The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction

• URL: http://arxiv.org/abs/2606.25986v1
• Date: Wed, 24 Jun 2026 15:54:09 GMT
• Title: The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
• Abstract summary: We find that the realized empirical frontier of predictive loss versus structural forward work is well summarized by a power law.<n>We motivate FastBiNLOB, a dense axis-separable LOB mixer built from hardware-friendly temporal and feature mixing operations.<n>FastBiNLOB exceeds the published $y_10$ and $y_100$ macro-F1 targets at notably lower latency than existing published SOTA architectures.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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