Human Feedback Types
provisional (inferred)None explicit
No explicit feedback protocol extracted.
"Decentralized finance exposes supervisors to fast-moving, networked credit risks."
HFEPX · Eval paper review
DeXposure-Claw: An Agentic System for DeFi Risk Supervision
Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen +1 more
Published
Jun 17, 2026
Citations
0
Trust level
Provisional
Usefulness score
Unavailable
Extraction confidence
0% (Provisional)
Derived from abstract and metadata only.
Signals refreshed
Jun 17, 2026
Should you rely on this paper?
Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.
This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This page is still relying on abstract and metadata signals, not a fuller protocol read.
Best use
Background context only
Use if you need
A provisional background reference while structured extraction finishes.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This page is still relying on abstract and metadata signals, not a fuller protocol read.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
Unavailable
Provisional (processing)
Eval-fit score is unavailable until extraction completes.
If you are doing eval pipeline work, start here
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.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
provisional (inferred)None explicit
Validate eval design from full paper text.
"Decentralized finance exposes supervisors to fast-moving, networked credit risks."
Quality Controls
provisional (inferred)Not reported
No explicit QC controls found.
"Decentralized finance exposes supervisors to fast-moving, networked credit risks."
Benchmarks / Datasets
provisional (inferred)Not extracted
No benchmark anchors detected.
"Decentralized finance exposes supervisors to fast-moving, networked credit risks."
Reported Metrics
provisional (inferred)Not extracted
No metric anchors detected.
"Decentralized finance exposes supervisors to fast-moving, networked credit risks."
Rater Population
provisional (inferred)Unknown
Rater source not explicitly reported.
"Decentralized finance exposes supervisors to fast-moving, networked credit risks."
Human feedback details
This page is using abstract-level cues only right now. Treat the signals below as provisional.
- Potential human-data signal: No explicit human-data keywords detected.
- Potential benchmark anchors: No benchmark names detected in abstract.
- Abstract highlights: 3 key sentence(s) extracted below.
Evaluation details
Evaluation fields are inferred from the abstract only.
- Potential evaluation modes: No explicit eval keywords detected.
- Potential metric signals: No metric keywords detected.
- Confidence: Provisional (metadata-only fallback).
Research brief
Metadata summary
Decentralized finance exposes supervisors to fast-moving, networked credit risks.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- 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.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.