Autodeleveraging (ADL) is a last-resort loss socialization mechanism used by perpetual futures venues when liquidation and insurance buffers are insufficient to restore solvency. Despite the scale of perpetual futures markets, ADL has received limited formal treatment as a sequential control problem. This paper provides a concise formalization of ADL as online learning on a PNL-haircut domain: at each round, the venue selects a solvency budget and a set of profitable trader accounts. The profitable accounts are liquidated to cover shortfalls up to the solvency budget, with the aim of recovering exchange-wide solvency. In this model, ADL haircuts apply to positive PNL (unrealized gains), not to posted collateral principal. Using our online learning model, we provide robustness results and theoretical upper bounds on how poorly a mechanism can perform at recovering solvency. We apply our model to the October 10, 2025 Hyperliquid stress episode. The regret caused by Hyperliquid's production ADL queue is about 50\% of an upper bound on regret, calibrated to this event, while our optimized algorithm achieves about 2.6\% of the same bound. In dollar terms, the production ADL model over liquidates trader profits by up to \$51.7M. We also counterfactually evaluated algorithms inspired by our online learning framework that perform better and found that the best algorithm reduces overshoot to \$3M. Our results provide simple, implementable mechanisms for improving ADL in live perpetuals exchanges.

翻译：自动减仓（ADL）是永续期货交易所在清算与保险缓冲资金不足以恢复偿付能力时采用的一种最后损失社会化机制。尽管永续期货市场规模庞大，但ADL作为一种序列控制问题所获得的正式研究有限。本文在PNL-扣减域上，将ADL简明形式化为在线学习问题：在每一轮中，交易所选择一个偿付预算和一组盈利交易者账户。盈利账户将被清算以覆盖偿付预算内的资金缺口，旨在恢复全交易所范围的偿付能力。在此模型中，ADL扣减适用于正向PNL（未实现收益），而非已质押的抵押品本金。利用我们的在线学习模型，我们提供了关于机制恢复偿付能力表现的理论鲁棒性结果与上界。我们将模型应用于2025年10月10日的Hyperliquid压力事件。Hyperliquid生产环境ADL队列造成的遗憾约为针对该事件校准的遗憾上界的50%，而我们优化后的算法仅达到同一上界的约2.6%。以美元计，生产环境ADL模型对交易者利润的超额清算高达5170万美元。我们还通过反事实评估了受在线学习框架启发的改进算法，发现最优算法能将超额清算降至300万美元。我们的研究结果为改进现有永续交易所的ADL机制提供了简单且可实施的方案。