Researchers have developed Janus, a novel framework designed to enhance Large Language Model (LLM)-driven discovery in scientific and engineering domains where evaluations are costly. Janus addresses the challenge of expensive, high-fidelity simulations or experiments by using LLMs to co-evolve both target programs and proxy evaluators. This approach leverages LLMs to generate task-specific evaluator programs and calibrates them with real outcomes, significantly reducing the number of expensive real evaluations needed. In tests across five design tasks, Janus achieved comparable final performance to a baseline method using fewer real evaluations, demonstrating its effectiveness in domains with scarce and expensive feedback. AI
IMPACT Enables LLM-driven discovery in scientific and engineering fields with expensive evaluation budgets.
RANK_REASON The cluster describes a new framework and methodology presented in an academic paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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