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Pulse · analyticsinsight.net

analyticsinsight.net · Nov 4

Apple Opens Siri AI to Third-Party Apps with iOS 27.3 Beta Update

Apple has started letting all developers connect their apps to Siri AI. The company opened access on Tuesday, November 3, with the first developer beta of iOS 2

Pulse · msn.com

msn.com · Nov 4

India unveils 3 indigenous AI models: EKA-1, Dhīr 2B and 5B bring offline AI and Indian language support

India has unveiled three indigenous AI models - EKA-1, Dhīr 2B and Dhīr 5B - under the IndiaAI Mission. Developed by Bengaluru-based Socket AI and ZenteiQ, the models are designed for applications ranging from coding and advanced reasoning to offline AI access on smartphones.

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arXiv

Nov 3

DreamX: Video Diffusion Model is a Universal Latent Action Learner

Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearance. Existing latent action models infer actions from visual transitions, but their representations often entangle the underlying action with irrelevant visual variations, limiting transfer across embodiments. We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner. Our approach extracts compact latent actions from the video model's rich spatiotemporal features, compressing each latent video frame by $\boldsymbol{300\times}$ ($\boldsymbol{1{,}200 \rightarrow 4}$ tokens). In addition, by leveraging the video model's generative prior, these latent actions capture action-relevant dynamics disentangled from visual appearance. The resulting latent space serves as a shared action interface across embodiments (human and robot), environments, and action parameterizations. On LARY-Bench, DreamX achieves state-of-the-art performance among latent action models. Moreover, actions encoded in one setting remain effective in another: the latent…

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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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