arXiv subjects

Zheng Che

Publications and source records attributed to Zheng Che.

5 recordsLinked to original sources

arXiv2026

Importance-Reweighted Fock-Space Variational Monte Carlo

Fock-space variational Monte Carlo (FS-VMC) evaluates variational quantities by sampling discrete many-body configurations. In molecular applications, concentrated Born distributions can hinder Markov-chain mixing, while Monte Carlo estimators can also exhibit large variance. We introduce importance-reweighted FS-VMC (IR-FS-VMC), which combines Metropolis--Hastings sampling, an evaluation kernel, and self-normalized importance sampling to estimate the Born-distribution energy, gradient, and stochastic reconfiguration (SR) matrix. Controlled comparisons on equilibrium H2O and Fe2S2 show that the acceptance rate and local-energy variance can respond differently to the sampling and estimation procedure. A single production protocol yields accurate variational energies for H2O dissociation, 36-site hydrogen lattices, and Fe2S2 and Fe4S4 active spaces without system-specific sampling parameters.

physics.chem-ph↗

arXiv2026

Heterogeneous Cross-Chain Transaction Tracing for Solana Bridges via Candidate-Set Selective Decision

Solana is a rapidly growing high-throughput blockchain platform that has attracted substantial liquidity and user activity. However, this expansion has also drawn the attention of illicit actors, who frequently leverage cross-chain bridges to route illicit funds onto Solana to obfuscate transaction lineage. Unlike EVM-compatible platforms, Solana features distinct execution dynamics and lacks standard event logs, creating severe semantic gaps that prevent existing tracing methods from reliably correlating cross-ledger transactions. In this paper, we formalize four types of Solana-bound cross-chain transaction modes and propose a candidate-set selective decision-based tracing method called SolTracer. SolTracer maps disparate execution semantics into a unified event space and employs candidate-set selective decision-making to reliably associate target transactions while abstaining when valid targets are absent. Extensive experiments demonstrate that SolTracer outperforms state-of-the-art (SOTA) methods across three representative scenarios: closed-world association, open-world association, and cross-source-chain generalization. In particular, under the challenging open-world setting with a 50% TA ratio, SolTracer improves the F1 score by 20.16% over the strongest SOTA baseline. Utilizing SolTracer, we conduct an empirical analysis on real-world cross-chain transfers to investigate ecosystem dynamics. Our analysis explores the stark count-value divergence across bridge mechanisms, the prevalence of cross-asset shifts, and the decoupling between on-chain settlement and explorer visibility.

cs.CR↗

arXiv2026

A Deterministic Framework for Neural Network Quantum States in Quantum Chemistry

We present a deterministic optimization framework for Neural Network Quantum States (NQS) designed to bypass the sampling variance and slow mixing issues inherent in stochastic optimization. By projecting a neural backflow ansatz onto dynamically evolving configuration subspaces and applying a post-hoc second-order perturbative correction, our method provides a systematic route for optimizing the selected variational component of the wavefunction and estimating residual correlation through a post-hoc perturbative correction. The implementation utilizes a hybrid CPU-GPU architecture that shows empirical sub-linear wall-time scaling with respect to the subspace size over the tested range, enabling the calculation of strongly correlated systems, such as the chromium dimer, within Hilbert spaces of $10^{23}$ configurations. Benchmarks on molecular bond dissociations demonstrate that this deterministic approach yields stable convergence and accuracies comparable to selected reference methods in the tested systems.

physics.chem-ph↗

arXiv2025

Correlating Account on Ethereum Mixing Service via Domain-Invariant feature learning

The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, which restrict their practical applicability. In this paper, we propose StealthLink, a novel framework that addresses these limitations through cross-task domain-invariant feature learning. Our key innovation lies in transferring knowledge from the well-studied domain of blockchain anomaly detection to the data-scarce task of mixing transaction tracing. Specifically, we design a MixFusion module that constructs and encodes mixing subgraphs to capture local transactional patterns, while introducing a knowledge transfer mechanism that aligns discriminative features across domains through adversarial discrepancy minimization. This dual approach enables robust feature learning under label scarcity and distribution shifts. Extensive experiments on real-world mixing transaction datasets demonstrate that StealthLink achieves state-of-the-art performance, with 96.98\% F1-score in 10-shot learning scenarios. Notably, our framework shows superior generalization capability in imbalanced data conditions than conventional supervised methods. This work establishes the first systematic approach for cross-domain knowledge transfer in blockchain forensics, providing a practical solution for combating privacy-enhanced financial crimes in decentralized ecosystems.

cs.CR↗

arXiv2024

Across-Platform Detection of Malicious Cryptocurrency Transactions via Account Interaction Learning

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, we first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method.

cs.CR↗