Summary

This repository curates AutoResearch use cases and implementations, an AI-driven framework for automating iterative optimization workflows. It targets engineers and researchers seeking accelerated performance gains via AI agents. The primary benefit is significant uplift across diverse technical domains.

How It Works

AutoResearch employs a prompt-guided AI agent to iteratively refine a target file against an evaluation metric. The agent edits, executes, evaluates, and commits/reverts changes in a loop. This methodology is portable, adapted from LLM training to GPU kernels, template engines, and predictive modeling, demonstrating flexible automated optimization.

Quick Start & Requirements

No single install command is provided. Implementations include autoresearch (Python, single GPU), autoresearch-mlx (Apple Silicon), autoresearch-win-rtx (Windows RTX), pi-autoresearch (generalized), and autoresearch-at-home (distributed). Hardware (GPU, Apple Silicon) and Python dependencies vary by implementation. Links to individual project repos are provided.

Highlighted Details

• 20 overnight improvements on hand-tuned LLM training code.
• 53% faster Shopify Liquid parse+render, 61% fewer allocations.
• GPU kernel performance boosted from 18 to 187 TFLOPS.
• Voice agent prompt scores improved from 0.728 to 0.969.
• Bitcoin price prediction formulas found with 50.5% RMSE improvement.
• Cross-scroll generalization nearly doubled for ink detection models.

Maintenance & Community

Contributions are welcomed via PRs/issues, favoring verifiable submissions (progress charts, public repos). No specific community channels or roadmap links are provided.

Licensing & Compatibility

Licensed under CC0 1.0 (Public Domain Dedication), offering maximum flexibility. This permissive license allows unrestricted commercial use, modification, and integration into closed-source projects.

Limitations & Caveats

The README does not explicitly list limitations. Effectiveness depends on agent quality, optimization target, and evaluation metric robustness. Potential issues like reward hacking have been noted in specific use cases.