[2310.09049] SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network
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Credibility Rating
Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: arXiv
This paper is tangentially relevant to AI safety, focusing primarily on applying LLMs to communication network tasks; its relevance to the safety knowledge base is limited to capability evaluation and deployment risk considerations in critical infrastructure contexts.
Paper Details
Metadata
Abstract
In the rapid development of artificial intelligence, solving complex AI tasks is a crucial technology in intelligent mobile networks. Despite the good performance of specialized AI models in intelligent mobile networks, they are unable to handle complicated AI tasks. To address this challenge, we propose Systematic Artificial Intelligence (SAI), which is a framework designed to solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format intent-based input to connect self-designed model library and database. Specifically, we first design a multi-input component, which simultaneously integrates Large Language Models (LLMs) and JSON-format intent-based inputs to fulfill the diverse intent requirements of different users. In addition, we introduce a model library module based on model cards which employ model cards to pairwise match between different modules for model composition. Model cards contain the corresponding model's name and the required performance metrics. Then when receiving user network requirements, we execute each subtask for multiple selected model combinations and provide output based on the execution results and LLM feedback. By leveraging the language capabilities of LLMs and the abundant AI models in the model library, SAI can complete numerous complex AI tasks in the communication network, achieving impressive results in network optimization, resource allocation, and other challenging tasks.
Summary
This paper proposes SAI, a systematic AI framework for solving diverse AI tasks in communication networks by integrating large language models with structured reasoning approaches. It addresses how LLMs can be applied to network management and optimization problems through systematic decomposition of complex communication tasks. The work explores capability thresholds and risk assessment for AI deployment in critical network infrastructure.
Key Points
- •Introduces SAI framework that leverages LLMs to handle AI tasks in communication network environments systematically
- •Proposes structured task decomposition to break down complex network problems into manageable sub-tasks solvable by AI
- •Evaluates LLM capabilities and performance thresholds relevant to deployment in communication infrastructure
- •Addresses risk assessment considerations for AI systems operating in critical telecommunications contexts
- •Bridges the gap between general-purpose LLM capabilities and domain-specific network management requirements
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Capability Threshold Model | Analysis | 72.0 |
Cached Content Preview
\\jyear
2022
\[1,2\]\\fnmYong Zhang
1\]\\orgdivSchool of Electronic Engineering, \\orgnameBeijing University of Posts and Telecommunications, \\orgaddress\\cityBeijing, \\postcode100876, \\stateChina
2\]\\orgdivBeijing Key Laboratory of Work Safety Intelligent Monitoring, \\orgnameBeijing University of Posts and Telecommunications, \\orgaddress\\cityBeijing, \\postcode100876, \\stateChina
# SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network
\\fnmLei Yao
[yongzhang@bupt.edu.cn](mailto:yongzhang@bupt.edu.cn)\\fnmZilong Yan
\\fnmJialu Tian
\[\
\[\
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###### Abstract\
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In the rapid development of artificial intelligence, solving complex AI tasks is a crucial technology in intelligent mobile networks. Despite the good performance of specialized AI models in intelligent mobile networks, they are unable to handle complicated AI tasks. To address this challenge, we propose Systematic Artificial Intelligence (SAI), which is a framework designed to solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format intent-based input to connect self-designed model library and database. Specifically, we first design a multi-input component, which simultaneously integrates Large Language Models (LLMs) and JSON-format intent-based inputs to fulfill the diverse intent requirements of different users. In addition, we introduce a model library module based on model cards which employ model cards to pairwise match between different modules for model composition. Model cards contain the corresponding model’s name and the required performance metrics. Then when receiving user network requirements, we execute each subtask for multiple selected model combinations and provide output based on the execution results and LLM feedback. By leveraging the language capabilities of LLMs and the abundant AI models in the model library, SAI can complete numerous complex AI tasks in the communication network, achieving impressive results in network optimization, resource allocation, and other challenging tasks.\
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###### keywords:\
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Large Language Models, AI Tasks, Systematic Artificial Intelligence, Communication Network\
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## 1 Introduction\
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In recent years, with the development of 5G technology and computing power network, significant developments have occurred in network computing power and communication networks. This evolution has profoundly altered the way networks are constructed and AI tasks are executed. Additionally, with the significant breakthroughs achieved by LLM in human-like intelligence, handling complex AI tasks based on intent has become feasible. Recently, the key method of multi-agent collaboration based on LLM [shen2023hugginggpt](https://ar5iv.labs.arxiv.org/html/2310.09049#bib.bib1 ""); [hong2023metagpt](https://ar5iv.labs.arxiv.org/html/2310.09049#bib.bib2 ""); [tang2023towards](https://ar5iv.labs.arxiv.org/html/2310.09049#bib.bib3 ""), such as HuggingGPT [shen2023hugginggpt](https://ar5iv.labs.arxiv.org
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