arXiv:2610.33586v1 Announce Type: new Abstract: Depression severity estimation is crucial for mental health assessment and intervention, yet traditional approaches depend on clinical interviews or self-report questionnaires that require users' active participation. With the widespread use of online social media, users leave rich social traces that may reflect their mental states, motivating the task of social media-based depression severity estimation. Existing efforts have progressed from textual-feature-based classification to LLM-based questionnaire completion over standardized scale items, yet they still fall short in user-specific mental priors modeling, diversified semantic retrieval, and fine-grained reasoning over mental evidence. To address these limitations, we propose UMer, a user-specific mental evidence reasoning framework for social traces-based depression severity estimation. UMer first distills a user-specific mental prior from historical social traces to capture the user's psychological tendencies, behavioral patterns, and self-expression style. It then performs scale-guided multi-query retrieval to construct evidence memories from item-relevant posts. Finally, it conducts coarse-to-fine evidence-aware reasoning to distinguish direct and indirect evidence and predict item-level scores with interpretable rationales. Extensive experiments on real-world benchmarks demonstrate that UMer not only achieves competitive performance over existing baselines, but also provides evidence-grounded interpretability and generalizes to other scale-based tasks.
Knowledge-Guided LLM for Intent-Driven Service Orchestration in Integrated Terrestrial and Non-Terrestrial 6G Networks