这篇论文用实证发现限价订单簿预测存在幂律计算前沿，还设计了一个低延迟架构FastBiNLOB，在FI-2010上比现有模型更快更准。做量化金融或序列预测的值得看。
该论文研究限价订单簿预测中是否存在类似缩放定律的推理-计算前沿。使用FI-2010数据集，从决策树到MLPLOB等模型评估发现，预测损失与结构前向工作量的经验前沿可用幂律很好拟合（排除MLPLOB族时，R²=0.941）。延迟空间的实验效果显著较弱，表明延迟不只是带噪声的计算。作者提出FastBiNLOB，一种密集轴可分离的LOB mixer，结合硬件友好的时间与特征混合操作。在五组种子实验中，FastBiNLOB以比现有SOTA架构明显更低的延迟超越了已发表的y₁₀和y₁₀₀ macro-F1目标。
The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
We study whether a scaling-law-style inference-compute frontier appears in limit order book prediction. Using FI-2010 and a suite of models ranging from small decision trees to neural LOB architectures, we find that the realized empirical frontier of predictive loss versus structural forward work is well summarized by a power law. In particular, with MLPLOB held out as an architecture family, a power-law fit to the low- and mid-compute non-MLPLOB frontier extrapolates across multiple orders of magnitude and attains $R^2=0.941$ on the excluded high-compute MLPLOB target frontier. A similar exercise in latency space gives substantially weaker results, showing that latency is not merely noisy compute. We use this gap to motivate FastBiNLOB, a dense axis-separable LOB mixer built from hardware-friendly temporal and feature mixing operations. In a five-seed experiment, FastBiNLOB exceeds the published $y_{10}$ and $y_{100}$ macro-F1 targets at notably lower latency than existing published SOTA architectures.