Knowledge catalogue
arXiv:2608.25828v1 Announce Type: new Abstract: We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude
arXiv:2608.25898v1 Announce Type: new Abstract: Forecasting a stochastic dynamical system rarely means a single number: one wants several observables---future state, threshold event, regime label---ea
arXiv:2608.24894v1 Announce Type: cross Abstract: The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price beh
arXiv:2509.24209v2 Announce Type: replace Abstract: Instant reconstruction of dynamic 3D humans from uncalibrated sparse-view videos is critical for numerous downstream applications. Existing methods,
arXiv:2608.25926v1 Announce Type: cross Abstract: Air traffic control procedures are executed through spoken exchanges between controllers and pilots. These interactions are essential to the safety of
arXiv:2512.17021v2 Announce Type: replace Abstract: Current large-scale satellite-based forest disturbance monitoring systems operate at 10-30~m resolution, too coarse to detect changes at the scale o
arXiv:2608.25981v1 Announce Type: new Abstract: Subgroup performance differences are the standard evidence for fairness bias in medical imaging, and the usual response removes the demographic informat
arXiv:2608.25604v1 Announce Type: new Abstract: Accurate gust forecasting under typhoon conditions remains challenging due to the highly non-stationary and multi-scale characteristics of extreme wind
arXiv:2608.25243v1 Announce Type: new Abstract: Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fi
arXiv:2601.04632v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance on many tasks, but their progress remains uneven across languages and cultures, often reflec
arXiv:2608.25894v1 Announce Type: new Abstract: Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon w
arXiv:2608.25605v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive doma
arXiv:2608.24979v1 Announce Type: cross Abstract: Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated progr
arXiv:2608.25468v1 Announce Type: cross Abstract: Functional data analysis is an important statistical field that treats data as random functions. In practice, the random functions are often not fully
arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Mul
arXiv:2608.25158v1 Announce Type: cross Abstract: Evaluating the ability of large language models (LLMs) to discover software bugs is increasingly important. Existing benchmarks typically evaluate thi
arXiv:2608.26074v1 Announce Type: new Abstract: Intent misinterpretation during vehicle interactions causes recurring planning failures. We study a decision layer in which a language-guided intent mod
arXiv:2608.25483v1 Announce Type: new Abstract: The underwater environment is challenging for 3D reconstruction, because particles suspended in the water scatter and diffuse light, turbidity varies, a
arXiv:2608.25659v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have advanced language-conditioned robotic manipulation, yet action-imitation objectives provide only weak supervi
arXiv:2608.24959v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models encode visual observations as flat 2D patch tokens that carry no intrinsic geometric structure, and augmenting the
arXiv:2601.07752v4 Announce Type: replace-cross Abstract: Estimating the Riesz representer is central to debiased machine learning, yet the generator and representer model determine which regression d
arXiv:2608.25284v1 Announce Type: cross Abstract: Physical human-robot collaboration requires a robot to provide assistance when human intention is clear while remaining compliant when several future
arXiv:2501.05458v3 Announce Type: replace-cross Abstract: We organize the generative-modeling literature around three classes of generators, corresponding to three distinct inferential tasks: estimati
arXiv:2608.25574v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based
arXiv:2608.25807v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate edge functions, making the choice of edge pa
arXiv:2608.25375v1 Announce Type: cross Abstract: Generative vision-language models (VLMs) are increasingly used in human-centered settings, yet they can produce demographically biased outputs even wh
arXiv:2504.07742v4 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-b
arXiv:2608.25334v1 Announce Type: new Abstract: Diffusion-based real-world image super-resolution (SR) achieves impressive perceptual quality but inherently suffers from severe texture hallucination.
