autoresearch for AMD

autoresearch for AMD is a framework for autonomous scientific experimentation in machine learning, enabling AI agents to iteratively improve models through a continuous loop of hypothesis generation, experimentation, and evaluation. The system is built around a minimal structure that includes a data preparation module, a training script that can be modified, and a program specification that guides the agent’s decision-making process. During each iteration, the agent edits the training code, runs an experiment within a fixed time budget, evaluates performance metrics, and decides whether to retain or discard the changes. This loop allows the system to explore a wide range of architectural and hyperparameter configurations without human intervention. The framework emphasizes simplicity and reproducibility, ensuring that experiments are comparable and results are traceable over time.