Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Score
78提出将自演化学习与专家在环结合用于抑郁症标注的新框架，填补了结构化证据缺失的空缺。
方法设计完整，包含三阶段和双记忆架构，但初步实验规模较小，长远评估留待未来。
直接服务于心理健康领域的可解释数据集构建，对下游模型透明度和临床应用有实际意义。
论文发表于2026年7月，紧跟LLM赋能医疗标注的热点，具有较强时效性。
来源为arXiv预印本，但研究机构不详，且尚未经同行评审，信度中等偏上。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper innovatively combines self-evolving learning with expert verification, addressing the key bottleneck of lacking structured evidence in depression annotation, offering strong practical value.
AI commentary
「What stands out in this paper is the integration of self-evolving learning into mental health annotation, with a dual-memory architecture enabling continuous improvement without retraining. For researchers working on medical AI, especially explainability and dataset quality control, this is a highly practical toolkit. If you are building depression-related NLP datasets, the audit trail and expert feedback mechanism are worth adopting.」
Quick overview
This paper proposes a self-evolving, expert-in-the-loop annotation framework for building explainable, DSM-5-TR-aligned depression datasets. It operates in three stages: candidate evidence selection, criterion-level analysis, and case-level synthesis. A dual-memory architecture internalizes expert feedback to iteratively improve annotations without retraining. A pilot study shows improved consistency and explainability with reduced manual revision effort.
- Proposes a self-evolving MDD annotation framework combining LLM assistance with expert verification for DSM-5-TR-aligned labels.
- Three-stage pipeline: candidate evidence selection, criterion-level analysis, and case-level synthesis, with export of clinical evidence, reasoning traces, and edit history.
- Dual-memory architecture (Example Memory and Reflection Memory) internalizes expert feedback for iterative improvement without retraining.
- Pilot study shows improved annotation consistency and explainability with reduced manual revision effort.
Full text
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