{"slug": "iquest-q1-advancing-foundation-capabilities-for-agentic-cli-systems", "title": "IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems", "summary": "IQuest released IQuest-Q1, a sparse Mixture-of-Experts agentic foundation model with 320B total parameters and 15B activated parameters, built for agentic CLI systems. The model is trained in three stages, beginning with pre-training that combines Stable SplitNorm, Muon and muP under a two-stage curriculum. The release details the architecture and training approach for developers building command-line AI agents.", "body_md": "We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (1) stable and efficient pre-training, combining Stable SplitNorm, Muon, and muP with a two-stage curriculum whose second", "url": "https://wpnews.pro/news/iquest-q1-advancing-foundation-capabilities-for-agentic-cli-systems", "canonical_source": "https://aiflash.com/news/137928/", "published_at": "2026-10-23 10:38:59+00:00", "updated_at": "2026-10-23 10:41:50.098328+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-agents", "ai-research"], "entities": ["IQuest", "IQuest-Q1", "Stable SplitNorm", "Muon", "muP"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/iquest-q1-advancing-foundation-capabilities-for-agentic-cli-systems", "markdown": "https://wpnews.pro/news/iquest-q1-advancing-foundation-capabilities-for-agentic-cli-systems.md", "text": "https://wpnews.pro/news/iquest-q1-advancing-foundation-capabilities-for-agentic-cli-systems.txt", "jsonld": "https://wpnews.pro/news/iquest-q1-advancing-foundation-capabilities-for-agentic-cli-systems.jsonld"}}