mlsanityData to 7 Oct 2026

Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach

arXiv 2501.18320 ↗First posted 30 Jan 2025cs.AI eess.SPcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Automated optimization modeling (AOM) has evoked considerable interest with the rapid evolution of large language models (LLMs). Existing approaches predominantly rely on prompt engineering, utilizing meticulously designed expert response chains or structured guidance. However, prompt-based techniques have failed to perform well in the sensor array signal processing (SASP) area due the lack of specific domain knowledge. To address this issue, we propose an automated modeling approach based on retrieval-augmented generation (RAG) technique, which consists of two principal components: a multi-agent (MA) structure and a graph-based RAG (Graph-RAG) process. The MA structure is tailored for the architectural AOM process, with each agent being designed based on principles of human modeling procedure. The Graph-RAG process serves to match user query with specific SASP modeling knowledge, thereby enhancing the modeling result. Results on ten classical signal processing problems demonstrate that the proposed approach (termed as MAG-RAG) outperforms several AOM benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 24 of its 31 references that are in the map. First posted on arXiv 30 Jan 2025.

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24 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  2. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun

Other references · 22

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2017Graph Attention Networkscited by 8,854
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  7. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  8. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  9. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  10. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  11. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  12. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
10 more
  1. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  2. 2022PAL: Program-aided Language Modelscited by 635
  3. 2023Augmented Language Models: a Surveycited by 398
  4. 2024LongRoPE: Extending LLM Context Window Beyond 2 Million Tokenscited by 302
  5. 2022Iteratively Prompt Pre-trained Language Models for Chain of Thoughtcited by 75
  6. 2024LLMs Are Few-Shot In-Context Low-Resource Language Learnerscited by 68
  7. 2023OptiMUS: Optimization Modeling Using MIP Solvers and large language modelscited by 39
  8. 2024Language Models as Hierarchy Encoderscited by 26
  9. 2024Retrieval Augmented Zero-Shot Text Classificationcited by 4
  10. 2022Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspectivecited by 3

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