arXiv subjects

Zhaorui Jiang

Publications and source records attributed to Zhaorui Jiang.

3 recordsLinked to original sources

arXiv2026

Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning

While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS. To bridge this gap, we introduce Geo-Expert, a family of parameter-efficient geological LLMs fine-tuned on a custom-curated, high-quality instruction dataset processed using our custom instruction synthesis pipeline. We investigate the impact of model scaling and architecture by fine-tuning three base models: Qwen3-8B, Qwen3-32B, and Gemma-3-27B, with Low-Rank Adaptation (LoRA) method. Our extensive evaluation on a novel domain-specific benchmark, Geo-Eval, reveals that a domain-aligned 8B model can outperform open-weight 70B generalists and proprietary GPT-4o on specialized geological reasoning, while a 32B variant approaches frontier reasoning models. The optimized 8B model further offers a competitive cost-performance ratio for deployment. This work provides a reproducible recipe for democratizing scientific LLMs and establishes a baseline for geological artificial intelligence.

cs.AI↗

arXiv2026

STProtein: predicting spatial protein expression from multi-omics data

The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological "Dark Matter".

cs.AI↗

arXiv2026

Crypto-ncRNA: a bio-inspired post-quantum cryptographic primitive exploiting RNA folding complexity

The imminent realization of fault-tolerant quantum computing precipitates a systemic collapse of classical public-key infrastructure and necessitates an urgent transition to post-quantum cryptography. However, current standardization efforts predominantly rely on structured mathematical problems that may remain vulnerable to unforeseen algorithmic breakthroughs, highlighting a critical need for fundamentally orthogonal security paradigms. Here, we introduce \emph{Crypto-ncRNA} as a biophysically inspired cryptographic primitive that exploits the thermodynamic complexity of non-coding RNA folding as a computational work-factor amplifier. By leveraging the rugged energy landscape inherent to RNA secondary structure prediction, a problem intractable to rapid inversion, we establish a security foundation independent of conventional number-theoretic assumptions. We validate this approach by mapping the folding problem to a Quadratic Unconstrained Binary Optimization model and demonstrate theoretical resilience against quantum optimization attacks including the Quantum Approximate Optimization Algorithm. Functioning as a symmetric key encapsulation and derivation primitive dependent on pre-shared seeds, Crypto-ncRNA achieves throughputs competitive with software-based Advanced Encryption Standard implementations. By utilizing the generated high-entropy keys within a standard stream cipher framework, it exhibits ciphertext entropy that satisfies rigorous NIST SP 800-22 statistical standards. These findings not only articulate a novel bio-computational pathway for cryptographic defense but also provide a rigorous algorithmic blueprint for future physical realization, demonstrating that the thermodynamic complexity of biological systems offers a robust and physically grounded frontier for securing digital infrastructure in the post-quantum era.

cs.CR↗