arXiv:2608.25116v1 Announce Type: new Abstract: Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learning and scientific discovery. While local
arXiv:2608.24936v1 Announce Type: cross Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal
arXiv:2601.16905v3 Announce Type: replace Abstract: Machine unlearning in Mixture-of-Experts (MoE) large language models presents a critical yet under-explored challenge. Current unlearning methods ap
arXiv:2608.25583v1 Announce Type: new Abstract: Reasoning-oriented large language models often achieve strong problem-solving performance by generating long chains of thought, but this behavior substa
arXiv:2608.25276v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert sub-networks through
arXiv:2608.26069v1 Announce Type: new Abstract: Large-kernel Convolutional Neural Networks (CNNs) deliver remarkable performance in vision tasks by significantly expanding receptive fields, yet their
arXiv:2601.13207v2 Announce Type: replace Abstract: Geo-localization aims to infer the geographic location where an image was captured using observable visual evidence. Traditional methods achieve imp
arXiv:2608.25343v1 Announce Type: new Abstract: Chinese query correction (CQC) is important for search and query recommendation on content platforms, but supervised methods rely on large annotated cor
arXiv:2608.24971v1 Announce Type: cross Abstract: We study the Lipschitz stability of attention retrieval in hyperbolic spaces. Existing methods lack deterministic guarantees on attention-weight prese
arXiv:2608.25071v1 Announce Type: new Abstract: General-purpose health benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, maki
arXiv:2608.25261v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models combine specialized predictors under learned routing, offering a principled mechanism for leveraging heterogeneity in
arXiv:2608.25873v1 Announce Type: new Abstract: In federated learning, it is well known that heterogeneous data can (in theory) slow down optimization, and much effort has been directed at designing o
arXiv:2608.26052v1 Announce Type: cross Abstract: Choosing the rank of a low-rank adaptation (LoRA) update is usually an empirical task. In this paper, we provide a task-dependent theory of the approx
arXiv:2608.25934v1 Announce Type: cross Abstract: Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a
arXiv:2507.04167v2 Announce Type: replace Abstract: Rising labor costs and narrow treatment windows have made teleoperated robots a proposed tool for vineyard scouting and treatment, but simulation-ba
arXiv:2608.25199v1 Announce Type: new Abstract: We study a tree-structured regularizer over class-prototype layouts in a hierarchical-classification model and ask whether the choice of latent manifold
arXiv:2608.26083v1 Announce Type: cross Abstract: Deep neural networks often exploit spurious associations in their training data, a failure known as shortcut learning. Concept-based explainability me
arXiv:2509.02855v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of i
arXiv:2601.01446v2 Announce Type: replace Abstract: Counterfactual examples are minimal edits to an input that alter a model's prediction. They are widely employed in explainable AI to probe model beh
arXiv:2608.26009v1 Announce Type: cross Abstract: An automated theorem prover builds a proof step by step, choosing at each point what to add and what to remove. We cast this construction as a policy
arXiv:2608.25052v1 Announce Type: cross Abstract: Hessian-free high-resolution (HFHR) dynamics augments underdamped Langevin dynamics (ULD) with reversible position diffusion for sampling problems tha
arXiv:2608.25109v1 Announce Type: cross Abstract: Whole-heart segmentation from CT and MRI is essential for quantitative cardiac image analysis, but remains challenging under multi-center and multi-mo
arXiv:2608.25586v1 Announce Type: new Abstract: Hierarchical clustering produces ultrametric representations that impose strong global geometric constraints and may distort local similarities in ways
arXiv:2409.18073v2 Announce Type: replace-cross Abstract: For AI agents to be helpful to humans, they should be able to follow natural language instructions to complete everyday cooperative tasks in h
arXiv:2608.25291v1 Announce Type: new Abstract: Symbolic regression (SR) seeks to discover parsimonious mathematical laws from observational data, yet conventional approaches often struggle with the v
arXiv:2608.25734v1 Announce Type: new Abstract: Co-speech gesture generation has made significant progress toward realistic full-body motion from speaker audio, yet existing models lack fine-grained s
arXiv:2608.25548v1 Announce Type: new Abstract: Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical represent
arXiv:2608.25723v1 Announce Type: new Abstract: Time is of the essence when dealing with multiple reward signals and non-linear utility. In this paper we argue that the current main approaches in mult
arXiv:2608.25593v1 Announce Type: new Abstract: Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/s
arXiv:2608.25443v1 Announce Type: new Abstract: Efficient determination of the effective multiplication factor (keff) is an important computational task in reactor core neutronics analysis. Physics-in
arXiv:2202.12093v2 Announce Type: replace Abstract: Though some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks
arXiv:2608.25854v1 Announce Type: new Abstract: Key Point Analysis (KPA) aims to identify a concise set of key points that summarize a collection of arguments together with their prevalence. We argue