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CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis

Gautham Ramachandran, Rick Yang

TL;DR

Experimental evaluations demonstrate that CortexCompile consistently outperforms GPT-4o in development time, accuracy, and user satisfaction, particularly in tasks involving real-time strategy games and first-person shooters, underscore the viability of neuroscience-inspired architectures in addressing the limitations of current NLP models.

Abstract

Current approaches to automated code generation often rely on monolithic models that lack real-time adaptability and scalability. This limitation is particularly evident in complex programming tasks that require dynamic adjustment and efficiency. The integration of neuroscience principles into Natural Language Processing (NLP) has the potential to revolutionize automated code generation. This paper presents CortexCompile, a novel modular system inspired by the specialized functions of the human brain's cortical regions. By emulating the distinct roles of the Prefrontal Cortex, Parietal Cortex, Temporal Lobe, and Motor Cortex, CortexCompile achieves significant advancements in scalability, efficiency, and adaptability compared to traditional monolithic models like GPT-4o. The system's architecture features a Task Orchestration Agent that manages dynamic task delegation and parallel processing, facilitating the generation of highly accurate and optimized code across increasingly complex programming tasks. Experimental evaluations demonstrate that CortexCompile consistently outperforms GPT-4o in development time, accuracy, and user satisfaction, particularly in tasks involving real-time strategy games and first-person shooters. These findings underscore the viability of neuroscience-inspired architectures in addressing the limitations of current NLP models, paving the way for more efficient and human-like AI systems.

CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis

TL;DR

Experimental evaluations demonstrate that CortexCompile consistently outperforms GPT-4o in development time, accuracy, and user satisfaction, particularly in tasks involving real-time strategy games and first-person shooters, underscore the viability of neuroscience-inspired architectures in addressing the limitations of current NLP models.

Abstract

Current approaches to automated code generation often rely on monolithic models that lack real-time adaptability and scalability. This limitation is particularly evident in complex programming tasks that require dynamic adjustment and efficiency. The integration of neuroscience principles into Natural Language Processing (NLP) has the potential to revolutionize automated code generation. This paper presents CortexCompile, a novel modular system inspired by the specialized functions of the human brain's cortical regions. By emulating the distinct roles of the Prefrontal Cortex, Parietal Cortex, Temporal Lobe, and Motor Cortex, CortexCompile achieves significant advancements in scalability, efficiency, and adaptability compared to traditional monolithic models like GPT-4o. The system's architecture features a Task Orchestration Agent that manages dynamic task delegation and parallel processing, facilitating the generation of highly accurate and optimized code across increasingly complex programming tasks. Experimental evaluations demonstrate that CortexCompile consistently outperforms GPT-4o in development time, accuracy, and user satisfaction, particularly in tasks involving real-time strategy games and first-person shooters. These findings underscore the viability of neuroscience-inspired architectures in addressing the limitations of current NLP models, paving the way for more efficient and human-like AI systems.
Paper Structure (30 sections, 2 equations, 6 figures, 1 table)

This paper contains 30 sections, 2 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Motivation
  4. Research Objectives
  5. Related Work
  6. Large Language Models in Code Generation
  7. Neuroscience-Inspired AI Architectures
  8. Multi-Agent Systems
  9. Conceptual Framework
  10. Cortical Specialization in the Brain
  11. Agent Design
  12. System Architecture
  13. Interaction and Communication Mechanisms
  14. Implementation Details
  15. Model Specifications
  16. ...and 15 more sections

Figures (6)

  • Figure 1: Cortical Regions
  • Figure 2: CortexCompile High Level System Architecture
  • Figure 3: Range of coding tasks and their increasing complexity from Pacman to FPS Game.
  • Figure 4: Development time comparison between CortexCompile and GPT-4o for each task.
  • Figure 5: Accuracy comparison between CortexCompile and GPT-4o across different tasks.
  • ...and 1 more figures