A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian

# A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian
Source: [https://arxiv.org/abs/2608.26162](https://arxiv.org/abs/2608.26162)
[View PDF](https://arxiv.org/pdf/2608.26162)

> Abstract:Safety\-critical mental\-health support systems must distinguish when supportive conversation is appropriate from when free\-form generation should be blocked\. This paper presents Anian, a safety\-gated multimodal AI backend for perinatal mental\-health support and mindfulness\-intervention routing\. Anian is not intended to diagnose psychiatric conditions or replace clinical care or crisis intervention\. Its modular pipeline places generative AI downstream of structured state representation, conservative risk fusion, and response gating\. User text or voice\-derived ASR transcripts are mapped into four linked layers: L1 emotion states, L2 psychosocial constructs, L3 safety risk, and L4 intervention routes\. Local text\- and rule\-based safety evidence is fused with external voice\-derived evidence using a highest\-risk\-priority rule, S\_fusion = max\(S\_local, S\_external\)\. At moderate or high fused risk, ordinary AI\-generated responses and text\-to\-speech delivery are blocked and replaced by fixed safety content and prompts for human support\. An internal prototype evaluation used approximately 858,295 normalized records from public emotion, dialogue, mental\-health\-related, and Chinese dialogue corpora within a weak\-label and rule\-derived framework\. Micro\-F1 scores were 0\.9604 for L1 emotion classification, 0\.9144 for L2 psychosocial constructs, and 0\.9742 for L4 routing\. In a controlled safety stress test of 233 samples, the L3 rule engine achieved high\-risk recall of 1\.0000 within predefined scenarios\. These findings support the internal feasibility of the label framework and gating logic but do not establish clinical validity, diagnostic accuracy, real\-world safety, or effectiveness\. We report the architecture, ontology, safety\-fusion mechanism, prototype evaluation, error\-analysis plan, and roadmap for expert\-reviewed and real\-world validation\.

## Submission history

From: Xiao Wang \[[view email](https://arxiv.org/show-email/20c04507/2608.26162)\] **\[v1\]**Fri, 10 Jul 2026 13:52:50 UTC \(11,437 KB\)