Credit Limits beyond Full Collateralization in Decentralized Micropayments: Incentive Conditions
- URL: http://arxiv.org/abs/2604.25913v1
- Date: Tue, 28 Apr 2026 17:57:50 GMT
- Title: Credit Limits beyond Full Collateralization in Decentralized Micropayments: Incentive Conditions
- Abstract summary: This paper characterizes the incentive conditions under which credit-based non-custodial micropayments can operate beyond full collateralization.<n>As an illustrative application-layer instantiation, an Arbitrum Nitro prototype provides execution-level evidence that the settlement, commitment, and incentive-enforcement paths of a credit-limit-based design can be realized with low on-chain overhead.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In decentralized non-custodial micropayments, the central challenge is not whether payments can be executed directly, but under what conditions such systems can offer credit limits without requiring full collateral backing. Existing approaches typically tie available credit to posted collateral, causing liquidity requirements to scale with transaction volume and settlement exposure and limiting the practical usefulness of credit-based micropayments. This paper characterizes the incentive conditions under which credit-based non-custodial micropayments can operate beyond full collateralization while remaining incentive compatible. We model repeated buyer--merchant interactions under public monitoring and identify the roles of bounded exposure, verifiable settlement outcomes, and continuation value in deterring strategic default under non-custodial execution. The resulting characterization clarifies the trade-off between capital efficiency and the enforcement conditions required to sustain under-collateralized credit expansion without custodial trust. As an illustrative application-layer instantiation, an Arbitrum Nitro prototype provides execution-level evidence that the settlement, commitment, and incentive-enforcement paths of a credit-limit-based design can be realized with low on-chain overhead.
Related papers
- Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
arXiv Detail & Related papers (2026-07-28T17:50:44Z) - Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents [0.0]
We propose a runtime actuarial layer for autonomous AI agents.<n>Every side-effect-bearing action carries a time-consistent, counterfactual risk toll against a contractually fixed safe default.<n>The paper states the primitive contract, the toll identity, the within-boundary no-arbitrage result, and the budget guarantee on which those later layers depend.
arXiv Detail & Related papers (2026-05-26T03:47:58Z) - Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
arXiv Detail & Related papers (2026-03-24T17:39:09Z) - Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models [0.0]
Invoice or payment dilution is a significant source of non credit risk and margin loss in supply chain finance.<n>This paper introduces an AI, machine learning framework and evaluates how that can supplement a deterministic algorithm to predict invoice dilution.
arXiv Detail & Related papers (2026-02-16T23:00:39Z) - Zero-Trust Runtime Verification for Agentic Payment Protocols: Mitigating Replay and Context-Binding Failures in AP2 [0.0]
We present a security analysis of the AP2 mandate lifecycle and identify enforcement gaps that arise during runtime in agent-based payment systems.<n>We propose a zero-trust runtime verification framework that enforces explicit context binding and consume-once mandate semantics.<n>We show that context-aware binding and consume-once enforcement address distinct and complementary attack classes, and that both are required to prevent replay and context-redirect attacks.
arXiv Detail & Related papers (2026-02-06T03:22:11Z) - Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance [0.8283940114367679]
Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral.<n>We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination.
arXiv Detail & Related papers (2025-06-18T09:35:50Z) - A theory of Lending Protocols in DeFi [0.4604003661048266]
Lending protocols are one of the main applications of Decentralized Finance (DeFi)<n>Unlike traditional lending systems, these protocols operate without relying on trusted authorities or off-chain enforcement mechanisms.<n>We propose a formal model of lending protocols that captures the essential features of mainstream platforms.
arXiv Detail & Related papers (2025-06-18T09:25:33Z) - Protocol Learning, Decentralized Frontier Risk and the No-Off Problem [56.74434512241989]
We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants.<n>This approach has the potential to aggregate orders of magnitude more computational resources than any single centralized entity.<n>It also introduces novel challenges: heterogeneous and unreliable nodes, malicious participants, the need for unextractable models to preserve incentives, and complex governance dynamics.
arXiv Detail & Related papers (2024-12-10T19:53:50Z) - Empowering Many, Biasing a Few: Generalist Credit Scoring through Large
Language Models [53.620827459684094]
Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks.
We propose the first open-source comprehensive framework for exploring LLMs for credit scoring.
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
arXiv Detail & Related papers (2023-10-01T03:50:34Z) - Latent State Marginalization as a Low-cost Approach for Improving
Exploration [79.12247903178934]
We propose the adoption of latent variable policies within the MaxEnt framework.
We show that latent variable policies naturally emerges under the use of world models with a latent belief state.
We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training.
arXiv Detail & Related papers (2022-10-03T15:09:12Z) - Quantization for decentralized learning under subspace constraints [61.59416703323886]
We consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints.
We propose and study an adaptive decentralized strategy where the agents employ differential randomized quantizers to compress their estimates.
The analysis shows that, under some general conditions on the quantization noise, the strategy is stable both in terms of mean-square error and average bit rate.
arXiv Detail & Related papers (2022-09-16T09:38:38Z) - Explanations of Machine Learning predictions: a mandatory step for its
application to Operational Processes [61.20223338508952]
Credit Risk Modelling plays a paramount role.
Recent machine and deep learning techniques have been applied to the task.
We suggest to use LIME technique to tackle the explainability problem in this field.
arXiv Detail & Related papers (2020-12-30T10:27:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.