Knowledge catalogue
arXiv:2610.12801v1 Announce Type: cross Abstract: Approximating the effective anisotropic hyperelastic response of a representative volume element (RVE) requires constitutive expressiveness, mechanica
arXiv:2610.13171v1 Announce Type: new Abstract: Power sampling improves large language model (LLM) reasoning at test time by sampling from a sharpened version of the base model's distribution. Its sam
arXiv:2610.13660v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) is increasingly used to evaluate whether neural networks can infer task-specific transformations from few d
arXiv:2610.12699v1 Announce Type: new Abstract: We consider the problem of learning to generate data while satisfying a prescribed sample-wise constraint with a user-specified probability of constrain
arXiv:2610.13296v1 Announce Type: cross Abstract: High-precision retrieval over external knowledge bases is essential for LLM agents to operate effectively. However, existing Information Retrieval (IR
arXiv:2610.12642v1 Announce Type: new Abstract: Motion retargeting is a critical preprocessing step for transferring human motion data to humanoid robots. While existing methods are effective for sing
arXiv:2610.13588v1 Announce Type: cross Abstract: Uncertainty quantification for electric vehicle (EV) charging demand is essential to turn the increasing pressure of EV adoption on power systems into
arXiv:2610.12702v1 Announce Type: cross Abstract: Aggregating independently computed sub-sample inferences is a well-known challenge in large-scale Bayesian computation, federated learning, and opinio
arXiv:2410.01917v3 Announce Type: replace-cross Abstract: Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to at
arXiv:2610.12766v1 Announce Type: new Abstract: Machine-learning models for perovskite solar cells are typically evaluated using random data splits, which we show systematically leak information throu
arXiv:2610.13568v1 Announce Type: new Abstract: We present Rationally Linearized Multilayer Perceptrons(RaLiMPs), a new approach to efficiently linearizing MLPs using low-degree rational approximation
arXiv:2610.12769v1 Announce Type: cross Abstract: Unlearning in large reasoning models (LRMs) requires removing forget-set information from both the reasoning trace and the final answer. Existing appr
arXiv:2506.11849v3 Announce Type: replace-cross Abstract: With origins in game theory, probabilistic values like Shapley values, Banzhaf values, and semi-values have emerged as a central tool in expla
arXiv:2610.13778v1 Announce Type: cross Abstract: State-space models (SSMs) maintain a fixed-size recurrent state, but reading and writing it at every decoding step can bottleneck hybrid models. This
arXiv:2610.13678v1 Announce Type: new Abstract: Prior work suggests fraudulent scientists write their articles with higher rates of linguistic obfuscation than genuine scientists, with a possible mech
arXiv:2610.13841v1 Announce Type: new Abstract: Coding agents often make useful progress before they fail, but retry-from-scratch discards the machine state created along the way. This makes failure r
arXiv:2610.13909v1 Announce Type: cross Abstract: While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression ta
arXiv:2610.12670v1 Announce Type: new Abstract: We consider planning for unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) teams in aerial monitoring missions with ordering requirements
arXiv:2610.12865v1 Announce Type: new Abstract: Existing convergence guarantees for non-log-concave sampling with stochastic gradient Langevin Monte Carlo (SG-LMC) typically assume globally Lipschitz
arXiv:2610.13527v1 Announce Type: cross Abstract: Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly underst
arXiv:2610.13823v1 Announce Type: cross Abstract: Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executi
arXiv:2610.12566v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization offers a memory-efficient alternative to backpropagation for fine-tuning large language models. However, existing ZO meth
arXiv:2610.13656v1 Announce Type: cross Abstract: Latent field models are essential for spatial statistics, but the posterior distribution of the latent field, even with a Gaussian process (GP) prior,
arXiv:2610.12986v1 Announce Type: cross Abstract: Continuous-time latent state-space models are widely used for irregularly observed time series, typically representing uncertainty through a random in
arXiv:2610.12636v1 Announce Type: new Abstract: Long, slender instruments are essential for minimally invasive surgery (MIS), but achieving precise distal manipulation becomes increasingly difficult w
arXiv:2601.18901v2 Announce Type: replace Abstract: Knowledge probing quantifies how much relational knowledge a language model (LM) has acquired during pre-training. Existing knowledge probes evaluat
arXiv:2610.13819v1 Announce Type: new Abstract: In clinical practice, experts often begin by converting raw inputs into a set of expert features before making a decision. While black-box models traine
arXiv:2610.13766v1 Announce Type: cross Abstract: Geospatial foundation models can support disaster damage assessment, but high accuracy on familiar disasters may hide shortcut learning: prediction fr
arXiv:2610.13614v1 Announce Type: cross Abstract: We discuss three simple connections between generative diffusion and optimal transport. First, the joint law of the data and of their Ornstein-Uhlenbe
