An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications

A comprehensive survey of generative AI and large language models as of early 2026 has been published, covering frontier open-weight models like DeepSeek and Qwen alongside proprietary systems, with detailed analysis of architectures, deployment protocols, and applications across fifteen industry sectors.

This automated survey represents a significant documentation of the generative AI landscape at a mature inflection point. The breadth of model coverage—spanning both open-weight systems gaining substantial adoption and proprietary offerings from major tech companies—reflects how competitive pressures have democratized access to frontier AI capabilities. The emphasis on models like DeepSeek-V3 and Qwen 3.5 signals the growing viability of alternatives to Western-developed systems, reshaping the global AI infrastructure competition.

The survey's focus on deployment protocols (RAG, MCP, agent-to-agent systems) and real-world applications indicates the field has transitioned from theoretical capability demonstrations to practical integration challenges. Organizations across financial services, legal technology, and agriculture now face concrete decisions about model selection, infrastructure investment, and risk management. The inclusion of empirical benchmarks and Chatbot Arena performance metrics provides practitioners with comparative frameworks previously unavailable in a single structured reference.

For developers and enterprises, this comprehensive cataloging of model architectures and training regimes accelerates decision-making around which systems to deploy. The systematic sector-by-sector application analysis demonstrates that AI adoption is advancing unevenly—some industries have clear ROI pathways while others remain experimental. The commitment to six-month update cycles suggests the survey itself will become a reference infrastructure for tracking AI progress, similar to how semiconductor roadmaps guide hardware planning. Market participants should monitor whether open-weight systems continue narrowing performance gaps with proprietary alternatives, as this directly impacts vendor lock-in dynamics and infrastructure costs.

• →DeepSeek and Qwen models represent credible open-weight alternatives gaining parity with proprietary systems across multiple benchmarks.
• →Deployment protocols like RAG and MCP have become standard requirements rather than optional enhancements for production AI systems.
• →Real-world AI adoption now spans across fifteen industry sectors with measurable business impact, moving beyond proof-of-concept phase.
• →The 2026 model landscape shows consolidation around 15-20 frontier systems, down from the proliferation of models in 2023-2024.
• →Infrastructure costs for deployment and serving frameworks have become primary decision factors alongside raw model capability.