Reverse Incentive Generation Mechanism for Agent Calibration and Stability
DOI:
https://doi.org/10.71222/bek1kw32Keywords:
agent, calibration, stability, reverse incentive generation mechanism, model constructionAbstract
This paper focuses on the reverse incentive generation mechanism for agent calibration and stability. Firstly, it clarifies the background and core significance of researching this mechanism, then conducts an in-depth analysis of various factors affecting agent calibration and stability. Finally, aiming at these issues, it discusses in detail the operational principles and specific methods of the reverse incentive generation mechanism in agent calibration and stability improvement. By establishing a reasonable reverse incentive model combined with practical cases, the paper analyzes its application effects, aiming to provide theoretical support and practical guidance for enhancing agent performance, strengthening its reliability and stability in practical applications, and promoting the development and application of agent technology in various fields.References
1. Z. Fan, B. Ghaddar, X. Wang, L. Xing, Y. Zhang, and Z. Zhou, "Artificial intelligence for operations research: Revolutionizing the operations research process," arXiv preprint arXiv:2401.03244, 2024.
2. C. Dormoy, J. M. André, and A. Pagani, "A human factors' approach for multimodal collaboration with Cognitive Computing to create a Human Intelligent Machine Team: a Review," In IOP Conference Series: Materials Science and Engineering, 2021, p. 012105.
3. H. C. W. Lau, S. K. Tso, and J. K. L. Ho, "Development of an intelligent task management system in a manufacturing information network," Expert Systems with Applications, vol. 15, no. 2, pp. 165-179, 1998.
4. R. Yang, D. Rajagopal, S. A. Hayati, B. Hu, and D. Kang, "Confidence calibration and rationalization for llms via multi-agent deliberation," arXiv preprint arXiv:2404.09127, 2024.
5. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," nature, vol. 521, no. 7553, pp. 436-444, 2015.
6. W. F. Wiggins, and A. S. Tejani, "On the opportunities and risks of foundation models for natural language processing in radiology," Radiology: Artificial Intelligence, vol. 4, no. 4, p. e220119, 2022. doi: 10.1148/ryai.220119
7. M. R. Endsley, "From here to autonomy: lessons learned from human-automation research," Human factors, vol. 59, no. 1, pp. 5-27, 2017.
8. S. Raisch, and S. Krakowski, "Artificial intelligence and management: The automation-augmentation paradox," Academy of management review, vol. 46, no. 1, pp. 192-210, 2021. doi: 10.5465/2018.0072
9. J. Zhang, and D. A. Norman, "Representations in distributed cognitive tasks," Cognitive science, vol. 18, no. 1, pp. 87-122, 1994. doi: 10.1207/s15516709cog1801_3
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Copyright (c) 2026 Juyi Yang (Author)

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