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Pulse · forbes.com

forbes.com · Nov 4

The New Trust Economy: Authentic Brands For Humans And AI Agents

Most conversations about trust focus on people, but another audience is emerging quickly: AI agents. As agentic commerce evolves, AI systems increasingly search, compare, evaluate, recommend, negotiate and potentially purchase on behalf of consumers.

Pulse · finance.yahoo.com

finance.yahoo.com · Nov 4

Oracle Cancels $7 Billion Tencent AI Chip Leasing Agreement Amid US Regulatory Pressure

Oracle (NYSE:ORCL) has cancelled a $7 billion agreement to lease advanced artificial intelligence chips to Chinese technology group Tencent (HKEX:0700), following concerns raised by US officials over China's access to American semiconductor technology,

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Top picks this weekarXiv

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 22

NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task

NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification (NADI) shared task series and the second dedicated to multidialectal Arabic speech processing. This edition comprises five tasks and eight subtasks spanning Automatic Speech Recognition (ASR), Spoken…

arXiv

Sep 22

Crossflow: Prefill-Decode Elasticity for Agentic LLM Serving

As serving capacity demand surpasses that of training, serving efficiency becomes increasingly important. Prefill-decode (P/D) disaggregation improves serving efficiency through specialization and isolation of the two phases. These benefits rest on a static partitioning. Phase…

arXiv

Sep 22

Heartian: Physiology-Aware Relightable Gaussian Head Avatar

Gaussian head avatars typically model intrinsic facial appearance as temporally static, omitting subtle cardiac-induced skin-color variation. We propose Heartian, a physiology-aware modulation framework that learns cardiac-cycle-dependent per-frame albedo modulation of facial…

arXiv

Sep 22

Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environments

Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perceptual taxonomies. However, existing benchmarks largely rely on short category labels and web-scraped imagery, leaving it…

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