发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出Snugi-AI-v2系统，利用学习停止策略和持续置信度门控，在Reddit讨论中实现早期抑郁症检测，优化ERDE50并减少误报，取得F1=0.73和最快评估时间。
AI 中文摘要
我们描述了Snugi-AI-v2提交至eRisk 2026任务2的系统，这是从Reddit讨论中进行情境化早期抑郁症检测的第二版。我们的核心贡献是一个学习的MLP停止策略，该策略被训练为直接优化ERDE50，取代了所有先前eRisk任务2提交中使用的固定和分层阈值策略。结合一个持续置信度门控，该门控仅在N=3轮连续高策略置信度后才提交，该系统减少了由短暂情绪帖子引起的误报，而不牺牲召回率。该流程使用冻结的MentalRoBERTa模型对每个讨论线程进行编码，通过MLP分类器将累积表示映射为抑郁症概率，并将决策时机委托给学习到的策略。我们最好的运行实现了F1=0.73（运行1）和F_latency=0.70（运行0和3），中位警报轮次为500轮中的第8轮，在1小时26分钟内完成全部评估，是所有完整提交团队中最快的。我们报告了跨五次运行的系统消融，涵盖两种编码器变体、四种停止策略和三种门控值，以及GRPO策略训练、BDI-II后过滤、MentalLongformer编码和DeBERTa集成的负面结果。代码：此https URL
英文摘要
We describe the Snugi-AI-v2 submission to eRisk 2026 Task 2, the second edition of contextualized early depression detection from Reddit discussions. Our central contribution is a learned MLP stopping policy trained to directly optimize ERDE50, replacing the fixed and tiered threshold strategies used in all prior eRisk Task 2 submissions. Combined with a sustained confidence gate that commits only after N=3 consecutive rounds of high policy confidence, the system reduces false positives caused by transient emotional posts without sacrificing recall. The pipeline encodes each discussion thread with a frozen MentalRoBERTa model, maps the accumulated representation to a depression probability via an MLP classifier, and delegates the timing decision to the learned policy. Our best run achieves F1 = 0.73 (Run 1) and F_latency = 0.70 (Runs 0 and 3), with a median alert round of 8 out of 500, completing the full evaluation in 1 hour 26 minutes, the fastest among all complete-submission teams. We report a systematic ablation across five runs spanning two encoder variants, four stopping strategies, and three gate values, along with negative results from GRPO policy training, BDI-II post filtering, MentalLongformer encoding, and DeBERTa ensembling. Code: https://github.com/chiuyuwen91/erisk-2026
Comments11 pages, 4 figures, 7 tables. Working notes paper for CLEF 2026 eRisk Lab Task 2 (Early Depression Detection). Published in CLEF 2026 Working Notes, CEUR-WS.org
