让神经算法推理模型找出多个正确解，突破单一输出限制。
Neural Algorithmic Reasoning with Multiple Correct Solutions
- 设计新训练流程，生成多解数据并从模型输出采样验证
- 在贝尔曼-福特和深度优先搜索任务中成功获取多个正确路径
- 为神经算法推理提供可复用的多解框架，适合算法学习研究者
神经算法推理（NAR）将经典算法拓展至高维数据。然而，传统NAR方法仅训练模型输出单一解，即便存在多个正确解（如单源最短路径）。针对需多解的应用场景，本文首次提出支持多解的NAR方法。实验聚焦贝尔曼-福特（BF）与深度优先搜索（DFS）两类经典算法，强调对算法本质的理解而非广泛覆盖。该方法包含生成适配训练数据、从模型输出采样及验证解的完整流程。整个框架不仅适用于本文任务，亦可作为未来神经算法推理研究的通用范式。本工作首次在NAR领域实现多解生成，具有开创性意义。
原文摘要 · Abstract (English)
Neural Algorithmic Reasoning (NAR) extends classical algorithms to higher dimensional data. However, canonical implementations of NAR train neural networks to return only a single solution, even when there are multiple correct solutions to a problem, such as single-source shortest paths. For some applications, it is desirable to recover more than one correct solution. To that end, we give the first method for NAR with multiple solutions. We demonstrate our method on two classical algorithms: Bellman-Ford (BF) and Depth-First Search (DFS), favouring deeper insight into two algorithms over a broader survey of algorithms. This method involves generating appropriate training data as well as sampling and validating solutions from model output. Each step of our method, which can serve as a framework for neural algorithmic reasoning beyond the tasks presented in this paper, might be of independent interest to the field and our results represent the first attempt at this task in the NAR literature.
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