arXiv subjects

Yefei Zhang

Publications and source records attributed to Yefei Zhang.

3 recordsLinked to original sources

arXiv2026

RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines

Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features. While current quantum hardware is not yet practical for direct large-scale vision-language model (VLM) inference, simulated quantum computation can be used during model construction to generate structured parameters for compact classical AI systems. We build RiverONE, a lightweight vision-language model for quantum calibration plot understanding, using simulated quantum computation. It employs a specialized visual encoder and an InternVL-based language backbone. To compensate for compression-induced information loss, we introduce quantum-generated parameters, which are materialized as classical tensors after training. This allows RiverONE to run entirely on classical GPUs at inference time, with no quantum hardware or runtime quantum simulation. With approximately 1.9 billion parameters, RiverONE achieves at least 95\% of the performance of NVIDIA Ising Calibration 1 on quantum calibration plot understanding tasks while using less than 10\% of its parameter count. These results suggest that simulated quantum computation can serve as a practical construction-stage mechanism for building lightweight, knowledge-intensive scientific VLMs. Our code is available at https://github.com/THeWakeSystems/RiverOne.

quant-ph↗

arXiv2026

Pattern Formation in a Spatial Public Goods Dilemma due to Diffusive or Directed Motion

The costly provision of public goods serves as a model problem for the evolution of cooperative behavior, presenting a social dilemma between the collective benefits of shared resources and the individual incentive to free-ride in resource production. The spatial structure of populations can also impact cooperation over public goods, as diffusion of public goods and intentional motion of individuals towards regions with greater resources can interact with population and public goods dynamics to produce heterogeneous patterns in the spatial distribution of strategies and resources. In this paper, we build off a model introduced by Young and Belmonte for the reaction dynamics of interacting individuals and explicit public good, deriving a system of PDEs that describes the spatial profiles of strategies and the public good in the presence of both diffusive motion of individuals and resources and chemotaxis-like directed motion of individuals in response to gradients in the concentration of public goods. Through linear stability analysis, we show that spatial patterns in strategic and public goods profiles can emerge due to either Turing instability with high defector diffusivity or a directed-motion instability through strong sensitivity of cooperators towards increasing resource concentration. We further explore the emergent spatial patterns with a mix of weakly nonlinear stability analysis and numerical simulation, showing that, for a wide range of reaction parameters, diffusion-driven instability appears to increase cooperation and public goods across the spatial domain, while directed motion of cooperators towards public goods tends to decrease cooperation and environmental quality across the environment.

q-bio.PE↗

arXiv2025

ECG Identity Authentication in Open-set with Multi-model Pretraining and Self-constraint Center & Irrelevant Sample Repulsion Learning

Electrocardiogram (ECG) signal exhibits inherent uniqueness, making it a promising biometric modality for identity authentication. As a result, ECG authentication has gained increasing attention in recent years. However, most existing methods focus primarily on improving authentication accuracy within closed-set settings, with limited research addressing the challenges posed by open-set scenarios. In real-world applications, identity authentication systems often encounter a substantial amount of unseen data, leading to potential security vulnerabilities and performance degradation. To address this issue, we propose a robust ECG identity authentication system that maintains high performance even in open-set settings. Firstly, we employ a multi-modal pretraining framework, where ECG signals are paired with textual reports derived from their corresponding fiducial features to enhance the representational capacity of the signal encoder. During fine-tuning, we introduce Self-constraint Center Learning and Irrelevant Sample Repulsion Learning to constrain the feature distribution, ensuring that the encoded representations exhibit clear decision boundaries for classification. Our method achieves 99.83% authentication accuracy and maintains a False Accept Rate as low as 5.39% in the presence of open-set samples. Furthermore, across various open-set ratios, our method demonstrates exceptional stability, maintaining an Open-set Classification Rate above 95%.

cs.CR↗