L2MAC: Large Language Model Automatic Computer for Extensive Code Generation

TL;DR

L2MAC is a novel LLM-based framework that enables long, coherent output generation by mimicking a stored-program computer architecture, surpassing existing methods in large codebase and extensive text generation tasks.

Contribution

This paper introduces L2MAC, a practical multi-agent LLM system with a dual-component memory architecture, allowing for extensive output generation beyond fixed context limitations.

Findings

Achieves state-of-the-art performance in large codebase generation.

Successfully generates entire books and complex texts.

Outperforms existing coding and text generation methods.

Abstract

Transformer-based large language models (LLMs) are constrained by the fixed context window of the underlying transformer architecture, hindering their ability to produce long and coherent outputs. Memory-augmented LLMs are a promising solution, but current approaches cannot handle long output generation tasks since they (1) only focus on reading memory and reduce its evolution to the concatenation of new memories or (2) use very specialized memories that cannot adapt to other domains. This paper presents L2MAC, the first practical LLM-based general-purpose stored-program automatic computer (von Neumann architecture) framework, an LLM-based multi-agent system, for long and consistent output generation. Its memory has two components: the instruction registry, which is populated with a prompt program to solve the user-given task, and a file store, which will contain the final and intermediate outputs. Each instruction in turn is executed by a separate LLM agent, whose context is managed by a control unit capable of precise memory reading and writing to ensure effective interaction with the file store. These components enable L2MAC to generate extensive outputs, bypassing the constraints of the finite context window while producing outputs that fulfill a complex user-specified task. We empirically demonstrate that L2MAC achieves state-of-the-art performance in generating large codebases for system design tasks, significantly outperforming other coding methods in implementing the detailed user-specified task; we show that L2MAC works for general-purpose extensive text-based tasks, such as writing an entire book; and we provide valuable insights into L2MAC's performance improvement over existing methods.