Researchers have developed AutoSND, a novel three-stage tree search framework designed to automatically discover effective heuristics for network dismantling. This method addresses the limitations of existing large language model approaches by transforming execution evidence and failure states into structural guidance for heuristic generation. Experiments on numerous real-world networks demonstrate that AutoSND yields superior search performance, stability, and produces more competitive and interpretable network dismantling programs, utilizing residual degree as a backbone and incorporating local signals and restricted state update ranges. AI
IMPACT Introduces a new method for automated heuristic discovery in complex systems, potentially applicable to AI safety and robustness.
RANK_REASON Academic paper detailing a new algorithmic framework. [lever_c_demoted from research: ic=1 ai=1.0]
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