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Pulse · devops.com

devops.com · Oct 30

AWS installs an AI accelerator every 14 seconds and eyes $1 trillion annual revenue

Pulse · ca.finance.yahoo.com

ca.finance.yahoo.com · Oct 30

Tencent Cloud Accelerates International Growth, Advances Enterprise AI and Expands Global Partnerships

Tencent today unveiled the enterprise capabilities of its AI office productivity agent, WorkBuddy and previewed the upcoming general release of its Hy4 model and Hy4 Flash at the 2026 Tencent Global Digital Ecosystem Summit. Tencent Cloud's active overseas customer base has also doubled over the past year, while international demand for its AI products and services has grown by triple digits.

Pulse · finance.yahoo.com

finance.yahoo.com · Oct 30

Tencent Cloud Accelerates International Growth, Advances Enterprise AI and Expands Global Partnerships

SHENZHEN, China, Oct. 30, 2026 /PRNewswire/ -- Tencent today unveiled the enterprise capabilities of its AI office productivity agent, WorkBuddy and previewed the upcoming general release of its Hy4 model and Hy4 Flash at the 2026 Tencent Global Digital ...

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arXiv

Oct 29

Elucidating the Design Space of LM Data Attribution

Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval. However, existing evaluations vary multiple dataset properties simultaneously, making it impossible to isolate exactly why an attribution algorithm fails. To diagnose these failures, we explore the design space of LLM data attribution, with a primary focus on evaluation. We introduce the Controlled Attribution Benchmark (CATT) for fact tracing, in which we fine-tune LLMs on entirely fictitious knowledge, providing unambiguous ground truth for attribution. This synthetic setup allows us to systematically alter one dataset configuration at a time, such as document redundancy and lexical distractors. With this configurable evaluation, we demonstrate that redundancy and distractors consistently degrade attribution performance. Rather than proposing a new estimator, we examine two design choices that are underexplored: the loss used to compute gradients and how per-token attribution scores are aggregated into a document score. We find that the true causal evidence within a training document is highly localized, but standard methods dilute this evidence into noise by…

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arXiv

Sep 18

Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this principle, we introduce two variants: Dirichlet-Multinomial Bagging Random Forest (DM) and Dirichlet-Weighted Random Forest…

arXiv

Sep 18

Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals

Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains difficult because activation outliers degrade effective quantization resolution. Although weight optimization, channel-wise…

arXiv

Sep 18

ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling

World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future visual states. Tactile sensing complements this foundation with direct measurements of physical interaction. Some existing methods…

arXiv

Sep 18

FootQuery: Future-Touchdown-Guided Retrieval from Depth History for Perceptive Humanoid Locomotion

Humanoid locomotion over complex terrain requires anticipating footholds that may no longer be visible at touchdown. Limited camera coverage and self-occlusion make it necessary to retrieve relevant terrain information from earlier observations. We present FootQuery, a…

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