In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classification work which is a meaningful and challenging task. Nevertheless, research in this field is still limited. In this paper, we design a novel Multimodal Graph Neural Network (MGNN) framework for predicting cancer survival, which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. Specifically, we first construct the bipartite graphs between patients and multimodal data to explore the inherent relation. Subsequently, the embedding of each patient on different bipartite graphs is obtained with graph neural network. Finally, a multimodal fusion neural layer is proposed to fuse the medical features from different modality data. Comprehensive experiments have been conducted on real-world datasets, which demonstrate the superiority of our modal with significant improvements against state-of-the-arts. Furthermore, the proposed MGNN is validated to be more robust on other four cancer datasets.

Retrieval-Augmented Generation (RAG) has become a practical approach for question answering over external corpora, particularly when answers should be grounded in source documents rather than generated only from model parameters. While recent large language models can process increasingly long contexts, they do not remove the need for selecting, organizing, and auditing evidence, especially when systems rely on smaller local models for privacy, cost, or deployment constraints. In this paper, we frame retrieval granularity as an evidence-design variable for answer grounding in small-model RAG question answering. After a brief exploratory NewsQA phase that motivates the error categories, the main study uses the HotpotQA distractor validation split with 7405 hard multi-hop questions and sentence-level supporting-fact annotations. With Qwen3-8B as the fixed generator, we compare closed-book, fixed-budget whole-context, retrieved-context, gold-document, and gold-supporting-fact conditions while varying retrieval granularity, retriever type, and context budget. Retrieved context substantially outperforms closed-book answering and the 1024-token fixed-budget whole-context condition but remains below gold-document and gold-supporting-fact upper bounds, indicating that retrieval, generation, and evaluation limitations should be analyzed separately. Sentence-level retrieval under-recovers multi-hop evidence, especially for questions with three or more supporting facts, while paragraph-level and moderate token-level chunks recover substantially more complete evidence. In the full condition matrix, hybrid retrieval with 256-token chunks and no overlap achieves an F1 of 0.6816 with a supporting-fact recall of 0.9609, compared with an F1 of 0.6166 and supporting-fact recall of 0.7801 for BM25 sentence retrieval. Additional ablations show that fixed-budget whole-context performance is strongly affected by truncation, that overlap has little practical effect under the tested 1024-token budget, and that a stronger BGE dense retriever improves the best retrieved-context F1 to 0.7027. These results align with a diagnostic perspective on chunking: using evidence at a task-appropriate level of granularity can improve grounding, auditability, and answer quality, but the observed patterns should be interpreted within the HotpotQA distractor setting, fixed generator, and tested context budgets.

Loss functions play a significant role in shaping model behavior in machine learning, yet their design implications remain underexplored in natural language processing tasks such as Named Entity Recognition (NER). This study investigates the performance and optimization behavior of five loss functions—L1, L2, Cross-Entropy (CE), KL Divergence (KL), and the proposed DLITE (Discounted Least Information Theory of Entropy) Loss—within transformer-based NER models. DLITE introduces a bounded, entropy-discounting approach to penalization, prioritizing recall and training stability, especially under noisy or imbalanced data conditions. We conducted empirical evaluations across three benchmark NER datasets: Basic NER, CoNLL-2003, and the Broad Twitter Corpus. While CE and KL achieved the highest weighted F1-scores in clean datasets, DLITE Loss demonstrated distinct advantages in macro recall, precision–recall balance, and convergence stability—particularly in noisy environments. Our findings suggest that the choice of loss function should align with application-specific priorities, such as minimizing false negatives or managing uncertainty. DLITE adds a new dimension to model design by enabling more measured predictions, making it a valuable alternative in high-stakes or real-world NLP deployments.