Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search

• URL: http://arxiv.org/abs/2508.03661v2
• Date: Thu, 28 Aug 2025 09:35:57 GMT
• Title: Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
• Authors: He Wang, Liang Zeng,
• Abstract summary: We propose the first integration of large language model (LLM) guidance with domain-aware physical constraints for automated gravitational wave detection.<n>We show substantial performance improvements over state-of-the-art gravitational wave detection algorithms on the MLGWSC-1 benchmark dataset.<n>Our framework establishes a transferable methodology for automated algorithmic discovery across computational science domains.
• Score: 8.633654445285892
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Gravitational-wave signal detection with unknown source parameters buried in dynamic detector noise remains a formidable computational challenge. Existing approaches face core limitations from restrictive assumptions: traditional methods rely on predefined theoretical priors, while neural networks introduce hidden biases and lack interpretability. We propose Evolutionary Monte Carlo Tree Search (Evo-MCTS), the first integration of large language model (LLM) guidance with domain-aware physical constraints for automated gravitational wave detection. This framework systematically explores algorithmic solution spaces through tree-structured search enhanced by evolutionary optimization, combining MCTS for strategic exploration with evolutionary algorithms for solution refinement. The LLM component provides domain-aware heuristics while maintaining interpretability through explicit algorithmic pathway generation. Experimental validation demonstrates substantial performance improvements, achieving a 20.2% improvement over state-of-the-art gravitational wave detection algorithms on the MLGWSC-1 benchmark dataset and a remarkable 59.1% improvement over other LLM-based algorithm optimization frameworks. Beyond performance improvements, our framework establishes a transferable methodology for automated algorithmic discovery across computational science domains.

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