Autonomous artificial intelligence (AI) agents are increasingly deployed on blockchain platforms,  yet the design space governing their interaction remains poorly understood. This convergence, in which autonomous agents operate on and within decentralized systems, characterizes the emerging Web 4.0  paradigm. We organize this Systematization of Knowledge (SoK) around a bidirectional trust framework.  For the B → A direction (Blockchain → Agent), blockchain provides the trust infrastructure needed by agents. This direction follows their on-chain lifecycle: identity and account abstraction establish an agent on-chain; permission and delegation define what it may do; intent-centric execution carries out its goals; and tokenized agent economies support economic participation. The A → B direction (Agent → Blockchain) concerns participation in core blockchain mechanisms, beginning with security auditing and extending to consensus and governance. Verifiable computation forms the Trust Foundation (TF) shared by both directions. We consider zero-knowledge machine learning (zkML) and optimistic machine learning (opML) alongside trusted execution environments (TEEs). Their trade-offs between trust minimality and computational overhead differ, as does deployment readiness. The Agent–Blockchain Interaction Model (ABIM) formalizes this interaction. We catalog 70 Ethereum Improvement Proposals (EIPs) and Ethereum Request for Comments (ERC) standards in the Appendix and review 127 academic papers. The analysis also covers 20 representative industry projects. This material is compared using five dimensions: Verifiability, Minimality of Trust, Expressiveness, Composability, and Maturity. The comparison reveals three unresolved issues. The agent-specific standards ecosystem is predominantly immature, with only 3 of 13 direct AI/agent  ERCs having reached Final status. Intent architectures lack formal analysis. Research on AI participation in consensus and governance remains limited to isolated studies, and a unified security framing that treats  AI as a first-class actor at the protocol layer is still absent. We propose a three-dimensional taxonomy of agent autonomy, trust model, and operational direction. We identify nine concrete open problems and outline the principal research opportunities.

Over $3.1 trillion in illicit money moves through the global financial system yearly, and much of it now uses stablecoins, which launderers prefer for their liquidity. Decentralized protocols increasingly hide transaction patterns with zero-knowledge proofs, while centralized stablecoins remain visible. To preserve the ability to convert to fiat, they must maintain an auditable record, which makes them natural points of compliance oversight. Using this visibility, we create an Ethereum dataset of Tether USD (USDT) and USD Coin (USDC) wallet transfers and establish a baseline for behavioral anti-money-laundering (AML) detection. We compare linear models, tree ensembles, deep networks,  and graph neural networks. Tree ensembles achieve the best Macro-F1 score, while the graph neural networks lose accuracy as the transaction network fragments. The models separate distinct typologies rather than only flagging suspicion: the fast, dispersed movement of cybercrime wallets is distinguished from the constrained, static footprint of sanctioned or frozen wallets. These results align with the industry shift toward deterministic verification and address the auditability and compliance requirements now forming under regulations such as the EU’s Markets in Crypto-Assets (MiCA) and the U.S. Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act), while limiting unjustified  asset freezes. A high-precision behavioral classification of suspicious wallets raises the economic cost of financial misconduct and informs compliance practice under emerging stablecoin rules.

Blockchain transaction data exhibit heterogeneous structures, complex interactions, and highly imbalanced distributions, which pose significant challenges for unsupervised anomaly detection. Existing methods often fail to effectively model the intrinsic feature distributions of normal transactions and typically lack interpretability, limiting their reliability and usability in practical blockchain scenarios. To address these issues, this paper proposes an unsupervised anomaly detection framework based on feature distribution learning. The core idea is to explicitly model the distribution of normal transaction behavior in a compact latent space while preserving interpretability. Specifically, a Kolmogorov–Arnold Network (KAN) is employed to learn a two-dimensional latent representation of blockchain transactions, capturing nonlinear relationships between features. During training, the model minimizes the radius of  a hypersphere enclosing normal samples, encouraging a compact and structured distribution. During inference, anomalies are identified based on their geometric deviation from the learned distribution, measured by the distance to the latent-space center. This design avoids reliance on reconstruction errors  and enables a more direct and stable decision mechanism. Experiments on Ethereum, Blockchain Network Attack Traffic dataset (BNaT) and Real World Dataset of Cryptocurrency Addresses with Transaction Profiles (Real-CATS) demonstrate that the proposed method consistently outperforms state-of-the-art unsupervised baselines across multiple metrics. Furthermore, the symbolic expressions derived from the learned mapping reveal key transaction features that drive anomaly detection decisions, providing clear interpretability. These results highlight the effectiveness, robustness, and practical value of feature distribution learning for blockchain anomaly detection.