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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2507.21636 |
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| _version_ | 1866915416790532096 |
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| author | Maritan, Alessio |
| author_facet | Maritan, Alessio |
| contents | Large language model (LLM) agents integrate pre-trained LLMs with modular algorithmic components and have shown remarkable reasoning and decision-making abilities. In this work, we investigate their use for two tightly intertwined challenges in workforce management: staffing, i.e., the assignment and scheduling of tasks to workers, which may require team formation; and profiling, i.e., the continuous estimation of workers' skills, preferences, and other latent attributes from unstructured data. We cast these problems in a formal mathematical framework that links scheduling decisions to latent feature estimation, and we introduce StaffPro, an LLM agent that addresses staffing and profiling jointly. Differently from existing staffing solutions, StaffPro allows expressing optimization objectives using natural language, accepts textual task descriptions and provides high flexibility. StaffPro interacts directly with humans by establishing a continuous human-agent feedback loop, ensuring natural and intuitive use. By analyzing human feedback, our agent continuously estimates the latent features of workers, realizing life-long worker profiling and ensuring optimal staffing performance over time. A consulting firm simulation example demonstrates that StaffPro successfully estimates workers' attributes and generates high quality schedules. With its innovative design, StaffPro offers a robust, interpretable, and human-centric solution for automated personnel management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StaffPro: an LLM Agent for Joint Staffing and Profiling Maritan, Alessio Artificial Intelligence Large language model (LLM) agents integrate pre-trained LLMs with modular algorithmic components and have shown remarkable reasoning and decision-making abilities. In this work, we investigate their use for two tightly intertwined challenges in workforce management: staffing, i.e., the assignment and scheduling of tasks to workers, which may require team formation; and profiling, i.e., the continuous estimation of workers' skills, preferences, and other latent attributes from unstructured data. We cast these problems in a formal mathematical framework that links scheduling decisions to latent feature estimation, and we introduce StaffPro, an LLM agent that addresses staffing and profiling jointly. Differently from existing staffing solutions, StaffPro allows expressing optimization objectives using natural language, accepts textual task descriptions and provides high flexibility. StaffPro interacts directly with humans by establishing a continuous human-agent feedback loop, ensuring natural and intuitive use. By analyzing human feedback, our agent continuously estimates the latent features of workers, realizing life-long worker profiling and ensuring optimal staffing performance over time. A consulting firm simulation example demonstrates that StaffPro successfully estimates workers' attributes and generates high quality schedules. With its innovative design, StaffPro offers a robust, interpretable, and human-centric solution for automated personnel management. |
| title | StaffPro: an LLM Agent for Joint Staffing and Profiling |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.21636 |