Search: “framework”

arXiv:2607.12200v1 Announce Type: new Abstract: As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert

arXiv:2607.12215v1 Announce Type: new Abstract: Accurately assessing personality from text is challenging because traits are latent, context-dependent, and often subtly expressed across long narrative

arXiv:2607.12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remai

arXiv:2607.11888v1 Announce Type: new Abstract: We develop a rigorous theoretical framework for optimal market making in perpetual futures markets with zero maker fees. We model the market maker's pro

arXiv:1809.01898v2 Announce Type: replace-cross Abstract: The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learnin

arXiv:2607.06740v1 Announce Type: cross Abstract: Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation due to their i

arXiv:2607.05406v1 Announce Type: cross Abstract: Large language models generate code from natural language prompts, enabling 'vibe coding,' which allows non-programmers to develop computational solut

arXiv:2607.04028v1 Announce Type: cross Abstract: We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchi

arXiv:2607.04103v1 Announce Type: cross Abstract: The release of SR 26-2 marks a significant modernization of U.S. model risk management by replacing SR 11-7 with a more risk-based and materiality-sen

arXiv:2510.20091v3 Announce Type: replace-cross Abstract: Creativity is often seen as a hallmark of human intelligence. While large language models(LLMs) are increasingly perceived as generating creat

arXiv:2607.01362v1 Announce Type: cross Abstract: Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demand

DSGym is a comprehensive framework designed to evaluate and train AI agents for data science tasks, providing a structured environment for benchmarking agent performance across various data science wo

arXiv:2606.31178v1 Announce Type: cross Abstract: Optimizing nonlinear preferences in multi-objective reinforcement learning (MORL) is essential for capturing complex trade-offs like risk aversion or

arXiv:2606.29212v1 Announce Type: new Abstract: Agent-based evacuation simulations are widely used to study crowd behavior during emergencies, but many models rely on assumptions such as perfect event

arXiv:2508.02923v3 Announce Type: replace Abstract: Maximum A Posteriori (MAP) estimation is a cornerstone framework for blind inverse problems, where an image and a forward operator are jointly estim

arXiv:2606.29459v1 Announce Type: cross Abstract: Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine

arXiv:2602.06989v2 Announce Type: replace-cross Abstract: Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage. X-ray compute

Vercel Services allows developers to run multiple web frameworks within a single project, enabling polyglot architecture patterns and flexible technology choices. This feature likely simplifies managi

arXiv:2606.29794v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) frameworks rely on camera-specific rasterization, suffering from inconsistent solid-angle sampling and degraded pe

arXiv:2606.27831v1 Announce Type: cross Abstract: This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framew

arXiv:2602.09456v2 Announce Type: replace Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward

arXiv:2606.26585v1 Announce Type: new Abstract: With the gradual introduction of AI into telescope scheduling, AI-based decision-making has shown advantages in handling complex multi-constraint proble

arXiv:2606.26457v1 Announce Type: cross Abstract: This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but must adapt to

arXiv:2606.23720v1 Announce Type: cross Abstract: We present an integrated framework that unifies runtime verification and model-based diagnosis within the stream specification language LOLA. By encod

arXiv:2606.20620v1 Announce Type: new Abstract: Starting from the axiomatic formulation of scale-space theory, we develop a viscosity-solution framework for multiscale image representations arising fr

arXiv:2606.23157v1 Announce Type: new Abstract: This paper presents a modular training-free framework for zero-shot, language-guided robotic manipulation in semi-structured environments. The architect

arXiv:2606.21378v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated significant potential in supporting engineering design tasks, including computer-aided

arXiv:2510.02561v2 Announce Type: replace Abstract: Recent advances in large video-language models (VLMs) rely on extensive fine-tuning techniques that strengthen alignment between textual and visual

We've fielded the same question at every conference this year. An engineer has chosen a framework, CrewAI one week, LangGraph the next, Mastra the week after, and wants to see exactly how observabilit

TSAuditor is a contamination auditing framework for time series foundation models that uses probe adaptation dynamics to detect when evaluation datasets may have been exposed during pretraining, valid

arXiv:2606.12018v1 Announce Type: new Abstract: We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intel

arXiv:2606.12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing clima

Zscaler Inc. today unveiled a major expansion of its zero-trust SASE platform, adding an agentic framework that lets administrators manage the system through natural-language prompts and extending its

arXiv:2601.09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately pr

arXiv:2606.08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization. The key idea is to approximate the Moreau envelope of the object

arXiv:2606.07186v1 Announce Type: new Abstract: Software-in-the-loop (SIL) simulation is a cornerstone for the validation of modern automotive safety functions. However, many current frameworks utiliz

arXiv:2606.06996v1 Announce Type: new Abstract: This paper studies runtime safety for autonomous driving when high-level driving commands become faulty or unreliable. Unlike conventional runtime-safet

arXiv:2510.17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline

arXiv:2606.06250v1 Announce Type: new Abstract: Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-sui

arXiv:2110.06847v3 Announce Type: replace Abstract: From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogo

arXiv:2512.06553v2 Announce Type: replace-cross Abstract: We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated b

arXiv:2606.04321v1 Announce Type: new Abstract: Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability. Neither posture

Jerry Liu built one of the most installed pieces of AI plumbing of the last three years. Then he sat down and told me the framework era he helped create is over. The agent harness ate the abstraction

Google has open-sourced its flood forecasting framework, which replicates operational FloodHub model training settings and reflects methodology described in a 2024 Nature paper for global ungauged flo

Learn how Microsoft helps developers build trustworthy AI agents with open evaluations, portable runtime controls, production observability, and security workflows that work across frameworks. The pos

arXiv:2606.00333v1 Announce Type: new Abstract: LLMs increasingly answer questions about taxes, labor protections, healthcare, education, pensions, and administrative procedures, where usefulness ofte