{"slug": "unleashing-the-potential-of-large-language-models-a-blueprint-for-real-time", "title": "Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments", "summary": "Researchers propose a unified LLMOps architecture for real-time, enterprise-ready deployments of large language models, addressing knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops. The system integrates real-time data ingestion, continual learning, retrieval-augmented generation, and human-in-the-loop feedback, with components including an adaptive ingestion pattern orchestrator (AIPO), STAR+FAR continual learning, SAGE retrieval policy, and an automated feedback-driven convergence stage. The approach aims to reduce latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.", "body_md": "arXiv:2608.00419v1 Announce Type: new\nAbstract: Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops. We present a unified, pattern-driven LLMOps architecture integrating real-time data ingestion, continual learning, retrieval-augmented generation (RAG), and human-in-the-loop feedback into a single operational pipeline. Four contributions map to established software design patterns: an adaptive ingestion pattern orchestrator (AIPO) evaluated with FreshStreamBench; STAR+FAR continual learning with sparse temporal adapter routing and freshness-aware replay; SAGE, an SLO-aware adaptive retrieval policy predicting a per-query passage budget to meet tail-latency targets; and an automated feedback-driven convergence stage with RLHF triggers. The result reduces latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.", "url": "https://wpnews.pro/news/unleashing-the-potential-of-large-language-models-a-blueprint-for-real-time", "canonical_source": "https://arxiv.org/abs/2608.00419", "published_at": "2026-08-04 04:00:00+00:00", "updated_at": "2026-08-04 04:34:53.540020+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "ai-infrastructure", "mlops", "ai-research"], "entities": ["arXiv", "FreshStreamBench", "AIPO", "STAR+FAR", "SAGE"], "alternates": {"html": "https://wpnews.pro/news/unleashing-the-potential-of-large-language-models-a-blueprint-for-real-time", "markdown": "https://wpnews.pro/news/unleashing-the-potential-of-large-language-models-a-blueprint-for-real-time.md", "text": "https://wpnews.pro/news/unleashing-the-potential-of-large-language-models-a-blueprint-for-real-time.txt", "jsonld": "https://wpnews.pro/news/unleashing-the-potential-of-large-language-models-a-blueprint-for-real-time.jsonld"}}