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Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving

by Xinyu Zhang arxiv/2604.20183
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Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation. To address this, we propose Dual-Cluster Memory Agent (DCM-Agent) to enhance performance by leveraging historical solutions in a training-free manner. Central to this is Dual-Cluster Memory Construction. This agent assigns historical solutions to modeling and coding clusters, ...

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2026 Year
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Registry ID 2604.20183
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BibTeX
@misc{arxiv_2604_20183,
  author = {Xinyu Zhang},
  title = {Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.20183}},
  note = {Accessed via Free2AITools.}
}
APA Style
Xinyu Zhang. (2026). Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving [Paper]. Free2AITools. https://arxiv.org/abs/2604.20183

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Semantic (S) 50

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Authority (A) 0
Popularity (P) 0
Recency (R) 52
Quality (Q) 60

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πŸ“ Executive Summary

"Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation. To address this, we propose Dual-Cluster Memory Agent (DCM-Agent) to enhance performance by leveraging historical solutions in a training-free manner. Central to this is Dual-Cluster Memory Construction. This agent assigns historical solutions to modeling and coding clusters, ..."

❝ Cite Node

@article{Zhang2026DualCluster,
  title={Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving},
  author={Xinyu Zhang},
  journal={arXiv preprint arXiv:2604.20183},
  year={2026}
}

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Xinyu Zhang

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2604.20183
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2604.20183
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Xinyu Zhang
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