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English(EN) Anatomy of a Sound Neural Reasoner: One-Shot Amortization, First-Pass Poisoning, and Search Inertness in Clue-Rich Completion

神经推理器充当单次预测器，而非迭代求解器

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新论文介绍了一种名为 Lattice Deduction Transformer (LDT) 的神经求解器，它与预期相反，在充满线索的数独游戏中充当单次预测器，而不是迭代推理器。研究表明，该模型在单次传递中就完成了大部分网格的填充，这种现象被称为“首次通过投毒”，即在任何搜索过程开始之前就自信地删除了错误的值。虽然添加诸如回溯之类的搜索机制可以显著减少冗余计算，但并不能提高准确性，这表明优化和样本效率是关键优势。该论文提出了两种干预措施：数字排列增强和测试时对对称变换后的传递进行联合处理，这些措施在不重新训练的情况下将准确性整体提升至 100%。 AI

影响 这项研究突显了当前神经求解器架构的潜在局限性，表明需要新的方法来实现真正的迭代推理能力。

排序理由 该集群包含一篇详细介绍新神经网络架构及其在特定任务上性能的学术论文。

在 Hugging Face Daily Papers 阅读 →

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神经推理器充当单次预测器，而非迭代求解器

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Aleksey Komissarov ·

    一个强大的神经推理器的解剖：线索丰富的补全中的单次摊销、首次通过投毒和搜索惰性

    arXiv:2607.19635v1 Announce Type: cross Abstract: Neural solvers are built to deduce, branch, and revise intermediate states. The Lattice Deduction Transformer (LDT) appears to do exactly that. In clue-rich Sudoku, it does not: one forward pass commits essentially the entire grid…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    一个强大的神经推理器的解剖：线索丰富的补全中的单次摊销、首次通过投毒和搜索惰性

    Neural solvers are built to deduce, branch, and revise intermediate states. The Lattice Deduction Transformer (LDT) appears to do exactly that. In clue-rich Sudoku, it does not: one forward pass commits essentially the entire grid (every blank cell on standard 6x6, 94-96% on augm…

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