Knowledge catalogue
arXiv:2510.02453v3 Announce Type: replace-cross Abstract: Frontier language models are deployed as black-box services, where model weights cannot be modified and customization is limited to prompting.
arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed
arXiv:2605.15238v1 Announce Type: cross Abstract: Large language models are increasingly used for code generation, but many generated programs fail to compile, a prerequisite for further correctness c
arXiv:2605.15741v1 Announce Type: new Abstract: Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental 'granularity dilemma': captu
arXiv:2605.16211v1 Announce Type: new Abstract: Traditional scientific modeling typically begins with fixed, instance-wise effective equations and then carries out equation-specific analysis and compu
arXiv:2605.15224v1 Announce Type: new Abstract: Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed,
arXiv:2602.21536v2 Announce Type: replace Abstract: Retrospective MRI harmonization is limited by poor scalability across modalities and reliance on traveling subject datasets. To address these challe
arXiv:2605.16175v1 Announce Type: new Abstract: Pediatric critical care is a dynamic, high-stakes process involving constant monitoring and adjustments in life-saving treatments. Modeling these interv
arXiv:2605.15960v1 Announce Type: new Abstract: We propose a novel definition of model exploitation in reinforcement learning. Informally, a world model is exploitable if it implies that one policy sh
arXiv:2602.06470v2 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constra
arXiv:2602.16363v2 Announce Type: replace Abstract: We study reward-free and reward-agnostic exploration in episodic finite-horizon Markov decision processes (MDPs), where an agent explores an unknown
arXiv:2605.16193v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate human opinions and survey responses, but their ability to reproduce population responses
arXiv:2603.17915v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are deployed in multilingual settings, their safety behavior in culturally diverse, low-resource languages rem
arXiv:2510.05676v2 Announce Type: replace Abstract: Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud
arXiv:2603.13864v2 Announce Type: replace-cross Abstract: Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems
arXiv:2605.05652v2 Announce Type: replace Abstract: Simulation-based inference (SBI) provides amortized Bayesian parameter inference from simulator-generated data without requiring explicit likelihood
arXiv:2504.09544v3 Announce Type: replace-cross Abstract: Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput micro
arXiv:2605.15675v1 Announce Type: cross Abstract: Influence functions approximate how removing a training example changes a quantity of interest, called the target function, such as a held-out loss. T
arXiv:2605.15822v1 Announce Type: new Abstract: Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear str
arXiv:2603.27043v2 Announce Type: replace Abstract: We introduce the Mandarin-English Language Interview (MELI) Corpus, an open-source resource of 29.8 hours of speech from 51 Mandarin-English bilingu
arXiv:2605.15923v1 Announce Type: new Abstract: Modern image encoders achieve high generalization by decoupling semantic meaning from resolution, an ability yet to be fully realized in the 3D domain.
arXiv:2605.15626v1 Announce Type: new Abstract: Large language models deliver strong performance across language and reasoning tasks, but their storage and compute costs remain major barriers to deplo
arXiv:2605.15226v1 Announce Type: cross Abstract: We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate
arXiv:2605.15384v1 Announce Type: cross Abstract: Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time
arXiv:2605.16069v1 Announce Type: new Abstract: Timeseries regression models often struggle to leverage large volumes of labeled multimodal data, particularly when the data are irregularly sampled or
arXiv:2605.16258v1 Announce Type: cross Abstract: Reconstructing coherent 3D geometry and appearance from unposed multi-view images is a fundamental yet challenging problem in computer vision. Most ex
arXiv:2506.23552v2 Announce Type: replace Abstract: The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS
arXiv:2605.16023v1 Announce Type: new Abstract: LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its
arXiv:2605.15548v1 Announce Type: new Abstract: Traditional robotic hand metrics focus on static properties such as workspace, manipulability, and grasp stability. However, these metrics do not direct
arXiv:2605.15419v1 Announce Type: new Abstract: Flow matching trains a neural velocity field by regression against a target velocity associated with a prescribed probability path connecting a simple i
arXiv:2605.15769v1 Announce Type: cross Abstract: The co-optimization of a robot's body and brain presents a coupled challenge: the morphology constrains which control strategies are effective, while
