Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in real-world deployments make inductive KGC difficult, especially under few-shot and zero-shot settings. Multimodal information and Large Language Model (LLM)-derived priors can enrich sparse relational contexts, but they may also introduce noisy or hallucinated evidence. To address these issues, we propose DuPLeR, a \textbf{Du}al-\textbf{P}ath \textbf{L}LM \textbf{R}easoning framework for multimodal few-shot KGC. DuPLeR builds a calibrated relation graph by combining multimodal LLM-derived type priors with factual support structures, and performs dual-level structural reasoning over the refined relation topology. Moreover, a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation. Experiments on eight inductive variants of two multimodal KG (MMKG) benchmarks show that DuPLeR achieves robust performance in data-scarce KGC scenarios.
Jinlan Liu, Zhiying Tu, Yongchao Xing et al.· 0 citations
The proposed knowledge-enhanced visual diagnostic system enhances the transparency of traditional Chinese medicine diagnostic reasoning and the interpretability of treatment plans through knowledge graph-driven visualization and multimodal interaction, offering a practical solution for trustworthy artificial intelligence-assisted traditional Chinese medicine applications.
Yunhan Wang, Yu-Die Wang, Zhiying Tu et al.· arXiv.org· 0 citations
This work introduces Model Automated Deployment Engine (MADE), a dual-agent coordination system that iteratively constructs and validates the deployment artifacts, updates its deployment belief based on execution feedback, and revisits invalid upstream artifacts until the model is successfully served as a ready-to-call API that can then be used by other agents.
Yicheng Liu, Bolin Zhang, Weiran Liu et al.· 0 citations
This work proposes VPO, a negative gradient constraint method for human non-preference samples based on V -usable information, which can alleviate the squeezing effect of DPO, enhance alignment with the generation objective, and maintain the model’s ability to distinguish between preference and non-preference samples.
Zecheng Wang, Chunshan Li, Yupeng Zhang et al.· Neural Information Processin...· 1 citation
