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Pulse · reuters.com

reuters.com · Nov 3

AI Agent Access Becomes a Top Security Priority for 2026

Pulse · techrseries.com

techrseries.com · Nov 3

HRTech Interview with Jimmy Adams, President, National Account Services, ADP

ADP has been shaping the world of work with innovation and expertise for more than 75 years. As a global leader in HR and payroll solutions, ADP continuously works to solve business challenges for their clients and their workers, from simple, easy-to-use tools for small businesses to fully integrated platforms for global enterprises, and everything in between.

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arXiv

Nov 2

Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation

General-purpose mobile manipulation requires a combination of capable robot hardware, scalable demonstration collection, expressive whole-body control, and effective policy learning. Yet these components are often developed independently, creating mismatches that limit the capabilities of the complete system. We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation. Pixel combines a low-cost wheeled humanoid with human-aligned kinematics and compliant grippers, multiple teleoperation and robot-free human data-collection interfaces, a unified whole-body controller, and a common policy learning pipeline across human and robot demonstrations. A hierarchical learning stack further enables language-conditioned visuomotor control and long-horizon task execution. We evaluate the system across hardware, data collection, and policy learning, and demonstrate mobile-manipulation behaviors requiring dexterity, active perception, navigation, and whole-body coordination. We hope that Pixel provides an integrated and extensible foundation for studying how robot hardware, data, control, and learning can be designed together for robot learning. More…

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arXiv

Sep 18

PRISM-BN: A Controlled Corpus and Benchmark for Text-to-Parameterized Bayesian Network Extraction

Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural symbolic targets for neurosymbolic AI. Yet training text-to-parameterized-BN systems requires paired text-to-BN resources…

arXiv

Sep 18

Accelerating Dense LLMs via L0-regularized Mixture-of-Experts

Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeable performance degradation, while Mixture-of-Experts (MoE) models require extensive computational resources. In this paper, we…

arXiv

Sep 18

From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Open Data (LOD) cloud, isolating archived artefacts from semantic discovery. This paper presents an end-to-end reconciliation…

arXiv

Sep 18

Samsone: A Family of Open Small Audio Language Models for On-Device Inference

The success of Large Audio Language Models has driven the development of massive multimodal networks exceeding billions of parameters. However, the demand for privacy-preserving, low-latency processing has shifted focus toward Small Audio Language Models (SALMs) capable of…

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