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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

Goal-driven Variant Categorization

Process discovery rarely yields a single coherent process structure. For analysis, a common step is to cluster process variants based on structural similarity and then assign business meaning to the resulting groups. Since these partitions are not derived from the…

arXiv

Sep 18

Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation

Mixture-of-Experts (MoE) models are increasingly deployed alongside Speculative Decoding (SD) to accelerate inference, but combining the two is challenging. SD improves the inference speed of dense models by verifying groups of tokens in parallel. However, the inference speedup…

arXiv

Sep 18

Neutrosophic Ensemble Classification for Uncertainty-Aware Bearing Fault Detection: Evidence from Laboratory and Variable-Speed Industrial Benchmarks

Machine learning classifiers for bearing fault detection produce scalar confidence scores that conflate confident errors with genuinely ambiguous predictions, and the conventional truth/falsity pair (F = 1 - T) is algebraically redundant by construction. We operationalize a…

arXiv

Sep 18

Toward Personalized Sleep Guidance from Wearable Data Using Language Models

Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering remain insufficient for personalized sleep guidance. Training specialized models, however, often requires costly expert…

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