data-science-autoresearch

What problem does it solve?

Designs end-to-end autonomous AI research systems that iteratively improve ML models by automatically preparing data, training, evaluating, and logging results.

Core Features & Use Cases

• End-to-end autoresearch framework: fixed data preparation, training harness, evaluation harness, agent program, and a blessed runner to enforce single-run discipline.
• Reproducible experiments: provenance stamping, environment manifest, and data lineage for auditable research automation.
• Rapid iteration: deterministic evaluation, simple baseline to improve upon, and automated logging of results and decisions across cycles.

Quick Start

Provide a problem statement and data characteristics; the system will generate and run a baseline autonomous autoresearch loop.