AI 中文总结
AutoSND是一种三阶段树搜索框架，通过将执行证据转化为结构策略，在真实网络上发现了更优的网络拆解启发式方法，性能与稳定性更佳且结构可解释。
AI 中文摘要
网络拆解是分析复杂系统鲁棒性与脆弱性的基础，但实用启发式方法需在有效性与计算效率间取得平衡，且通常由研究人员手动设计。现有基于大语言模型的自动启发式设计方法虽能生成并筛选候选方案，却难以将执行过程中的候选质量或失败状态进一步转化为对后续生成的结构级指导。本文提出AutoSND，一种用于完整网络拆解程序的三阶段树搜索框架：阶段I从简单启发式方法出发进行广泛探索，归档执行证据；阶段II将候选记录编译为涉及局部信号、邻域访问及状态更新范围的结构策略；阶段III基于这些策略继续树搜索，得到最终的质量优先与速度优先候选方案AutoSND-Q/S。在12个真实网络及3个大型真实网络上的实验表明，AutoSND实现了更优的搜索性能与稳定性，发现了更具竞争力且结构可解释的网络拆解程序。最终候选方案形成了以剩余度为核心、通过有界局部信号调整节点顺序并限制状态更新范围的可解释结构。代码可访问此https URL。
英文摘要
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness and computational efficiency, and are usually designed manually by researchers. Existing large language model based automatic heuristic design methods can generate and screen candidates, yet they have difficulty further transforming candidate quality or failure states during execution into structural-level guid- ance for subsequent generation. We propose AutoSND, a three stage tree search framework for complete network dismantling pro- grams. Stage I broadly explores from simple heuristics and archives execution evidence. Stage II compiles candidate records into struc- tural policies concerning local signals, neighborhood access, and state update ranges. Stage III continues tree search conditioned on these policies and obtains the final quality prioritized and speed prioritized candidates, AutoSND-Q/S. Experiments on 12 real world networks and 3 large real world networks show that AutoSND achieves better search performance and stability and discovers more competitive and structurally interpretable network disman- tling programs. The final candidates form an interpretable structure that uses residual degree as the backbone, adjusts node order with bounded local signals, and restricts the state update range. Code is available at https://github.com/MirrorNew/AutoSND.
