Search: “framework”

Ethan Mollick argues that regulatory frameworks for AI will not overlook or exempt open-source AI systems from compliance requirements, implying that future AI regulation will comprehensively address

arXiv:2606.13984v2 Announce Type: replace-cross Abstract: Classification and Regression Trees (CART) constitute one of the most influential paradigms in statistical learning. Although a variety of imp

arXiv:2606.22902v2 Announce Type: replace Abstract: Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct dom

Vercel released AI SDK version 7, introducing new features and improvements to their JavaScript/TypeScript framework for building AI-powered applications. The update likely includes enhancements to mo

Meta released Harness, an open-source framework for evaluating and benchmarking AI model performance across diverse tasks and datasets. The tool enables standardized testing of language models and aim

arXiv:2408.01273v3 Announce Type: replace Abstract: We propose a framework for training neural network controllers with certified robust forward invariant polytopes. First, we parameterize a family of

'Choose Your Own' likely refers to a framework or methodology from Nous Research for customizable AI decision-making or model selection processes. Without access to the specific post content, this ent

arXiv:2606.25273v1 Announce Type: new Abstract: Zero-shot 3D anomaly detection is essential for industrial quality inspection, where labeled anomaly samples are scarce. Meanwhile, existing methods lac

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