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arxiv: 2306.02781 · v4 · submitted 2023-06-05 · 💻 cs.LG · cs.AI

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

Eduardo C. Garrido-Merch\'an , \'Alvaro L\'opez L\'opez This is my paper

Pith reviewed 2026-05-24 07:57 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords generative artificial intelligencelarge language modelsmodel comparisonarchitecturesapplicationsdeployment protocolssurvey
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The pith

This automated survey compares frontier large language models on architectures, training regimes and performance as of early 2026.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to establish an accessible overview of generative artificial intelligence through a detailed comparison of leading large language model families and their practical uses. It describes architectural innovations, training regimes, benchmark results and deployment protocols for both open and proprietary systems. The survey also reviews applications in fifteen industry sectors with case studies. A sympathetic reader would care because this condenses complex, fast-moving information into one reference that can inform model selection and technology adoption.

Core claim

The central claim is that an automated survey generated under human supervision delivers a current treatment of generative AI as of early 2026, with detailed analysis of leading model families on architectural innovations, training regimes, empirical performance, deployment protocols, and applications across multiple industry sectors.

What carries the argument

The detailed comparative analysis of frontier large language models that describes architectural innovations, training regimes, and empirical performance on benchmarks and the Chatbot Arena leaderboard for each model.

If this is right

  • The survey enables direct comparison of open-weight models with proprietary ones on empirical benchmarks.
  • Deployment protocols including Retrieval-Augmented Generation, Model Context Protocol, Agent-to-Agent protocol and function calling are presented for practical implementation.
  • Real-world applications in fifteen sectors from financial services to agriculture are reviewed with empirical evidence and case studies.
  • Updated editions of the survey can be produced approximately every six months to reflect new developments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The use of an AI system to generate the survey content could support more rapid updates than manual methods.
  • Describing forthcoming models suggests the analysis incorporates anticipated future capabilities.
  • The emphasis on both open-weight and proprietary systems points to trade-offs in accessibility versus performance.

Load-bearing premise

An AI model can produce an accurate and comprehensive survey of generative AI models and applications as of early 2026 despite the paper being submitted in June 2023.

What would settle it

Checking in early 2026 whether the listed models exist, match the described architectures and achieve the stated performance on benchmarks and leaderboards.

read the original 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 2026, with a strong focus on the leading model families, deployment protocols, and real-world applications. The core of the survey is devoted to a detailed comparative analysis of the frontier large language models, with particular emphasis on open-weight systems: DeepSeek-V3, DeepSeek-R1, DeepSeek-V3.2, and the forthcoming DeepSeek V4; the Qwen 3 and Qwen 3.5 series; GLM-5; Kimi K2.5; MiniMax M2.5; LLaMA 4; Mistral Large 3; Gemma 3; and Phi-4, alongside proprietary systems including GPT-5.4, Gemini 3.1 Pro, Grok 4.20, and Claude Opus 4.6. For each model, we describe the architectural innovations, training regimes, and empirical performance on current benchmarks and the Chatbot Arena leaderboard. The survey further covers deployment protocols including Retrieval-Augmented Generation, the Model Context Protocol, the Agent-to-Agent protocol, function calling standards, and serving frameworks. We present an extensive review of real-world applications across fifteen industry sectors, from financial services and legal technology to tourism and agriculture, supported by empirical evidence and case studies. This work has been generated by Claude Opus 4.6 (Anthropic) under the supervision and editorial review of the human authors, with the goal of producing updated editions approximately every six months.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims to deliver an automated survey of generative AI as of early 2026, centered on a comparative analysis of frontier LLMs (DeepSeek-V3/R1/V3.2/V4, Qwen 3/3.5, GLM-5, LLaMA 4, GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6 and others). For each model it describes architectural innovations, training regimes, benchmark performance and Chatbot Arena rankings; it also reviews deployment protocols (RAG, Model Context Protocol, function calling) and applications across fifteen sectors. The work is stated to have been generated by Claude Opus 4.6 under human supervision.

Significance. A factually grounded, regularly updated survey of this scope would be a useful reference for the community. The present manuscript, however, cannot supply such grounding.

major comments (1)
  1. [Abstract] Abstract (and title page): the manuscript asserts coverage of models and events dated 2025–2026 (DeepSeek-V3, GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6, Chatbot Arena leaderboard as of early 2026) while carrying arXiv identifier 2306.02781, which fixes submission to June 2023. No mechanism is supplied for obtaining post-submission information, so every concrete claim about model architectures, training details, or empirical results rests on impossible future knowledge rather than observed data.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful review and for highlighting the critical timeline inconsistency in the manuscript. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and title page): the manuscript asserts coverage of models and events dated 2025–2026 (DeepSeek-V3, GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6, Chatbot Arena leaderboard as of early 2026) while carrying arXiv identifier 2306.02781, which fixes submission to June 2023. No mechanism is supplied for obtaining post-submission information, so every concrete claim about model architectures, training details, or empirical results rests on impossible future knowledge rather than observed data.

    Authors: The referee is correct that the arXiv identifier 2306.02781 fixes the submission date to June 2023, while the manuscript references specific models, architectures, benchmarks, and events from 2025–2026. No mechanism for post-submission information acquisition is described that could legitimately support such claims. This renders the concrete factual assertions about future models impossible to ground in observed data. We will revise the manuscript to remove or explicitly qualify all post-2023 references, aligning the content with information available as of the actual submission date. This revision will require substantial changes to the abstract, model comparison sections, and application reviews. revision: yes

Circularity Check

0 steps flagged

No circularity: purely descriptive survey with no derivations or fitted claims

full rationale

The manuscript is a narrative survey of LLMs with no equations, parameters, or derivation chain of any kind. No self-definitional steps, fitted inputs presented as predictions, or load-bearing self-citations appear. The document simply describes claimed model properties and applications; its content is self-contained as a review and does not reduce any result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced because the document is a descriptive survey rather than a theoretical or empirical research contribution.

pith-pipeline@v0.9.0 · 5847 in / 1066 out tokens · 35681 ms · 2026-05-24T07:57:09.674647+00:00 · methodology

discussion (0)

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    cs.SE 2025-06 unverdicted novelty 6.0

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