The ClinSkill QA shared task requires models to recover the temporal order of scrambled clinical keyframes and generate explanations. We propose EvidenceFlow, a structured zero-shot framework based on Qwen2.5-VL that decomposes the task into global overview, local evidence modeling, and ordering decision, with two variants: model-led EvidenceFlow-M and rule-guided EvidenceFlow-R. On the official test set, EvidenceFlow-R achieves better ordering performance, while EvidenceFlow-M produces better explanation quality, revealing a trade-off between ordering stability and rationale generation. EvidenceFlow provides an interpretable zero-shot baseline for clinical keyframe ordering.

“The rapid development of artificial intelligence has led to an explosion of literature in the biomed-ical field, and Biomedical Named Entity Recognition (BioNER) can quickly and accurately iden-tify key information from unstructured text. This task has become an important topic to promotethe rapid development of intelligence in the biomedical field. However, in the Named EntityRecognition (NER) of the biomedical field, there are always some problems of unclear boundaryrecognition, the underutilization of hierarchical information in sentences and the scarcity of train-ing data resources. Based on this, this paper proposes a multi-task BioNER model based on dataaugmentation, using four data augmentation methods: Mention Replacement (MR), Label-wisetoken Replacement (LwTR), Shuffle Within Segments (SiS) and Synonym Replacement (SR)to increase the training data. The syntactic information is extracted by incorporating the inputsentence into the Graph Convolutional Network (GCN), and then the tag information encodedby BERT is interacted through a co-attention mechanism to obtain an interaction matrix. Subse-quently, NER is performed through boundary detection tasks and span classification tasks. Com-parative experiments with other methods are conducted on the BC5CDR and JNLPBA datasets,as well as the CCKS2017 dataset. The experimental results demonstrate the effectiveness of themodel proposed in this paper.”