DeXposure-Claw: An Agentic System for DeFi Risk Supervision

[Submitted on 17 Jun 2026 (v1), last revised 29 Jun 2026 (this version, v2)]

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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 this https URL.

Submission history

From: Aijie Shu [view email]
[v1] Wed, 17 Jun 2026 18:40:08 UTC (70 KB)
[v2] Mon, 29 Jun 2026 18:36:47 UTC (70 KB)