Stablecoin Design with Adversarial-Robust Multi-Agent Systems via Trust-Weighted Signal Aggregation

Paper: arXiv 2601.22168

Authors: Shengwei You, Aditya Joshi, Andrey Kuehlkamp, Jarek Nabrzyski

Abstract

Algorithmic stablecoins promise decentralized monetary stability by maintaining a target peg through programmatic reserve management. Yet, their reserve controllers remain vulnerable to regime-blind optimization, calibrating risk parameters on fair-weather data while ignoring tail events that precipitate cascading failures. The March 2020 Black Thursday collapse, wherein MakerDAO’s collateral auctions yielded $8.3M in losses and a 15% peg deviation, exposed a critical gap: existing models like SAS systematically omit extreme volatility regimes from covariance estimates, producing allocations optimal in expectation but catastrophic under adversarial stress. We present MVF-Composer, a trust-weighted Mean-Variance Frontier reserve controller incorporating a novel Stress Harness for risk-state estimation. Our key insight is deploying multi-agent simulations as adversarial stress-testers: heterogeneous agents (traders, liquidity providers, attackers) execute protocol actions under crisis scenarios, exposing reserve vulnerabilities before they manifest on-chain. We formalize a trust-scoring mechanism T: A -> [0,1] that down-weights signals from agents exhibiting manipulative behavior, ensuring the risk-state estimator remains robust to signal injection and Sybil attacks. Across 1,200 randomized scenarios with injected Black-Swan shocks (10% collateral drawdown, 50% sentiment collapse, coordinated redemption attacks), MVF-Composer reduces peak peg deviation by 57% and mean recovery time by 3.1x relative to SAS baselines. Ablation studies confirm the trust layer accounts for 23% of stability gains under adversarial conditions, achieving 72% adversarial agent detection. Our system runs on commodity hardware, requires no on-chain oracles beyond standard price feeds, and provides a reproducible framework for stress-testing DeFi reserve policies.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative, adversarial multi-agent simulation framework (MVF-Composer) for stablecoin reserve management, successfully addressing Byzantine signal injection and Sybil attacks without requiring persistent identities. Its rigorous empirical setup—testing across 1,200 randomized scenarios with injected shocks—is backed by well-defined formal security objectives and clear mathematical formulations.

Research Flowchart

  flowchart TD
    A[Goal: Prevent Stablecoin Collapses from Tail-Risk & Adversarial Attacks] --> B[Input: Market Data & 1,200 Black-Swan Shock Scenarios]
    B --> C[Multi-Agent Simulation: Heterogeneous Agents & Adversarial Attackers]
    C --> D[Trust-Scoring Mechanism: Down-weight Manipulative Signals]
    D --> E[MVF-Composer: Trust-Weighted Risk Estimation & Reserve Control]
    E --> F[Outcomes: Peak Peg Deviation Reduced by 57% & Recovery Time Improved 3.1x]

Cited References (24)

1. A stablecoin that’s actually stable: A portfolio optimization approach (2025) — link
2. Stablecoins – Past, Present, and Future (2022) — link
3. Stablecoins 2.0: Economic Foundations and Risk-based Models (2020) — link
4. Research on the Application Methods of Large Language Model Interpretability in FinTech Scenarios (2024) — link
5. AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents (2025)
6. Exploring the Security Issues of Real World Assets (RWA) (2024) — link
7. A deep Q-learning portfolio management framework for the cryptocurrency market (2020) — link
8. The Decentralized Financial Crisis (2020) — link

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.