DeXposure-Claw: An Agentic System for DeFi Risk Supervision

Researchers have introduced DeXposure-Claw, a forecast-grounded agentic supervision system designed to address fast-moving credit risks in DeFi, overcoming the limitations of general-purpose LLMs.

Computer Science > Artificial Intelligence

Title: DeXposure-Claw: An Agentic System for DeFi Risk Supervision

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:

DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks;
Deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and
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.

Source: arXiv cs.AI Recent

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