# Summary: 2026-07-16_16-59-54Z_Self_EvolvingHuman_CenteredFrameworkforExplainable.md
Saved: 2026-07-16 21:01
Source: 2026-07-16_16-59-54Z_Self_EvolvingHuman_CenteredFrameworkforExplainable.md
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## Summary  
The paper proposes a self‑evolving, human‑centered framework that improves the quality of depression symptom annotations by aligning them with DSM‑5‑TR criteria and providing transparent reasoning traces. By combining large language model (LLM) assistance with expert verification, the system generates explainable labels rather than performing clinical diagnosis. The framework’s dual‑memory architecture enables iterative learning without retraining, producing a fully auditable annotation pipeline.

## Key Contributions  
- A self‑evolving annotation framework that iteratively improves labeling using Example Memory and Reflection Memory.  
- Integration of LLM‑assisted evidence selection with human expert review to produce DSM‑5‑TR‑aligned labels and explainable reasoning traces.  
- Export of clinical evidence, reasoning traces, and edit histories for comprehensive auditability.

## Methodology  
The authors designed a three‑stage pipeline: first, candidate evidence is selected from textual records; second, the framework performs criterion‑level DSM‑5‑TR analysis to identify relevant symptoms; third, case‑level synthesis creates label‑level diagnostic and severity annotations. A dual‑memory architecture stores successful examples (Example Memory) and processes expert feedback to refine future annotations (Reflection Memory), allowing continuous improvement without model retraining.

## Results  
In a pilot study using expert‑reviewed samples, the framework increased annotation consistency and explainability while reducing manual revision effort. The dual‑memory mechanism facilitated iterative refinement across feedback cycles, though detailed evaluation of performance over multiple cycles is reserved for future work.

## Significance  
High‑quality, explainable mental‑health datasets are essential for reliable AI research; this work addresses the bottleneck of unstructured labels and supports transparent, trustworthy artificial intelligence development in depression studies.

## Related Concepts  
Self‑evolving systems, human‑in‑the‑loop annotation, dual‑memory architecture, LLM‑assisted labeling, DSM‑5‑TR alignment, explainable AI (XAI), clinical evidence traceability.