MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models
<|reference_start|>MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models: The application of large language models to facilitate automated software operations and tool generation (SOTG), thus augmenting software productivity, mirrors the early stages of human evolution when the ability to create and use tools accelerated the progress of civilization. These complex tasks require AI to continuously summarize and improve. Current research often overlooks the importance of converting real-time task experiences into system memory and differentiating the value of existing knowledge for future reference. This paper addresses these issues by evolving external memory models into Memory-Loop Networks for timely memorization and experience referencing. We also enhance a RAG mechanism with knowledge precision segmentation to utilize memory based on value differentiation, and design the MaxMind model for SOTG accordingly.To demonstrate our approach, we developed MaxMind4Sheet, an electronic spreadsheet processing system aligned with the MaxMind philosophy. Comparative experiments with SheetCopilot have demonstrated that the accumulation and recycling of task memories lead to a steady enhancement in task success rate, with an improvement rate of approximately 3%-6% per round in this implementation example. Note that as the memories continue to grow, this cumulative improvement may be substantial. The inclusion of memory recycling can also boost the system's task execution efficiency by up to 25%, and it can address the retraining issue faced by LLMs when handling specialized tasks through memories transfer.These suggest that MaxMind has significant potential to enhance the capabilities and productivity of LLM systems in SOTG.<|reference_end|>
@article{dong2024maxmind:,
    title={MaxMind: A Memory Loop Network to Enhance Software Productivity based on
  Large Language Models},
    author={Yuchen Dong, XiaoXiang Fang, Yuchen Hu, Renshuang Jiang, Zhe Jiang},
    journal={arXiv preprint arXiv:2408.03841},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03841},
    primaryClass={cs.SE cs.AI}
}