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Researcher profile

Zhong Chen

Zhong Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceCryptography and SecurityMachine LearningSoftware Engineeringastro-ph.HEComputation and Languageeess.IVInformation Theorymath.ITmath.SPNumerical Analysisphysics.med-ph

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Published work

7 published item(s)

preprint2026arXiv

Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions

The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-chain transactions. We present \textsc{Intent2Tx}, a high-fidelity benchmark featuring 29,921 single-step and 1,575 multi-step instances meticulously derived from 300 days of real-world Ethereum mainnet traces. Unlike prior works that rely on synthetic instructions, \textsc{Intent2Tx} grounds natural language intents in real-world protocol interactions across 11 categories, including diverse long-tail Decentralized Finance (DeFi) primitives. To enable rigorous evaluation, we propose an execution-aware framework that transcends surface-level text matching by employing differential state analysis on forked mainnet environments. Our extensive evaluation of 16 state-of-the-art LLMs reveals that while scaling and retrieval-augmentation enhance logical consistency and parameter precision, current models struggle with out-of-distribution generalization and multi-step planning. Crucially, our execution-based analysis demonstrates that syntactically valid outputs often fail to achieve intended state transitions, highlighting a significant gap in current "reasoning-to-execution" capabilities. \textsc{Intent2Tx} serves as a critical foundation for developing autonomous, reliable agents in intent-centric Web3 ecosystems. Code and data: https://anonymous.4open.science/r/Intent2Tx_Bench-97FF .

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection

Representation Engineering typically relies on static refusal vectors derived from terminal representations. We move beyond this paradigm, demonstrating that refusal is a dynamic and sparse process rather than a localized outcome. Using Causal Tracing, we uncover the Refusal Trajectory-a persistent upstream signature that remains intact even when adversarial attacks (e.g., GCG) suppress terminal signals. Leveraging this, we propose SALO (Sparse Activation Localization Operator), an inference-time detector designed to capture these latent patterns. SALO effectively recovers defense capabilities against forced-decoding attacks, improving detection rates from ~0% to >90% where methods relying on terminal states perform poorly.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust T2 mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for T2 mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in T2 maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Xscope: Hunting for Cross-Chain Bridge Attacks

Cross-Chain bridges have become the most popular solution to support asset interoperability between heterogeneous blockchains. However, while providing efficient and flexible cross-chain asset transfer, the complex workflow involving both on-chain smart contracts and off-chain programs causes emerging security issues. In the past year, there have been more than ten severe attacks against cross-chain bridges, causing billions of loss. With few studies focusing on the security of cross-chain bridges, the community still lacks the knowledge and tools to mitigate this significant threat. To bridge the gap, we conduct the first study on the security of cross-chain bridges. We document three new classes of security bugs and propose a set of security properties and patterns to characterize them. Based on those patterns, we design Xscope, an automatic tool to find security violations in cross-chain bridges and detect real-world attacks. We evaluate Xscope on four popular cross-chain bridges. It successfully detects all known attacks and finds suspicious attacks unreported before. A video of Xscope is available at https://youtu.be/vMRO_qOqtXY.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The intrinsic structure of Sagittarius A* at 1.3 cm and 7 mm

Sagittarius A* (Sgr A*), the Galactic Center supermassive black hole (SMBH), is one of the best targets to resolve the innermost region of SMBH with very long baseline interferometry (VLBI). In this study, we have carried out observations toward Sgr A* at 1.349 cm (22.223 GHz) and 6.950 mm (43.135 GHz) with the East Asian VLBI Network, as a part of the multi-wavelength campaign of the Event Horizon Telescope (EHT) in 2017 April. To mitigate scattering effects, the physically motivated scattering kernel model from Psaltis et al. (2018) and the scattering parameters from Johnson et al. (2018) have been applied. As a result, a single, symmetric Gaussian model well describes the intrinsic structure of Sgr A* at both wavelengths. From closure amplitudes, the major-axis sizes are ~704$\pm$102 $μ$as (axial ratio $\sim$1.19$^{+0.24}_{-0.19}$) and $\sim$300$\pm$25 $μ$as (axial ratio $\sim$1.28$\pm$0.2) at 1.349 cm and 6.95 mm respectively. Together with a quasi-simultaneous observation at 3.5 mm (86 GHz) by Issaoun et al. (2019), we show that the intrinsic size scales with observing wavelength as a power-law, with an index $\sim$1.2$\pm$0.2. Our results also provide estimates of the size and compact flux density at 1.3 mm, which can be incorporated into the analysis of the EHT observations. In terms of the origin of radio emission, we have compared the intrinsic structures with the accretion flow scenario, especially the radiatively inefficient accretion flow based on the Keplerian shell model. With this, we show that a nonthermal electron population is necessary to reproduce the source sizes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Kaya: A Testing Framework for Blockchain-based Decentralized Applications

In recent years, many decentralized applications based on blockchain (DApp) have been developed. However, due to inadequate testing, DApps are easily exposed to serious vulnerabilities. We find three main challenges for DApp testing, i.e., the inherent complexity of DApp, inconvenient pre-state setting, and not-so-readable logs. In this paper, we propose a testing framework named Kaya to bridge these gaps. Kaya has three main functions. Firstly, Kaya proposes DApp behavior description language (DBDL) to make writing test cases easier. Test cases written in DBDL can also be automatically executed by Kaya. Secondly, Kaya supports a flexible and convenient way for test engineers to set the blockchain pre-states easily. Thirdly, Kaya transforms incomprehensible addresses into readable variables for easy comprehension. With these functions, Kaya can help test engineers test DApps more easily. Besides, to fit the various application environments, we provide two ways for test engineers to use Kaya, i.e., UI and command-line. Our experimental case demonstrates the potential of Kaya in helping test engineers to test DApps more easily.

0 citations0 reviewsNo code linkedOpen access
preprint2017arXiv

Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals

Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. But existing approaches can not efficiently recover $N$-dimensional exponential signals with $N\geq 3$. In this paper, we study the problem of recovering N-dimensional (particularly $N\geq 3$) exponential signals from partial observations, and formulate this problem as a low-rank tensor completion problem with exponential factor vectors. The full signal is reconstructed by simultaneously exploiting the CANDECOMP/PARAFAC structure and the exponential structure of the associated factor vectors. The latter is promoted by minimizing an objective function involving the nuclear norm of Hankel matrices. Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.

0 citations0 reviewsNo code linkedOpen access