Tsetlin机器中的压缩循环反馈：一项可复现的布尔有限状态机研究
Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study
浏览论文内容
中文总结 AI 辅助
本文提出一种通过异或折叠压缩循环反馈的Tsetlin机器，将480个子句激活降至96比特，在布尔有限状态机基准上以更窄接口取得与原始反馈相当的准确率，并强调无反馈对照组的重要性。
中文摘要 AI 辅助
小型设备上的序列推理要求模型在无需反复处理长输入记录的情况下保留有用的历史信息。循环Tsetlin机器（RTM）通过将一个时间步的布尔子句输出作为下一步的输入来提供这种记忆。然而，直接反馈会随子句库规模增长，可能使循环输入不必要地过宽。本文研究了一种固定宽度的替代方案。我们通过异或（XOR）折叠合并子句激活，在两种时间尺度上保留折叠后的比特，并将其阈值化回二值状态。所提出的设计将480个子句激活减少为96个循环比特。我们在一个具有显式转换规则、数据划分和随机种子的可复现布尔有限状态机基准上评估了该方法。在144次运行中，压缩模型在两个任务族上分别获得$61.47 \pm 6.74\\%$和$62.94 \pm 9.92\\%$的准确率。原始子句反馈使这些均值变化不到一个百分点，同时将循环宽度增加十倍，并将实测主机执行时间分别增加$4.38\times$和$3.71\times$。门控神经模型仍然更准确，而仅保留短期输入历史的无反馈对照组实现了相当或略高的准确率。在该基准上，折叠在更窄的接口下以较小的经验差距匹配原始反馈；这些发现也强调了在基准测试序列模型时，无反馈循环对照组的至关必要性。
英文摘要
Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin Machine (RTM) provides this memory by returning Boolean clause outputs from one time step as inputs to the next. Direct feedback, however, grows with the clause bank and can make the recurrent input unnecessarily wide. This paper investigates a fixed-width alternative. We combine clause activations by exclusive-OR (XOR) folding, retain the folded bits at two time scales, and threshold them back to a binary state. The resulting design reduces 480 clause activations to 96 recurrent bits. We evaluate the method on a reproducible Boolean finite-state-machine benchmark with explicit transition rules, data splits, and random seeds. Across 144 runs, the compressed model obtains $61.47 \pm 6.74\%$ and $62.94 \pm 9.92\%$ accuracy on the two task families. Raw clause feedback changes these means by less than one percentage point, while increasing the recurrent width tenfold and measured host execution time by $4.38\times$ and $3.71\times$. Gated neural models remain more accurate, and a no-feedback control retaining only short input history achieves comparable or slightly higher accuracy. On this benchmark, folding matches raw feedback within small empirical margins at a much narrower interface; these findings also underscore the critical necessity of no-feedback recurrence controls when benchmarking sequence models.
发表机构
- Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
- Newcastle University（纽卡斯尔大学）
机构由 AI 辅助整理，请以论文原文为准。
