Knowledge catalogue
arXiv:2606.23742v1 Announce Type: cross Abstract: Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear
arXiv:2606.24011v1 Announce Type: cross Abstract: A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, a
arXiv:2606.24248v1 Announce Type: new Abstract: Event-based object Detection (EvDet), as a biologically inspired visual perception paradigm, demonstrates superior performance in scenarios demanding hi
arXiv:2505.10931v4 Announce Type: replace Abstract: Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural deta
arXiv:2606.23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the up
arXiv:2108.02283v3 Announce Type: replace-cross Abstract: Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient booste
arXiv:2606.23851v1 Announce Type: cross Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion
arXiv:2506.10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale gra
arXiv:2606.23983v1 Announce Type: cross Abstract: A single forward pass of a capable model is a fast, fluent, and unreliable problem-solver: it is right often enough to be useful and wrong often enoug
arXiv:2407.06136v4 Announce Type: replace Abstract: Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously
arXiv:2606.24479v1 Announce Type: new Abstract: In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based r
arXiv:2606.24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operat
arXiv:2606.24375v1 Announce Type: new Abstract: Detecting anomalies in industrial objects is an important topic for increasing production efficiency. More complex objects often require the analysis of
arXiv:2606.24841v1 Announce Type: new Abstract: Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectiv
arXiv:2606.24644v1 Announce Type: new Abstract: Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper st
arXiv:2606.24155v1 Announce Type: new Abstract: Existing medical AI benchmarks lack process visibility, atomic skill evaluation, and integrated hallucination detection. We introduce MedBench v5, a red
arXiv:2606.24433v1 Announce Type: cross Abstract: Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting r
arXiv:2606.24337v1 Announce Type: new Abstract: Czech has been part of Universal Dependencies since its first release in 2015. It has also been one of the best represented languages, with the Prague D
arXiv:2606.24595v1 Announce Type: new Abstract: Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interactio
arXiv:2510.24295v2 Announce Type: replace Abstract: As many benchmarks have become saturated, it has become increasingly important to create new datasets that evaluate the generalization capacity of c
arXiv:2606.24151v1 Announce Type: cross Abstract: Self-evolving agents improve over time by distilling experience from past executions and reusing it in future tasks. Existing systems represent such e
arXiv:2606.23872v1 Announce Type: cross Abstract: As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a g
arXiv:2606.23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal repre
arXiv:2505.02281v3 Announce Type: replace-cross Abstract: This paper explores the performance of a random Gaussian smoothing zeroth-order (ZO) scheme for minimising quasar-convex (QC) and strongly qua
arXiv:2606.24078v1 Announce Type: new Abstract: Imitation learning enables robots to acquire complex manipulation skills from demonstrations, but its effectiveness is limited by the cost of collecting
arXiv:2509.22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions. A key challenge,
arXiv:2606.24301v1 Announce Type: new Abstract: Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. How
arXiv:2606.24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora.
arXiv:2509.14001v5 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightwei
arXiv:2606.24404v1 Announce Type: new Abstract: The incorporation of additional modalities into action recognition models increases their performance across a wide range of settings. However, how this
arXiv:2606.24715v1 Announce Type: cross Abstract: We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule, we denote by the te
arXiv:2606.23694v1 Announce Type: new Abstract: Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic docume
arXiv:2606.24366v1 Announce Type: new Abstract: We present MorfFlex, a morphological dictionary architecture suitable for languages with extensive regularity in both inflection and derivation. As the
arXiv:2606.19416v2 Announce Type: replace Abstract: Loan origination is the process by which a lender creates a new loan, from application and underwriting through approval and funding. This process s
arXiv:2606.24214v1 Announce Type: new Abstract: Accurate pulmonary vessel segmentation remains challenging due to the sparse, tortuous, and multi-scale nature of vascular structures, where small branc
arXiv:2606.24263v1 Announce Type: new Abstract: Microwave satellite imagery plays a crucial role in monitoring tropical cyclone precipitation and intensity worldwide, but suffers from long revisit tim
arXiv:2510.14792v4 Announce Type: replace Abstract: Open-vocabulary object detection (OVD) aims to recognize and localize object categories beyond the training set. Recent approaches leverage vision-l
arXiv:2602.22810v2 Announce Type: replace Abstract: In this work, we present the first theoretical analysis of multi-agent imitation learning (MAIL) in linear Markov games where both the transition dy
arXiv:2606.24567v1 Announce Type: new Abstract: Sparse-view computed tomography (SVCT) reduces radiation exposure and acquisition time, but the limited number of projection views makes the reconstruct
arXiv:2504.06138v3 Announce Type: replace-cross Abstract: Professional users need tools to help them gain actionable insights from large multimedia collections. Foundation models and AI agents have ra
arXiv:2504.17070v3 Announce Type: replace-cross Abstract: Robots need task planning methods to achieve goals that require more than one action. Recently, large pretrained models have demonstrated impr
arXiv:2606.24347v1 Announce Type: new Abstract: Accurate short-term PM_{2.5} forecasting is important for public health protection, air-quality early warning, and urban environmental management. Howev
arXiv:2602.15245v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but curre
arXiv:2606.24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing differential pr
arXiv:2606.24530v1 Announce Type: new Abstract: We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether
arXiv:2606.24196v1 Announce Type: new Abstract: Modern AIGC pipelines deliver high-fidelity images and videos but presuppose a well-formed creation instruction, while end users rarely articulate visua
arXiv:2606.24101v1 Announce Type: cross Abstract: Conventional visual navigation policies often struggle with myopic decision-making and mode collapse in complex environments. While world models offer
arXiv:2606.24265v1 Announce Type: cross Abstract: Numerical simulations in industrial applications often require performing numerous high-precision computations parameterized by specific experimental
arXiv:2601.16096v2 Announce Type: replace-cross Abstract: We introduce Neural Particle Automata (NPA), a Lagrangian generalization of Neural Cellular Automata (NCA) from static lattices to dynamic par
arXiv:2606.23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose interme
arXiv:2606.23761v1 Announce Type: cross Abstract: Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still unde
arXiv:2606.24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging. EEG provides a weak, spatially diffuse, and
arXiv:2606.24879v1 Announce Type: cross Abstract: We study the last iterate of the stochastic subgradient method for one-dimensional convex Lipschitz objectives. For a fixed horizon n, we consider the
arXiv:2606.24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a
arXiv:2603.15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show
arXiv:2606.24068v1 Announce Type: new Abstract: Embodied reasoning and exploration are increasingly considered crucial abilities for robots operating in complex and unfamiliar environments. To accompl
arXiv:2606.23978v1 Announce Type: cross Abstract: We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment. SLAM (Scan/
arXiv:2606.24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a pa
arXiv:2606.24504v1 Announce Type: new Abstract: We discuss reasons why the scaling exponents of current Large Language Models (LLMs) applications are indicating an unsustainable regime in terms of ene
arXiv:2606.24381v1 Announce Type: cross Abstract: Prompt-based interaction has become a dominant paradigm for using large language models (LLMs), where multiple candidate prompts are evaluated and the