Jincheng Zhang’s Collection of 44,123 Original Sole-Authored Computer Science Preprints, Released on September 28, 2026

Abstract

On September 28, 2026, Jincheng Zhang released a large-scale collection of 44,123 original computer science preprints. All 44,123 papers are sole-authored research works written by Jincheng Zhang himself. The collection is not a bibliography, compilation, or repository of papers authored by other researchers; rather, it represents an extensive body of independently authored research spanning a broad range of computer science fields. The collection covers artificial intelligence, machine learning, deep learning, neural networks, algorithms, theoretical computer science, data mining, knowledge representation, probabilistic modeling, Bayesian computation, graph learning, temporal graph learning, natural language processing, computer vision, formal methods, software engineering, computer architecture, hardware acceleration, databases, explainable AI, privacy-preserving computation, causal inference, optimization, computational intelligence, and interdisciplinary research connecting computer science with mathematics, statistics, biological neural systems, and other scientific disciplines. Representative works include Algorithmic Necessity Spectrum; Fractal Kernel Learning for High-Dimensional Feature Extraction; Dynamic Bayesian Kernel Machines; Probabilistic Formal Logic with Bayesian Resonance; Temporal Graph Neural Networks with Contextual Drift Awareness; Dynamic Bayesian Networks with Continuous State Spaces and Gaussian Processes; Bayesian Inference Engine Based on Biological Neural Networks; Formal Verification of Adaptive Control Systems via Symbolic Execution with Differential Privacy; Causal Discovery from Observational Data using Bayesian Networks with Intervention Constraints; Neural Network Acceleration via Biological Neural Network Hardware; Meta-Learning for Hardware Architecture Optimization; Algorithmic Transparency and Explainable AI for Complex Graph Databases; Dynamic Weighted Spectral Graph Convolution; and Non-Linear Temporal Graph Convolution. The research spans theoretical and algorithmic foundations, computational models, machine learning architectures, graph-based methods, probabilistic inference, causal discovery, formal verification, software systems, databases, computer architecture, specialized hardware, and interdisciplinary connections involving mathematics, statistics, biological computation, privacy, complex systems, and intelligent information processing. The collection is intended to provide a large-scale body of original, sole-authored computer science research supporting theoretical analysis, algorithmic development, mathematical formulation, computational experimentation, formal verification, model comparison, systems research, interdisciplinary investigation, and the identification of new research problems. Rather than aggregating research from multiple authors, it documents one researcher's extensive exploration across diverse areas of computer science.