通过演化程序瓶颈解释神经组合优化
Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks
AI总结：
提出演化程序瓶颈（EPB）框架，通过将黑盒神经组合优化模型蒸馏为可读程序组合，利用LLM和混合梯度下降实现可解释性，揭示模型行为与经典启发式变体的关系。
摘要：
神经组合优化（NCO）取得了强劲性能，但其黑盒性质仍然是部署和科学诊断的关键障碍。标准可解释性工具（如概念瓶颈模型）不适用于NCO，因为其决策是动态的、状态依赖的，且缺乏适当的概念词汇定义。为弥合这一差距，我们引入了演化程序瓶颈（EPB），据我们所知，这是首个通过将黑盒NCO模型蒸馏为人类可读程序组合来解释NCO策略的框架。EPB利用LLM自主演化一组程序，其中每个程序的每步动作分布作为瓶颈。EPB通过迭代框架工作：模块I固定程序库容量，并引入混合文本-数值梯度下降方案，该方案将学生路由器更新的数值梯度和基于LLM程序修订的文本梯度相结合；模块II通过故障目标扩展和冗余剪枝动态调整库容量。大量实验证明了EPB的有效性和广泛适用性，蒸馏后的程序组合在很大程度上保持了原始性能。EPB还揭示了NCO行为在优化阶段的变化，并且可以近似为经典启发式变体的组合。我们的工作推进了可解释NCO，并将EPB建立为解释序列决策模型的有前途工具。
英文摘要：
Neural Combinatorial Optimization (NCO) achieves strong performance, yet its black-box nature remains a key roadblock to deployment and scientific diagnosis. Standard interpretability tools, such as Concept Bottleneck Models (CBMs), are ill-equipped for NCO, whose decisions are dynamic, state-dependent, and lack proper concept vocabulary definition. To close this gap, we introduce Evolving Programmatic Bottlenecks (EPB), to our knowledge, the first framework for interpreting NCO policies by distilling black-box NCO models into human-readable program portfolios. EPB employs an LLM to autonomously evolve a bank of programs, where each program's per-step action distribution serves as the bottleneck. EPB works through an iterative framework: Block I fixes program bank capacity and introduces a hybrid textual-numerical gradient descent scheme that couples numerical gradients for student router updates and textual gradients for LLM-based program revision; Block II dynamically adapts bank capacity via fault-targeted expansion and redundancy pruning. Extensive experiments demonstrate EPB's effectiveness and broad applicability, where the distilled program portfolios largely match original performance. EPB also reveals that NCO behavior shifts across optimization stages and can be approximated as a composition of classic heuristic variants. Our work advances interpretable NCO and establishes EPB as a promising tool for interpreting sequential decision-making models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
