main webpage W Topic Generation Beta arXiv (Cornell University) DynamicBench: Evaluating Real-Time Report Generation in Large Language Models 2025 Traditional benchmarks for large language models (LLMs) typically rely on static evaluations through storytelling or opinion expression, which fail to capture the dynamic requirements of real-time information processing in contemporary applications. To addres… Article Open Generation Beta Open Article Page

Generation Beta

Generational cohort succeeding Generation Alpha Generation Beta (often shortened to Gen Beta ) is the proposed name for the demographic cohort succeeding Generation Alpha. The name was coined by futurist and demographer Mark McCrindle (who also coined the name Generation Alpha ). He defines the cohort as those born from 2025 to 2039. Researchers have not yet formed a general consensus as to the generation's birth years; since no official body determines generational boundaries, definitions may vary (see § Date range definitions). Generation Beta is named after the second letter of the Greek alphabet. McCrindle expects its members to primarily be the children of young Millennials and Generation Z. … Critical Symbolic Virtual Narrative Open Generation Beta Open Article Page

Exploring foci of: arXiv (Cornell University) DynamicBench: Evaluating Real-Time Report Generation in Large Language Models 2025 Traditional benchmarks for large language models (LLMs) typically rely on static evaluations through storytelling or opinion expression, which fail to capture the dynamic requirements of real-time information processing in contemporary applications. To address this limitation, we present DynamicBench, a benchmark designed to evaluate the proficiency of LLMs in storing and processing up-to-the-minute data. DynamicBench utilizes a dual-path retrieval pipeline, integrating web searches with local report databases. It… Open Article Page

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Explore Generation Beta W Topic Generation Beta arXiv (Cornell University) DynamicBench: Evaluating Real-Time Report Generation in Large Language Models 2025 Traditional benchmarks for large language models (LLMs) typically rely on static evaluations through storytelling or opinion expression, which fail to capture the dynamic requirements of real-time information processing in contemporary applications. To addres… Article Critical Symbolic Virtual Narrative Open Generation Beta Open Article Page