This project is an LLM research orchestrator and autonomous AI agent framework designed to automate the scientific lifecycle. It functions as an end-to-end research pipeline and model training toolkit, managing everything from initial literature reviews and hypothesis testing to the final drafting of academic papers.
The system is distinguished by its ability to convert unstructured academic PDFs into machine-executable knowledge layers, allowing agents to reproduce and extend research findings. It employs a two-loop orchestration architecture and a specialized research engineering skill library to guide autonomous agents through complex scientific workflows.
The platform covers a broad set of capabilities including distributed model training, parameter-efficient fine-tuning, and automated GPU infrastructure provisioning. It provides tools for mechanistic interpretability, research rigor review, and the generation of publication-ready visualizations and analysis reports.
The project is implemented primarily using TeX for academic document generation.
