mlsanityData to 14 Oct 2026

Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform

arXiv 2501.00750 ↗First posted 1 Jan 2025cs.AIcited by 5

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

also in 1 more
Abstract (arXiv)

This study proposes the design and implementation of a multimodal LLM-based Multi-Agent System (MAS) leveraging a No-Code platform to address the practical constraints and significant entry barriers associated with AI adoption in enterprises. Advanced AI technologies, such as Large Language Models (LLMs), often pose challenges due to their technical complexity and high implementation costs, making them difficult for many organizations to adopt. To overcome these limitations, this research develops a No-Code-based Multi-Agent System designed to enable users without programming knowledge to easily build and manage AI systems. The study examines various use cases to validate the applicability of AI in business processes, including code generation from image-based notes, Advanced RAG-based question-answering systems, text-based image generation, and video generation using images and prompts. These systems lower the barriers to AI adoption, empowering not only professional developers but also general users to harness AI for significantly improved productivity and efficiency. By demonstrating the scalability and accessibility of No-Code platforms, this study advances the democratization of AI technologies within enterprises and validates the practical applicability of Multi-Agent Systems, ultimately contributing to the widespread adoption of AI across various industries.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 16 of its 38 references that are in the map. First posted on arXiv 1 Jan 2025.

Builds on

16 of 38 references.

References · 16

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2024GPT-4o System Cardcited by 4,588
  3. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  4. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  5. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  6. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  7. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  8. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  9. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  10. 2022A Generalist Agentcited by 808
  11. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
  12. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
4 more
  1. 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 164
  2. 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecturecited by 28
  3. 2024Reasoning Limitations of Multimodal Large Language Models. A Case Study of Bongard Problemscited by 15
  4. 2022Multimodal and Crossmodal AI for Smart Data Analysiscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Large Language Models Miss the Multi-Agent Markcited by 26
  2. 2025A Study on the MCP x A2A Framework for Enhancing Interoperability of LLM-based Autonomous Agentscited by 8
  3. 2025E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processingcited by 5
  4. 2025An Agentic AI for a New Paradigm in Business Process Developmentcited by 2
  5. 2025A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AIcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024Large Multimodal Agents: A Survey
  2. 2026LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review
  3. 2025LLM-Powered AI Agent Systems and Their Applications in Industry
  4. 2024Large Language Model Evaluation Via Multi AI Agents: Preliminary results
  5. 2024Exploring Autonomous Agents through the Lens of Large Language Models: A Review
  6. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead
  7. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
  8. 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report