Financial anomalies arise from heterogeneous mechanisms -- price shocks, liquidity freezes, contagion cascades, and momentum reversals -- yet existing detectors produce uniform scores without revealing which mechanism is failing. This hinders targeted responses: liquidity freezes call for market-making support, whereas price shocks call for circuit breakers. Three key challenges remain: (1) static graphs cannot adapt when correlations shift across regimes; (2) uniform detectors overlook heterogeneous anomaly signatures; and (3) black-box scores provide no actionable guidance on driving mechanisms. We address these challenges with an adaptive graph learning framework that embeds interpretability architecturally rather than post hoc. The framework constructs stress-modulated graphs that adaptively interpolate between known sector and geographic relationships and data-driven correlations as market conditions evolve. Anomalies are decomposed via four mechanism-specific experts -- Price-Shock, Liquidity, Systemic-Contagion, and Momentum-Reversal -- each capturing a distinct anomaly channel documented in the financial economics literature. The resulting routing weights serve as interpretable proxies for mechanism attribution, with their relative values indicating each anomaly's primary driving mechanism. A hierarchical Market Pressure Index aggregates entity-level anomaly scores into graduated market-wide alerts. On 100 U.S. equities (2017-2024), the framework detects all six major stress events with a 3.7-day mean lead time, outperforming baselines by +33 percentage points, with AUC 0.888 and AP 0.626. Case studies on SVB (March 2023) and Japan carry-trade unwind (August 2024) demonstrate that routing weights automatically distinguish localized from systemic crises without labeled supervision.

翻译：金融异常源于多种异构机制——价格冲击、流动性冻结、传染级联与动量反转——然而现有检测器仅生成统一评分而未揭示具体失效机制。这阻碍了针对性应对措施的实施：流动性冻结需要做市支持，而价格冲击则需触发熔断机制。当前存在三大核心挑战：(1)静态图无法在跨市场体制相关性变化时自适应调整；(2)统一检测器忽略了异构异常特征；(3)黑箱评分无法为驱动机制提供可操作的指导。我们通过自适应图学习框架应对这些挑战，该框架将可解释性内置于架构设计而非事后分析。该框架构建压力调制图，在市场条件演变过程中自适应地插值已知行业/地理关系与数据驱动的相关性。异常通过四个机制特异性专家模块进行解构——价格冲击、流动性、系统传染与动量反转——每个模块捕捉金融经济学文献中记载的独特异常传导渠道。由此生成的路由权重作为机制归因的可解释代理指标，其相对值指示每个异常的主要驱动机制。分层式市场压力指数将实体级异常评分聚合为渐进式全市场预警。在100支美国股票(2017-2024)的测试中，该框架以3.7天的平均领先时间检测到全部六次重大压力事件，以AUC 0.888和AP 0.626的性能超越基线方法33个百分点。针对硅谷银行(2023年3月)和日本套息交易平仓(2024年8月)的案例研究表明，路由权重可在无标注监督条件下自动区分局部性危机与系统性危机。