compliance, and eligibility checks combine several conditions, exemptions, and missing-informatio
arXiv:2610.15156v1 Announce Type: new Abstract: Distribution-system state estimation is a growing necessity for the modern power grid as the addition of local storage and generation add system complex
arXiv:2610.16878v1 Announce Type: new Abstract: Autoregressive electronic health record (EHR) foundation models predict the next event token in patient trajectories: sequences of structured medical ev
arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor
arXiv:2610.14478v1 Announce Type: new Abstract: Omnidirectional aerial robots offer full 6-DoF independent control over position and orientation, making them popular for aerial manipulation. Although
arXiv:2610.13997v1 Announce Type: new Abstract: Concept representations in diffusion models drift across network depth and denoising time, yet existing sparse autoencoders offer only per-block or per-
arXiv:2610.15854v1 Announce Type: new Abstract: Grounded spatial reasoning about indoor scenes rests on a geometric reconstruction that the multimodal system does not itself produce. Feed-forward mult
arXiv:2507.23449v3 Announce Type: replace Abstract: One-class learning is an effective methodology for time series anomaly detection, yet kernel one-class approaches often compare sequences in a point
arXiv:2610.17534v1 Announce Type: new Abstract: Actuator sizing is an early and consequential decision: joint torque and velocity specifications fix the motors, gearboxes and brakes before any CAD mod
arXiv:2610.14011v1 Announce Type: new Abstract: Modern experiments increasingly produce rich post-treatment objects---such as text, images, and video---that may provide early surrogate signals for out
arXiv:2604.01477v2 Announce Type: replace Abstract: Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet existing MPC-RL frameworks commonly rely on gradie
arXiv:2610.15968v1 Announce Type: new Abstract: Spiking Transformers achieve energy efficiency by discarding softmax normalization in favor of thresholded, binary attention. However, this removal brea
arXiv:2610.18486v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significan
arXiv:2610.17226v1 Announce Type: cross Abstract: Most machine-learning interatomic potentials (MLIPs) are trained on gas-phase data, which limits their use for chemistry in solution. Here, we introdu
arXiv:2610.14671v1 Announce Type: new Abstract: Streaming video understanding requires answering questions from the observed video, with evidence drawn from recent observations or earlier events. Hist
arXiv:2610.15660v1 Announce Type: new Abstract: Domain generalised semantic segmentation (DGSS) has recently benefited from combining multiple foundation models, such as vision-language and vision fou
arXiv:2610.15039v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models combine specialized experts through an input-dependent routing mechanism, and their sparse variants underpin many modern
arXiv:2610.14512v1 Announce Type: new Abstract: Objective. To evaluate whether spatial context and temporal modeling improve tissue recognition during optical trocar entry, where multiple abdominal-wa
arXiv:2610.16264v1 Announce Type: new Abstract: Electroencephalography (EEG) is a non-invasive, scalable source of quantitative biomarkers for the early diagnosis of brain disorders, and deep represen
arXiv:2610.16533v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is reshaping how large language models (LLMs) acquire reasoning capabilities, yet the optimizers
arXiv:2302.00985v3 Announce Type: replace-cross Abstract: We consider online scheduling on unrelated (heterogeneous) machines in a speed-oblivious setting, where an algorithm is unaware of the exact j
arXiv:2610.17029v1 Announce Type: new Abstract: Neural operator splitting composes a library of neural operators, one per physical mechanism, into zero-shot simulators of new equations. Existing appro
arXiv:2610.17904v1 Announce Type: new Abstract: Continual learning (CL) adapts pretrained models to a sequence of tasks while retaining knowledge acquired from earlier tasks. When task data are sensit
arXiv:2610.16816v1 Announce Type: cross Abstract: Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based a
arXiv:2610.18811v1 Announce Type: new Abstract: In this work, we study grokking in a simple, analytically tractable setting whose rich phenomenology allows us to disentangle the effects of initializat
arXiv:2610.18495v1 Announce Type: cross Abstract: In order to execute quantum algorithms on bosonic quantum computers, it is necessary to encode their inputs into a desired target quantum state, with
arXiv:2610.15341v1 Announce Type: new Abstract: The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders oft
arXiv:2610.17483v1 Announce Type: new Abstract: Machine learning surrogates of partial differential equations (PDEs) are commonly trained on data generated by numerical solvers. Therefore, the trainin
arXiv:2610.16451v1 Announce Type: new Abstract: Fine-tuning large language models at scale is increasingly limited by the memory cost of backpropagation. Zeroth-Order (ZO) optimizers reduce this burde