arXiv:2610.13300v1 Announce Type: cross Abstract: Modeling human reactions to spatial audio requires understanding both what is heard and where it comes from. Prior work generates motion offline from
arXiv:2610.13051v1 Announce Type: cross Abstract: Some backdoor attacks never touch an image. FLIP only changes labels: on CIFAR-10, flipping 3% of them makes a ResNet-32 send 99.7% of triggered sourc
arXiv:2610.12532v1 Announce Type: cross Abstract: Generative creative interfaces routinely report the user's position within a space of possibilities. Such displays imply that the quantities shown wer
arXiv:2610.13840v1 Announce Type: new Abstract: Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars
arXiv:2510.07832v2 Announce Type: replace-cross Abstract: Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improv
arXiv:2610.13081v1 Announce Type: new Abstract: Recent work on large audio-language models (LALMs) explores adaptive reasoning to balance accuracy and efficiency. However, existing approaches often re
arXiv:2610.12790v1 Announce Type: new Abstract: Reliable robotic manipulation requires maintaining a firm grasp while strictly avoiding both contact slip and structural damage to the object. Satisfyin
arXiv:2610.13034v1 Announce Type: new Abstract: Trajectory extrapolation from a partial target trajectory is underdetermined, but completed related trajectories provide information about the unresolve
arXiv:2610.12921v1 Announce Type: new Abstract: Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as
arXiv:2610.13221v1 Announce Type: new Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets
arXiv:2610.13348v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to answer questions over long interaction histories, where relevant evidence may be scattered across distant sessi
arXiv:2610.12981v1 Announce Type: cross Abstract: Visual interventions provide a training-free way to reduce hallucinations in multimodal large language models. However, an intervention may have littl
arXiv:2610.12809v1 Announce Type: new Abstract: Continuous random node features increase the expressivity of message-passing neural networks (MPNNs), but existing theoretical analyses are not fine-gra
arXiv:2610.12864v1 Announce Type: new Abstract: Existing sequence models share a critical limitation: they lack an explicit, interpretable mechanism for content-aware temporal recall. LSTMs compress e
arXiv:2610.12697v1 Announce Type: cross Abstract: This paper studies an open network design problem of constructing graph topologies with maximum algebraic connectivity under fixed numbers of vertices
arXiv:2508.00525v2 Announce Type: replace-cross Abstract: A classic account of the quantification of semantic information is that of Bar-Hiller and Carnap. Their account proposes an inverse relation b
arXiv:2610.13688v1 Announce Type: new Abstract: Harvesting technologies have evolved from conventional mechanized systems based on bulk mechanical interaction toward increasingly automated and robotic
arXiv:2610.12845v1 Announce Type: new Abstract: Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal defini
arXiv:2610.13456v1 Announce Type: new Abstract: Cyber-Physical Systems are developed through multiple model based activities, comprising several stages of development. As the development progresses th
arXiv:2610.13077v1 Announce Type: new Abstract: Urban environments present significant challenges for the perception systems of automated driving due to their high complexity, characterized by dense t
arXiv:2610.13398v1 Announce Type: new Abstract: Tibetan, spoken by some seven million people on and around the Qinghai-Xizang Plateau, remains underrepresented in natural language processing (NLP). We
arXiv:2610.13617v1 Announce Type: new Abstract: User simulators are gaining attention as environments to train LLM assistants that can better cope with the nuances of multi-turn conversations with rea
arXiv:2610.12850v1 Announce Type: cross Abstract: Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows. We consider learning
arXiv:2610.13341v1 Announce Type: new Abstract: Mamba-3 introduces input-dependent rotations into recurrent state updates, but their contribution beyond state retention remains unclear. We interpret t
arXiv:2412.06534v5 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promisi
arXiv:2610.13211v1 Announce Type: cross Abstract: Generative machine learning has shown remarkable results across a wide range of interdisciplinary tasks, motivating significant efforts toward Quantum
arXiv:2610.12761v1 Announce Type: new Abstract: Foundation models are increasingly used for high-level multi-robot task planning, yet independently generated symbolic plans can become jointly infeasib
arXiv:2610.13145v1 Announce Type: new Abstract: Large language models (LLMs) increasingly generate structured layouts and executable scene programs from natural-language instructions. However, executa
arXiv:2610.13608v1 Announce Type: cross Abstract: Vocal intensity conversion renders a recorded utterance at a different vocal intensity, rather than simply scaling its amplitude, while preserving its
arXiv:2610.13186v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without exposing raw data, yet remains vulnerable to malicious clients injecting poisoned d
arXiv:2610.13024v1 Announce Type: cross Abstract: World Action Models (WAMs) inherit powerful priors from pretrained video generation models, but their variational autoencoder (VAE) representations ar