arXiv subjects

Ruiqi Luo

Publications and source records attributed to Ruiqi Luo.

3 recordsLinked to original sources

arXiv2026

ElectroChemLLM: A Multimodal LLM Pipeline for High-Fidelity Electrochemical Informatics

Electrochemistry is central to solving many modern-day challenges, including energy storage systems for a sustainable future, and its advancement relies heavily on the data from past findings. Existing automated pipelines are limited by text-only architectures, context fragmentation, and numerical hallucinations - collectively preventing accurate, large-scale curation of property databases. We introduce ElectrochemLLM, a multimodal information extraction pipeline that jointly identifies structured electrochemical records encompassing electrolyte compositions, electrode materials, cell configurations, and their measured physicochemical properties from unstructured scientific PDFs at scale. By combining hybrid document parsing with vision-capable LLM orchestration, the pipeline extracts verifiable, structured data across text, tables, and figures. Evaluated on the battery-electrolyte subset of our manually annotated benchmark of 232 papers, ElectrochemLLM achieves an F-1 score of 0.97, and has been applied to curate ElectroChemDB, a structured database spanning 15,000 electrochemical research articles. We demonstrate the utility of this resource by training machine learning models for electrolyte property prediction, illustrating a complete pathway from unstructured literature to predictive materials discovery.

cs.CL↗

arXiv2025

HiFIRec Towards High-Frequency yet Low-Intention Behaviors for Multi-Behavior Recommendation

Multi behavior recommendation leverages multiple types of user-item interactions to address data sparsity and cold-start issues,providing personalized services in domains such as healthcare and ecommerce.Most existing methods utilize graph neural networks to model user intention in a unified manner,which inadequately considers the heterogeneity across different behaviors.Especially,high frequency yet low intention behaviors may implicitly contain noisy signals,and frequent patterns that are plausible while misleading,thereby hindering the learning of user intentions.To this end,this paper proposes a novel multi-behavior recommendation method,HiFIRec,that corrects the effect of high-frequency yet low-intention behaviors by differential behavior modeling.To revise the noisy signals,we hierarchically suppress it across layers by extracting neighborhood information through layer-wise neighborhood aggregation and further capturing user intentions through adaptive cross layer feature fusion.To correct plausible frequent patterns,we propose an intensity-aware non-sampling strategy that dynamically adjusts the weights of negative samples.Extensive experiments on two benchmarks show that HiFIRec relatively improves HR@10 by 4.21%-6.81% over several state-of-the-art methods.

cs.IR↗

arXiv2025

MPLinker: Multi-template Prompt-tuning with Adversarial Training for Issue-commit Link Recovery

In recent years, the pre-training, prompting and prediction paradigm, known as prompt-tuning, has achieved significant success in Natural Language Processing (NLP). Issue-commit Link Recovery (ILR) in Software Traceability (ST) plays an important role in improving the reliability, quality, and security of software systems. The current ILR methods convert the ILR into a classification task using pre-trained language models (PLMs) and dedicated neural networks. these methods do not fully utilize the semantic information embedded in PLMs, resulting in not achieving acceptable performance. To address this limitation, we introduce a novel paradigm: Multi-template Prompt-tuning with adversarial training for issue-commit Link recovery (MPLinker). MPLinker redefines the ILR task as a cloze task via template-based prompt-tuning and incorporates adversarial training to enhance model generalization and reduce overfitting. We evaluated MPLinker on six open-source projects using a comprehensive set of performance metrics. The experiment results demonstrate that MPLinker achieves an average F1-score of 96.10%, Precision of 96.49%, Recall of 95.92%, MCC of 94.04%, AUC of 96.05%, and ACC of 98.15%, significantly outperforming existing state-of-the-art methods. Overall, MPLinker improves the performance and generalization of ILR models, and introduces innovative concepts and methods for ILR. The replication package for MPLinker is available at https://github.com/WTU-intelligent-software-development/MPLinker

cs.SE↗