DeXposure-Claw: An Agentic System for DeFi Risk Supervision
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Computer Science > Artificial Intelligence
Title:DeXposure-Claw: An Agentic System for DeFi Risk Supervision
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.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Risk Management (q-fin.RM) |
| Cite as: | arXiv:2606.19501 [cs.AI] |
| (or arXiv:2606.19501v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19501
arXiv-issued DOI via DataCite (pending registration)
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