arXiv:2605.15496v1 Announce Type: cross Abstract: Neural distance fields offer a compact and continuous representation of 3D geometry, making them attractive for incremental LiDAR mapping. However, th
arXiv:2603.25099v2 Announce Type: replace-cross Abstract: We present a framework in which a large language model (LLM) acts as an online adaptive controller for SIMP topology optimization, replacing c
arXiv:2504.08300v5 Announce Type: replace-cross Abstract: Benchmark-based evaluation, e.g., multiple-choice questions (MCQs) and open-ended questions (OEQs), is widely used for evaluating Large Langua
arXiv:2512.19701v2 Announce Type: replace-cross Abstract: Accurate prediction of resource consumption and runtime for cloud workflow jobs is critical for scheduling efficiency, yet remains challenging
arXiv:2605.15618v1 Announce Type: cross Abstract: Self-supervised video models are increasingly framed as world models, yet their evaluation remains largely confined to a single top-1 accuracy score o
arXiv:2603.10881v2 Announce Type: replace Abstract: Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low si
arXiv:2605.16234v1 Announce Type: cross Abstract: When researchers ask whether two transformer layers are 'equivalent' for compression, they often conflate distinct tests. Replacement asks whether one
arXiv:2605.15895v1 Announce Type: cross Abstract: Clinical application of high-resolution diffusion MRI is hindered by hardware limitations and prohibitive scan times, motivating computational super-r
arXiv:2605.15463v1 Announce Type: new Abstract: As machine learning models grow in complexity, they increasingly struggle with three conflicting demands: the need for high accuracy, the requirement fo
arXiv:2605.15582v1 Announce Type: new Abstract: Modern deep learning models for change detection (CD) often struggle to explicitly represent task-relevant semantic differences. This paper proposes the
arXiv:2605.15341v1 Announce Type: cross Abstract: LLMs are increasingly deployed in autonomous laboratories, under the assumption that their domain priors and reasoning over iterative feedback let the
arXiv:2605.16154v1 Announce Type: new Abstract: Reinforcement learning (RL) allows vision-language-action (VLA) policies to generalize beyond their training distribution by optimizing directly for tas
arXiv:2605.15760v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) optimization is most commonly performed using standard optimizers (Adam, SGD). While stable across diverse scenes, standard
arXiv:2605.15975v1 Announce Type: new Abstract: We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations ha
arXiv:2605.15789v1 Announce Type: new Abstract: Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely
arXiv:2605.15640v1 Announce Type: new Abstract: Multi-View Clustering (MVC) has gained significant attention for its ability to leverage complementary information across diverse views. However, existi
arXiv:2605.15713v1 Announce Type: cross Abstract: Legged manipulators extend robotic capabilities beyond static manipulation by integrating agile locomotion with versatile arm control. However, achiev
arXiv:2605.15535v1 Announce Type: new Abstract: Underwater salient object detection (USOD) has attracted increasing attention for underwater visual scene understanding and vision-guided robotic applic
arXiv:2504.09006v4 Announce Type: replace-cross Abstract: We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual
arXiv:2605.15487v1 Announce Type: cross Abstract: Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from tw
arXiv:2605.15236v1 Announce Type: cross Abstract: With the coded caching, the server can use the information the users have cached to serve multiple users at a time by sending a single coded multi-cas
arXiv:2605.16043v1 Announce Type: cross Abstract: Deformable Linear Objects (DLOs) such as ropes and cables are widely encountered in both household and industrial applications, yet remain challenging
arXiv:2604.02812v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have recently demonstrated strong capabilities in mapping multimodal observations to robot behaviors. However, most cu
arXiv:2602.04909v3 Announce Type: replace Abstract: Direct Preference Optimization (DPO) and related methods align large language models from pairwise preferences by regularizing updates against a fix
arXiv:2605.15504v1 Announce Type: cross Abstract: Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly ali
arXiv:2605.15886v1 Announce Type: new Abstract: This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in th
arXiv:2511.19931v2 Announce Type: replace-cross Abstract: Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various dom
arXiv:2605.15916v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural language process
arXiv:2509.21663v2 Announce Type: replace-cross Abstract: Neurosymbolic integration (NeSy) blends neural-network learning with symbolic reasoning. The field can be split between methods injecting hand