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News/An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications
arxiv
PublishedApril 9, 2026 at 4:00 AM
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An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2306.02781v3 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in modern computer science. This automated survey provides an accessible treatment of the field as of early

Models mentioned
01
  • 01meta-llama logo
    Llama-4
    meta-llama/Llama-4
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Mentioned models
17
  • 01
    DeepSeek-V3
  • 02
    DeepSeek-R1
  • 03
    DeepSeek-V3.2
  • 04
    DeepSeek V4
  • 05
    Qwen 3
  • 06
    Qwen 3.5
  • 07
    GLM-5
  • 08
    Kimi K2.5
  • 09
    MiniMax M2.5
  • 10
    Llama-4
    meta-llama/Llama-4
  • 11
    Mistral Large 3
  • 12
    Gemma 3
  • 13
    Phi-4
  • 14
    GPT-5.4
  • 15
    Gemini 3.1 Pro
  • 16
    Grok 4.20
  • 17
    Claude Opus 4.6
Source
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arxiv
Read original ↗All from arxiv →
Tags
04
#large-language-models#generative-ai#machine-learning#industry-applications
Mentioned companies
01
Anthropic

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Mentioned models
17
  • 01
    DeepSeek-V3
  • 02
    DeepSeek-R1
  • 03
    DeepSeek-V3.2
  • 04
    DeepSeek V4
  • 05
    Qwen 3
  • 06
    Qwen 3.5
  • 07
    GLM-5
  • 08
    Kimi K2.5
  • 09
    MiniMax M2.5
  • 10
    Llama-4
    meta-llama/Llama-4
  • 11
    Mistral Large 3
  • 12
    Gemma 3
  • 13
    Phi-4
  • 14
    GPT-5.4
  • 15
    Gemini 3.1 Pro
  • 16
    Grok 4.20
  • 17
    Claude Opus 4.6
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#large-language-models#generative-ai#machine-learning#industry-applications
Mentioned companies
01
Anthropic

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arxivNewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting6harxivFoundations of the Scaling Law6harxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science6harxivScoped Semantic Invariance as a Foundation for LLM Reliability6h
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