Janus: An Algorithm-Evaluator Co-Evolution Framework for LLM-Driven Discovery under Expensive Evaluation Budgets
This work addresses the high cost of ground-truth evaluation—such as high-fidelity simulation or physical experimentation—that limits large language model (LLM)-driven program discovery in scientific and engineering domains. To overcome this bottleneck, the authors propose Janus, a novel framework that co-evolves target programs and executable agent evaluators. Janus leverages LLMs to generate task-specific evaluators and integrates region-conditioned composition, online credit assignment, and a closed-loop validation mechanism with real-world feedback. This approach efficiently guides the search process under label scarcity and distributional shift. Evaluated across five diverse tasks, Janus achieves 99% of baseline performance using only 40.9% of the ground-truth evaluations on average, substantially improving sample efficiency and the accuracy of candidate ranking.