MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models

TL;DR

This paper introduces MaxMind, a memory loop network that enhances large language models' ability to retain and utilize task experiences, significantly improving software productivity and task success rates in automated tool generation.

Contribution

The paper proposes Memory-Loop Networks with value-based memory segmentation and a MaxMind model for improved experience referencing and task performance in LLM-based software operations.

Findings

Memory recycling improves task success rate by 3-6% per iteration.

Memory growth can lead to substantial cumulative performance gains.

Memory transfer addresses retraining issues in specialized tasks.

Abstract

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.