Journal of System Simulation
Abstract
Abstract: Traditional source search algorithms are prone to local optimization, and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention. In this study, we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models (MLLMs) to achieve visual-language conversion, combined chain-of-thought (CoT) reasoning to optimize decision-making, and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration. The effectiveness of the framework was verified by experiments. The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual dependency for source search task in complex scenes.
Recommended Citation
Chen, Yi; Qiu, Sihang; Zhu, Zhengqiu; Ji, Yatai; Zhao, Yong; and Ju, Rusheng
(2025)
"A Method of Heuristic Human-LLM Collaborative Source Search,"
Journal of System Simulation: Vol. 37:
Iss.
12, Article 12.
DOI: 10.16182/j.issn1004731x.joss.25-FZ0646E
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss12/12
First Page
3112
Last Page
3127
CLC
TP391.9
Recommended Citation
Chen Yi, Qiu Sihang, Zhu Zhengqiu, et al. A Method of Heuristic Human-LLM Collaborative Source Search[J]. Journal of System Simulation, 2025, 37(12): 3112-3127.
DOI
10.16182/j.issn1004731x.joss.25-FZ0646E
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