In the field of high-frequency trading, time is money. A millisecond delay could result in a million-dollar loss. Nowadays, more and more quantitative teams rely on AI models to capture trading signals, but the question is — can the model's output be trusted? Once AI reasoning goes wrong, the consequences could be unimaginable.
Lagrange's DeepProve was born to solve this dilemma. It can generate a zero-knowledge proof while the trading model generates signals. This proof does not leak model weights and inputs, but ensures that the output is indeed the result calculated by the model based on specified rules. More importantly, DeepProve outperforms similar zkML libraries in performance, generating proofs 158 times faster and verifying them an astonishing 671 times faster, fully supporting millisecond-level trading rhythms.
This means that future high-frequency trading desks will no longer feature "black box AI," but rather "verifiable AI." Traders can truly validate first and then execute, avoiding the costs incurred from erroneous signals. For the financial industry, this not only enhances security but also provides a foundation of trust for the large-scale application of AI.
