[R] LLM Agent Operating System - Rutgers University 2024 - AIOS

Paper: [https://arxiv.org/abs/2403.16971](https://arxiv.org/abs/2403.16971)

Github: [https://github.com/agiresearch/AIOS](https://github.com/agiresearch/AIOS)

Abstract:

>The integration and deployment of large language model (LLM)-based intelligent agents have been fraught with challenges that compromise their efficiency and efficacy. Among these issues are sub-optimal scheduling and resource allocation of agent requests over the LLM, the difficulties in maintaining context during interactions between agent and LLM, and the complexities inherent in integrating heterogeneous agents with different capabilities and specializations. The **rapid increase of agent quantity and complexity** further exacerbates these issues, often leading …

abstract agent agents capabilities challenges complexities context deployment efficiency integration intelligent interactions language language model large language large language model llm machinelearning scheduling