DeXposure-Claw: An Agentic System for DeFi Risk Supervision

Researchers introduced DeXposure-Claw, an agentic system for DeFi risk supervision that uses a graph time-series foundation model and structured evidence to reduce false alarms. The system, evaluated on five years of real data, aims to improve regulatory oversight of decentralized finance credit risks.

arXiv:2606.19501v1 Announce Type: new Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisions through structured evidence: 1 DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks; 2 deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and 3 data-health and confidence gates constrain escalation before DeXposure-Claw emits auditable supervisory tickets with rationales. We further develop DeXposure-Bench, a six-axis evaluation harness, whose decision axis scores tickets against a regulator-aligned absolute-loss ground truth and an explicit false-intervention rate. Experiments on five years of weekly real data fully support our system. Code is at https://github.com/EVIEHub/DeXposure-Claw.