arXiv:2610.18005v1 Announce Type: new Abstract: Surface reconstruction from range scans is challenging in the presence of noise and outliers, as fidelity to observations must be balanced against sensi
arXiv:2610.15645v1 Announce Type: new Abstract: Surgical world models must turn prospective robot actions into visually faithful, structurally consistent instrument motion. We introduce SurgCast, whic
arXiv:2610.17692v1 Announce Type: new Abstract: 3D scene graph prediction is commonly optimized with object-node and relation-edge classification losses. These objectives encourage exact object and re
arXiv:2610.16990v1 Announce Type: new Abstract: Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. Tabu
arXiv:2610.17448v1 Announce Type: new Abstract: Gaps, skips, and mistakes are the normal state of a historical register: rows go missing, tables cross page breaks, columns slip under wrong names, and
arXiv:2610.14651v1 Announce Type: new Abstract: The recent Segment Anything Model 3 (SAM3) extends powerful promptable segmentation from spatial cues to concepts, raising the prospect of a single foun
arXiv:2610.14626v1 Announce Type: new Abstract: Mixed-variable optimization in scientific discovery often requires exploring large combinatorial spaces under costly experimental evaluations, making sa
arXiv:2610.16429v1 Announce Type: cross Abstract: We consider inverse problems regularized by a trained neural network with convex activations and unrestricted weights. The resulting optimization prob
arXiv:2610.17069v1 Announce Type: new Abstract: In Nonlinear Model Predictive Control (NMPC), the control law is implicitly determined by the solution of an Optimal Control Problem (OCP). Consequently
arXiv:2403.01079v2 Announce Type: replace Abstract: We study the challenging problem for inference tasks on large-scale graph datasets of Graph Neural Networks: huge time and memory consumption, and t
arXiv:2610.16465v1 Announce Type: new Abstract: Vision-language models answer questions about images they were never shown, a failure called mirage. Such an answer can be correct without being justifi
arXiv:2610.14105v1 Announce Type: cross Abstract: Accurate artery extraction from X-ray coronary angiography (XCA) is essential for coronary artery disease diagnosis and intervention. However, low ves
arXiv:2210.01705v3 Announce Type: replace-cross Abstract: Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machi
arXiv:2610.14896v1 Announce Type: new Abstract: Humanoid Behavior Foundation Models (BFMs) can execute impressive motions, but downstream applications such as loco-manipulation demand high whole-body
arXiv:2610.15659v1 Announce Type: new Abstract: Centrality measures rank nodes even when the observed differences can be explained by ordinary structural variation. We therefore study a preceding ques
arXiv:2610.14663v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) has emerged as a powerful dynamic scene representation, supporting real-time high quality rendering but suffers from prohib
arXiv:2610.17908v1 Announce Type: cross Abstract: In manga translation, visual context outside speech-bubble text, including speaker identities, referents, and scene tone, is depicted in page images,
arXiv:2610.17655v1 Announce Type: cross Abstract: Watermarking embeds an imperceptible signal in text from large language models that is statistically detectable with a secret key. The recent textGrai
arXiv:2610.14281v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in image super-resolution (SR) by leveraging strong generative priors to enhance perceptual quality. H
arXiv:2610.14657v1 Announce Type: new Abstract: Can autoregressive video diffusion be accelerated further when only four denoising steps remain? Existing feature caching approaches struggle in this re
arXiv:2610.17444v1 Announce Type: new Abstract: Language model (LM) agents can fail for different reasons, requiring different explanations and interventions. We study how state beliefs, action values
arXiv:2610.16828v1 Announce Type: new Abstract: When does a shared representation become insufficient for jointly supporting multiple tasks? This question lies at the heart of multi-task learning mode
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and finetuning a base unimodal model for bi-modal generation. Diverging f
arXiv:2610.18104v1 Announce Type: new Abstract: Hybrid attention is rapidly becoming the dominant architectural choice for frontier models. Despite their successes, such architectures complicate model
arXiv:2404.14855v2 Announce Type: replace Abstract: We characterize the geometry of the set of all weight vectors for which a fully-connected linear neural network computes the same linear transformat
arXiv:2610.16565v1 Announce Type: new Abstract: Human gaze is the primary high-bandwidth interface through which we parse the semantics of our environment. While often modeled as a sequence, the degre
arXiv:2610.16749v1 Announce Type: new Abstract: Recent foundation models have drastically improved the generalization of monocular relative depth estimation, yet recovering accurate metric depth from