In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our method is an optimization framework that modifies an instance so that the classifier predicts a specified target label, while ensuring that the modification remains easily explainable. The objective function contains two components: an explainability-aware $L_0$ (XA-$L_0$) penalty that promotes sparse and interpretable modifications, and a classifier loss objective that steers the perturbed instance toward the desired output. This integrated optimization formulation is used both to identify the underlying causes of misclassification and to evaluate robustness by determining how an instance can change within a tolerance region before being reassigned to another class. To quantify robustness, we introduce the Tolerance Region Confusion Matrix (TOR-Confusion Matrix), which measures a classifier's susceptibility by modeling the class-to-class transition probabilities induced by tolerance-bounded perturbations. We validate the proposed method on both image and tabular datasets, demonstrating its ability to jointly deliver interpretability and robustness assessment.
Evgenii Kuriabov, David Miller, Jia Li· 0 citations
The rapid emergence of autonomous large-language-model (LLM) agents that transact on behalf of users, and
increasingly on behalf of other agents, has given rise to a new economic substrate commonly termed agentic commerce.
Protocols such as x402, the Agent Payments Protocol (AP2), the Agent-to-Agent (A2A) specification and the Model Context
Protocol (MCP) now allow software agents to discover services, negotiate scope, sign cryptographic mandates and settle
stablecoin or fiat-linked micropayments in a single HTTP round trip, without a human present at the point of transaction. This
convenience removes the implicit checkpoint that a human approver historically provided and creates an urgent need for
continuous, machine-speed observability. This paper proposes the Agentic Payments Observatory (APO), a dashboard-centred
reference architecture that unifies agent identity resolution, scoped permission verification, smart-contract and receipt
reconciliation, real-time anomaly detection and policy-breach adjudication into a single operational surface. At the core of APO
is a hybrid gated token-mixing transformer and graph neural encoder fused through conformal risk calibration, termed the
Agentic Payment Anomaly and Breach Detection (APABD) algorithm. We describe the system architecture, the streaming data
pipeline, the detection algorithm and a synthetic multi-agent transaction benchmark of 1.2 million events across 18,400 agent
identities. Experimental results show that APABD attains 0.93 precision, 0.91 recall and 0.92 F1-score, outperforming rulebased, gradient-boosted and single-modality graph or transformer baselines while producing calibrated uncertainty bounds
suitable for compliance escalation. The paper further discusses identity and zero-trust considerations, privacy-preserving
federated deployment across custodians, and the security posture required when agents hold spend-capable credentials.
Jay Roy, David Miller· International Journal for Re...· 0 citations