Knowledge catalogue
arXiv:2606.25357v1 Announce Type: new Abstract: State abstraction plays a key role in scaling reinforcement learning to complex but structured systems. In studying such systems, a wide range of behavi
arXiv:2606.25579v1 Announce Type: cross Abstract: Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using curr
Vercel has released Deep Agents and OpenCode adapters for the AI SDK Harness, expanding the framework's integration capabilities. These new adapters enable developers to leverage Deep Agents and OpenC
arXiv:2606.25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate und
arXiv:2606.24915v1 Announce Type: new Abstract: End-to-end automatic speech recognition systems frequently hallucinate rare entities and domain-specific terms, especially in low-resource languages. Wh
arXiv:2606.25606v1 Announce Type: new Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for ea
arXiv:2606.25778v1 Announce Type: new Abstract: This paper presents a versatile framework for evaluating fuzzy quantification queries over both standard and fuzzy ontologies as well as knowledge graph
arXiv:2606.25073v1 Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch fo
arXiv:2606.25753v1 Announce Type: new Abstract: Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable wavegu
arXiv:2606.25984v1 Announce Type: cross Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and
arXiv:2606.25656v1 Announce Type: new Abstract: As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for diff
arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T
arXiv:2606.25185v1 Announce Type: new Abstract: Accurately predicting the spatiotemporal evolution of amyloid-eta and tau proteins at the individual level is critical for improving the diagnosis and t
arXiv:2606.25006v1 Announce Type: new Abstract: Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an ef
arXiv:2606.25777v1 Announce Type: cross Abstract: We initiate a resource-aware theory of extit{language generation in the limit} under the minimal constraint of space efficiency. In our framework, a l
arXiv:2606.24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challe
arXiv:2606.25736v1 Announce Type: new Abstract: Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion
arXiv:2606.23977v1 Announce Type: new Abstract: Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehou
IBM Research presents a framework for benchmarking quantum optimization algorithms, addressing the need for standardized methods to evaluate quantum computing performance on optimization problems. The
arXiv:2504.01482v3 Announce Type: replace-cross Abstract: This paper develops a model-based framework for continuous-time policy evaluation (CTPE) in reinforcement learning, incorporating both Brownia
arXiv:2606.24140v1 Announce Type: new Abstract: Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics.
NVIDIA NeMo AutoModel is a tool designed to accelerate the fine-tuning process of transformer models by automating model selection and configuration. The solution leverages NVIDIA's NeMo framework to
arXiv:2509.14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human pre
arXiv:2511.21056v2 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) datasets are critical to the downstream performance of large language models, yet they often contain low-quality
arXiv:2510.25850v3 Announce Type: replace-cross Abstract: We introduce Debate2Create (D2C), a multi-agent LLM framework that formulates robot co-design as structured, iterative debate grounded in phys
arXiv:2602.01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet di
arXiv:2311.16707v2 Announce Type: replace-cross Abstract: Dense prediction is a fundamental requirement for many medical vision tasks such as medical image restoration, registration, and segmentation.
arXiv:2606.24144v1 Announce Type: new Abstract: In this paper, we present a novel geometry-aware style transfer framework for 3D Gaussian splatting (3DGS) that simultaneously transfers appearance attr
arXiv:2606.23745v1 Announce Type: cross Abstract: We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representation
arXiv:2606.24324v1 Announce Type: new Abstract: The Prague Dependency Treebank framework is unique in its attempt to systematically include and link different layers of language, including a meaning r
arXiv:2606.24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks priva
arXiv:2606.24046v1 Announce Type: cross Abstract: This work presents a machine learning framework that leverages an autoencoder (AE) for the efficient modeling of FinFET. We first calibrated a BSIM-CM
arXiv:2606.19623v2 Announce Type: replace Abstract: Graph neural networks (GNNs) offer a flexible framework for learning from scientific data with physical, biological, or functional associations. One
arXiv:2606.24350v1 Announce Type: new Abstract: Legged robots rely on accurate ground interaction awareness to traverse variable terrains, such as slippery surfaces. Existing slip detection methods of
arXiv:2606.23944v1 Announce Type: cross Abstract: State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture hea
arXiv:2606.24074v1 Announce Type: cross Abstract: Wang~ite{Wang2026} introduced the Stochastic-Oracle Turing Machine (SOTM) framework and defined token complexity as the minimum expected cost of inter
arXiv:2606.24058v1 Announce Type: new Abstract: This paper aims to bridge the semantic gap between visual content and natural language understanding by leveraging historical events in the real world a
arXiv:2505.11129v2 Announce Type: replace Abstract: Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionize
Francois Chollet presents a framework for understanding different approaches to token usage in AI models: casual users maximize tokens for flexibility, competitive users minimize tokens for efficiency
arXiv:2412.06190v2 Announce Type: replace Abstract: Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a w
arXiv:2606.21885v1 Announce Type: new Abstract: Foundation models have achieved remarkable performance across medical question answering, imaging, and electronic health record (EHR) tasks, yet reliabl
arXiv:2606.23364v1 Announce Type: new Abstract: Training dynamics is central to understanding neural networks, yet its theoretical analysis remains difficult even for simple architectures and becomes
arXiv:2606.20739v1 Announce Type: new Abstract: We present a unified framework that turns a set of 6-DoF inspection viewpoints into a time-optimal, collision-free route for a 9-DoF robotic system. Unl
arXiv:2511.20906v2 Announce Type: replace Abstract: Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However,
arXiv:2603.11909v2 Announce Type: replace Abstract: Reliable uncertainty quantification is paramount for forecasting multivariate time series and spatiotemporal data. While Transformer architectures e
arXiv:2606.22566v1 Announce Type: cross Abstract: Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical p
arXiv:2606.22159v1 Announce Type: cross Abstract: Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, con
arXiv:2606.21335v1 Announce Type: cross Abstract: This paper evaluates Reservoir Computing (RC) as an autonomous, 'feature-free' framework for audio processing, designed to eliminate traditional, hand
arXiv:2606.21159v1 Announce Type: cross Abstract: Automated floor plan generation is an important problem in computational architectural design. The goal is to construct a floor plan from user-defined
arXiv:2606.21153v1 Announce Type: cross Abstract: Decentralized bilevel optimization (DBO) provides a powerful framework for multi-agent systems to solve local bilevel tasks in a decentralized fashion
arXiv:2606.23655v1 Announce Type: new Abstract: We consider the problem of sequentially approximating functions of each element in a slowly-varying sequence, i.e. one where the magnitude alpha_i of th
arXiv:2606.22252v1 Announce Type: new Abstract: The integration of multiple thematic data layers into a single composite map, known as the cartographic synthesis problem, is typically addressed throug
arXiv:2606.23515v1 Announce Type: cross Abstract: Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined
arXiv:2602.23611v2 Announce Type: replace-cross Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such
arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus
arXiv:2606.22838v1 Announce Type: new Abstract: In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-sca
arXiv:2606.20727v1 Announce Type: cross Abstract: The escalating congestion in orbital space demands advanced monitoring solutions. This work presents a comprehensive open-source framework for neuromo
arXiv:2606.21440v1 Announce Type: new Abstract: Large-scale optimization problems are often solved repeatedly under similar structural conditions, leading to substantial computational overhead. This o
Genesis Workbench is a collaborative framework developed by Databricks and NVIDIA designed to accelerate AI adoption in the life sciences industry by providing integrated tools and best practices. The
arXiv:2602.04548v2 Announce Type: replace Abstract: We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learni