arXiv subjects

Zixin Xu

Publications and source records attributed to Zixin Xu.

6 recordsLinked to original sources

arXiv2026

HealMed: Multilingual Evaluation of Large Language Models in Medicine

We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine. HealMed contains 1,000 examples in each of nine languages, drawn from nine datasets and covering three task formats: MCQA, NLI and open-ended QA. The benchmark was developed over two years by 23 physicians and medical experts based across nine countries and regions. Each translation was evaluated and revised by two experts fluent in English and the corresponding target language. On HealMed, performance declined most in low-resource languages, although the size of the gap varied markedly across languages and models. The strongest proprietary models were the most stable across languages, whereas many open-source and medically specialized models showed larger and less consistent gaps. Medical specialization alone did not ensure multilingual robustness. Furthermore, expert revision could either raise or lower measured performance, indicating that translation quality materially affects cross-language evaluation results.

cs.CL↗

arXiv2026

Why Ammoniated Lithium Borohydrides Liquefy and Resolidify?

Ammonia ($\mathrm{NH_3}$) absorption drives $\mathrm{LiBH_4\!\cdot\!xNH_3}$ through a re-entrant ``solid--liquid--solid'' transition: $\mathrm{LiBH_4\!\cdot\!NH_3}$ is a well-defined solid ammoniate, compositions near $\mathrm{LiBH_4\!\cdot\!2NH_3}$ are liquid-like or partially liquefied, whereas $\mathrm{LiBH_4\!\cdot\!3NH_3}$ returns to a more rigid non-liquid ammoniate state. However, the microscopic origin of this unintuitive response remains a long-lasting mystery. Here, we uncover its mechanism. Cross-database analysis identifies borohydrides as a particularly state-diverse and composition-responsive material family. Structure prediction and ab initio molecular simulations reveal that $\mathrm{NH_3}$ progressively replaces $\mathrm{BH_4^-}$ in the Li coordination shell. The liquid-like state emerges not at the highest $\mathrm{NH_3}$ loading but near $x\approx2$, where Li--N and Li--B coordination modes are strongly mixed, coordination memory is weakest, and the sampled Li--N/N$\cdots$B coordination landscape is broadest. Further ammoniation produces Li--N-dominant coordination and slows $\mathrm{BH_4^-}/\mathrm{NH_3}$ contact renewal, with the resulting increase in network persistence and accompanying recovery of a rigid ammoniate state. Pressure--composition isotherm, $^{1}\mathrm{H}$ and $^{11}\mathrm{B}$ nuclear magnetic resonance, and Raman measurements support this non-monotonic state evolution and associated $\mathrm{BH_4^-}/\mathrm{NH_3}$ reorganization. These findings transform ammonia-induced liquefaction from an empirical phase anomaly into a competition between native-network disruption, mixed-coordination frustration, and ligand-built network reconstruction, providing a framework for chemically switching between transport-favouring fluidity and stability-favouring rigidity in hydrogen-rich materials.

cond-mat.mtrl-sci↗

arXiv2025

Learning neural representations for X-ray ptychography reconstruction with unknown probes

X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology. However, its full potential is constrained by the critical challenge of accurately reconstructing images when the illuminating probe is unknown. Conventional iterative methods and deep learning approaches are often suboptimal, particularly under the low-signal conditions inherent to low-dose and high-speed experiments. These limitations compromise reconstruction fidelity and restrict the broader adoption of the technique. In this work, we introduce the Ptychographic Implicit Neural Representation (PtyINR), a self-supervised framework that simultaneously addresses the object and probe recovery problem. By parameterizing both as continuous neural representations, PtyINR performs end-to-end reconstruction directly from raw diffraction patterns without requiring any pre-characterization of the probe. Extensive evaluations demonstrate that PtyINR achieves superior reconstruction quality on both simulated and experimental data, with remarkable robustness under challenging low-signal conditions. Furthermore, PtyINR offers a generalizable, physics-informed framework for addressing probe-dependent inverse problems, making it applicable to a wide range of computational microscopy problems.

cs.CV↗

arXiv2025

GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks

The exponential growth of spam text on the Internet necessitates robust detection mechanisms to mitigate risks such as information leakage and social instability. This work addresses two principal challenges: adversarial strategies employed by spammers and the scarcity of labeled data. We propose a novel spam-text detection framework GCC-Spam, which integrates three core innovations. First, a character similarity network captures orthographic and phonetic features to counter character-obfuscation attacks and furthermore produces sentence embeddings for downstream classification. Second, contrastive learning enhances discriminability by optimizing the latent-space distance between spam and normal texts. Third, a Generative Adversarial Network (GAN) generates realistic pseudo-spam samples to alleviate data scarcity while improving model robustness and classification accuracy. Extensive experiments on real-world datasets demonstrate that our model outperforms baseline approaches, achieving higher detection rates with significantly fewer labeled examples.

cs.LG↗

arXiv2025

Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs

Large language models (LLMs) perform well in medical QA, but their effectiveness in Japanese contexts is limited due to privacy constraints that prevent the use of commercial models like GPT-4 in clinical settings. As a result, recent efforts focus on instruction-tuning open-source LLMs, though the potential of combining them with retrieval-augmented generation (RAG) remains underexplored. To bridge this gap, we are the first to explore a knowledge graph-based (KG) RAG framework for Japanese medical QA small-scale open-source LLMs. Experimental results show that KG-based RAG has only a limited impact on Japanese medical QA using small-scale open-source LLMs. Further case studies reveal that the effectiveness of the RAG is sensitive to the quality and relevance of the external retrieved content. These findings offer valuable insights into the challenges and potential of applying RAG in Japanese medical QA, while also serving as a reference for other low-resource languages.

cs.CL↗

arXiv2024

Coordinate-based Neural Network for Fourier Phase Retrieval

Fourier phase retrieval is essential for high-definition imaging of nanoscale structures across diverse fields, notably coherent diffraction imaging. This study presents the Single impliCit neurAl Network (SCAN), a tool built upon coordinate neural networks meticulously designed for enhanced phase retrieval performance. Remedying the drawbacks of conventional iterative methods which are easiliy trapped into local minimum solutions and sensitive to noise, SCAN adeptly connects object coordinates to their amplitude and phase within a unified network in an unsupervised manner. While many existing methods primarily use Fourier magnitude in their loss function, our approach incorporates both the predicted magnitude and phase, enhancing retrieval accuracy. Comprehensive tests validate SCAN's superiority over traditional and other deep learning models regarding accuracy and noise robustness. We also demonstrate that SCAN excels in the ptychography setting.

cs.CV↗