@inproceedings{shu-etal-2026-dexposure,
title = "{D}e{X}posure-Claw: An Agent System for {D}e{F}i Risk Supervision",
author = "Shu, Aijie and
Chen, Bowei and
Wu, Wenbin and
Chen, Cathy Yi Hsuan and
He, Fengxiang",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.49/",
pages = "590--600",
ISBN = "979-8-89176-466-8",
abstract = "Decentralized finance exposes supervisors to fast-moving, networked credit risks that are difficult to inspect directly from raw on-chain records. We present DeXposure-Claw, an end-to-end decision-support system that converts forecasts of future exposure networks into bounded, auditable review tickets. DeXposure-FM produces future graph distributions. Deterministic monitors and stress scenarios organize forecast signals into a typed EvidenceBundle, and an LLM turns this record into ranked tickets with evidence-linked rationales. Data-health and confidence controls restrict intervention-level outputs when the supporting evidence is insufficient, while supervisors retain final authority. DeXposure-Bench evaluates the resulting workflow across forecasting, warning, calibration, stress analysis, ticket construction, and degraded-input behavior. The release schema separates forecast evidence, queue position, and control state for supervisory review. Retained legacy tickets do not contain every designed gate field. Code: https://github.com/EVIEHub/DeXposure-Claw/tree/finnlp-2026-code-snapshot-v1."
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<abstract>Decentralized finance exposes supervisors to fast-moving, networked credit risks that are difficult to inspect directly from raw on-chain records. We present DeXposure-Claw, an end-to-end decision-support system that converts forecasts of future exposure networks into bounded, auditable review tickets. DeXposure-FM produces future graph distributions. Deterministic monitors and stress scenarios organize forecast signals into a typed EvidenceBundle, and an LLM turns this record into ranked tickets with evidence-linked rationales. Data-health and confidence controls restrict intervention-level outputs when the supporting evidence is insufficient, while supervisors retain final authority. DeXposure-Bench evaluates the resulting workflow across forecasting, warning, calibration, stress analysis, ticket construction, and degraded-input behavior. The release schema separates forecast evidence, queue position, and control state for supervisory review. Retained legacy tickets do not contain every designed gate field. Code: https://github.com/EVIEHub/DeXposure-Claw/tree/finnlp-2026-code-snapshot-v1.</abstract>
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%0 Conference Proceedings
%T DeXposure-Claw: An Agent System for DeFi Risk Supervision
%A Shu, Aijie
%A Chen, Bowei
%A Wu, Wenbin
%A Chen, Cathy Yi Hsuan
%A He, Fengxiang
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F shu-etal-2026-dexposure
%X Decentralized finance exposes supervisors to fast-moving, networked credit risks that are difficult to inspect directly from raw on-chain records. We present DeXposure-Claw, an end-to-end decision-support system that converts forecasts of future exposure networks into bounded, auditable review tickets. DeXposure-FM produces future graph distributions. Deterministic monitors and stress scenarios organize forecast signals into a typed EvidenceBundle, and an LLM turns this record into ranked tickets with evidence-linked rationales. Data-health and confidence controls restrict intervention-level outputs when the supporting evidence is insufficient, while supervisors retain final authority. DeXposure-Bench evaluates the resulting workflow across forecasting, warning, calibration, stress analysis, ticket construction, and degraded-input behavior. The release schema separates forecast evidence, queue position, and control state for supervisory review. Retained legacy tickets do not contain every designed gate field. Code: https://github.com/EVIEHub/DeXposure-Claw/tree/finnlp-2026-code-snapshot-v1.
%U https://aclanthology.org/2026.finnlp-1.49/
%P 590-600
Markdown (Informal)
[DeXposure-Claw: An Agent System for DeFi Risk Supervision](https://aclanthology.org/2026.finnlp-1.49/) (Shu et al., FinNLP 2026)
ACL
- Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi Hsuan Chen, and Fengxiang He. 2026. DeXposure-Claw: An Agent System for DeFi Risk Supervision. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 590–600, Budapest, Hungary. Association for Computational Linguistics.