This research introduces an adaptive self-improvement system that enables LLMs to develop high-performance ML libraries for specialized hardware architectures without human intervention.

• Combines three key components: task specification, code generation, and critical evaluation
• Features a novel self-improvement loop where the system learns from failures and refines its approach
• Demonstrates effectiveness by generating over 100 ML kernels with performance comparable to human experts
• Achieves 92% success rate in developing complex ML libraries for specialized hardware

This breakthrough allows organizations to accelerate ML library development for domain-specific architectures without requiring scarce programming specialists, potentially democratizing access to high-performance ML systems.

Adaptive Self-improvement LLM Agentic System for ML Library Development