用智能代理主动预防DeFi借贷液化，比传统规则更准更有效。
From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention
- 基于生存分析构建可执行的智能代理，主动干预风险。
- 在4882个高危用户中实现零恶化率，挽救濒临液化的贷款。
- 能区分真实风险与垃圾清理，节省资金使用效率。
去中心化金融（DeFi）借贷协议如Aave v3依赖超额抵押保障贷款安全，但用户常因市场波动面临液化。现有风险管理工具采用静态健康因子阈值，反应迟缓且无法区分管理性‘垃圾清理’与真实违约。本文提出一个自主智能体，结合时间至事件（生存）分析，从预测转向执行。该代理感知风险、模拟反事实未来，并执行符合协议逻辑的干预措施，主动防止液化。我们引入基于数值稳定的XGBoost Cox比例风险模型的回溯期指标，统一不同交易类型的风险度量；结合波动率调整趋势评分，过滤市场短期噪声。为选择最优干预策略，设计反事实优化循环，模拟用户可能行为以最小化所需资本。在高保真、符合协议的Aave v3仿真器上对4,882个高风险用户进行验证。结果表明，该代理可在静态规则失效的临界风险场景中成功防止液化，实现‘拯救不可救者’的效果，同时保持零恶化率，提供自主金融代理中罕见的安全保障。此外，系统能有效区分可操作风险与微不足道的尘埃事件，提升资本效率。
原文摘要 · Abstract (English)
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions. Existing risk management tools utilize static health-factor thresholds, which are reactive and fail to distinguish between administrative "dust" cleanup and genuine insolvency. In this work, we propose an autonomous agent that leverages time-to-event (survival) analysis and moves beyond prediction to execution. Unlike passive risk signals, this agent perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations. We introduce a return period metric derived from a numerically stable XGBoost Cox proportional hazards model to normalize risk across transaction types, coupled with a volatility-adjusted trend score to filter transient market noise. To select optimal interventions, we implement a counterfactual optimization loop that simulates potential user actions to find the minimum capital required to mitigate risk. We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles. The results demonstrate the agent's ability to prevent liquidations in imminent-risk scenarios where static rules fail, effectively "saving the unsavable" while maintaining a zero worsening rate, providing a critical safety guarantee often missing in autonomous financial agents. Furthermore, the system successfully differentiates between actionable financial risks and negligible dust events, optimizing capital efficiency where static rules fail.
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