How it works
The input to our system is a scorable task, which includes a problem description, a scoring metric, and data suitable for training, validation, and evaluation. A user can also provide context, such as ideas from external literature, or directives for methodologies to prioritize.
The system then generates research ideas, including programmatic reproduction, optimization, and recombination of known methods, leading to novel and highly performant approaches. Ideas are implemented as executable code and the system uses a tree search strategy with an upper confidence bound (inspired by AlphaZero) to create a tree of software candidates and decide which candidates warrant further exploration. It then uses an LLM to rewrite the code to attempt to improve its quality score, and can exhaustively and tirelessly carry out solution searches at an unprecedented scale, identifying high-quality solutions quickly, reducing exploration time from months to hours or days. Its outputs, as coded solutions, are verifiable, interpretable and reproducible.
