LLM-Guided Program Evolution for Circle Packing: Breaking 10 Packomania Records for $28
Read the original on arXiv AI →The paper introduces Discovery Loop, a lightweight system that employs a large language model (LLM) to iteratively evolve optimization algorithms for the Packomania circle‑packing benchmark. Starting from a simple seed solver, the LLM proposes algorithmic improvements guided by a scoreboard and a history of prior ideas, evaluates each candidate against an independent verifier, and retains only successful changes. Within 15 iterations and a total LLM cost of $27.72, the system broke 10 Packomania records for N between 101 and 114, improving the best known solutions by 2.4%–5.4%. The work demonstrates that a cost‑efficient, LLM‑driven approach can rapidly advance state‑of‑the‑art solutions in a complex optimization domain, suggesting broader potential for democratizing automated scientific discovery.
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