PsyBridge: Hybrid AI framework for multi-dimensional mental health assessment
Researchers propose PsyBridge, a hybrid decision-support framework that integrates PHQ-9, GAD-7, cognitive evaluation, and personality profiling into a unified architecture for mental health risk classification. The system uses a weighted aggregation mechanism to produce interpretable outputs and was evaluated on a semi-synthetic dataset of 500 patient profiles. PsyBridge achieves 0.84 accuracy, outperforming standalone screening tools, with ablation studies confirming the value of multi-dimensional integration. The work targets digital healthcare and telehealth deployment contexts.
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PsyScore: Psychometrically-aware framework integrating IRT scoring with ZPD-scaffolded LLM feedback for essay assessment
PsyScore is a new framework for Automated Essay Scoring (AES) that unifies diagnostic assessment and instructional feedback through a shared latent ability representation. It combines a neural Item Response Theory scorer (based on the Graded Partial Credit Model) with a multi-agent LLM feedback generator conditioned on estimated student proficiency, operationalizing Vygotsky's Zone of Proximal Development. Experiments on the ASAP++ dataset show competitive scoring performance alongside more pedagogically aligned feedback. The work addresses a gap between psychometric rigor and LLM-based adaptive instruction.
Fine-tuning LLMs to passively estimate depression severity from AI mental health conversations
Researchers fine-tune a Qwen3.5-27B model with a regression head to predict PHQ-9 depression severity scores directly from AI mental health app conversation transcripts, eliminating the need for explicit self-report completion. The training set of 6,283 users combines 3,111 ground-truth labels with pseudolabels generated by Claude Opus and iterative intermediate models. On a held-out test of 842 users, the best model achieves MAE=2.6, Pearson r=0.80, and AUC=0.91 at the clinical PHQ-9≥10 threshold, with AUC>0.87 across all severity thresholds. The work demonstrates a passive, continuous symptom-monitoring approach that could reduce response bias in mental health platforms.
RubricsTree: Scalable hierarchical rubric framework for evaluating personal health AI agents
RubricsTree is a new evaluation framework for LLM-powered personal health agents, built around a hierarchical taxonomy of over 100 clinically-verifiable Boolean rubrics derived from 4,000 real user queries and curated with physician oversight. A context-aware router activates only relevant rubrics per query, enabling scalable yet expert-aligned evaluation. The framework outperforms strong LLM-as-a-judge baselines on expert alignment and, when used as training signal, yields up to ~66% relative gains on HealthBench across Gemini, GPT, and Qwen model families. The work addresses a concrete bottleneck in clinical deployment of health AI: the cost-quality tradeoff in evaluation.
PsychoSafe: Framework for Psychologically-Informed LLM Refusals in High-Risk Interactions
Researchers introduce PsychoSafe, a refusal framework that reframes LLM non-compliance as structured supportive communication grounded in evidence-based psychological intervention strategies. The work constructs an 8,019 prompt-response corpus across five risk domains and applies prompting and parameter-efficient fine-tuning to Qwen 3.5 27B, achieving 28.1% improvement in refusal quality over a generic baseline with notable gains in resource referral and psychological grounding. Evaluations on SORRY-Bench and XSTest reveal strong in-domain robustness but limited out-of-domain generalization, pointing to a need for more diverse fine-tuning data. The framework is relevant to safety alignment work targeting crisis, coercion, and escalating-intent scenarios.
DeepMind Publishes Framework for Evaluating Cybersecurity Threats of Advanced AI
DeepMind has released a framework designed to help cybersecurity experts assess and prioritize defenses against potential threats posed by advanced AI systems. The framework aims to systematically identify which defensive measures are necessary given AI's expanding capabilities in offensive cyber operations. This represents DeepMind's structured approach to evaluating AI-enabled cyber risks before they materialize at scale.
Risk-Aware Hybrid Selective Classification for HIV Suspicion Identification in Spanish Clinical Notes
This paper proposes a hybrid selective classification framework for clinical NLP that explicitly handles both aleatoric and epistemic uncertainty to avoid overconfident predictions in medical triage settings. The system combines Mondrian conformal prediction with a Multi-Centroid Mahalanobis Distance veto, evaluated on HIV suspicion identification in Spanish clinical notes. The authors demonstrate that standard uncertainty metrics and baseline classifiers suffer coverage collapse under strict reliability constraints, while their dual-verification approach isolates a trustworthy operational domain. The work critiques inflated benchmark metrics that arise from forcing deterministic classification on inherently ambiguous clinical instances.
Anthropic publishes structured harm assessment framework covering physical, psychological, economic, and societal impacts
Anthropic has released a policy document describing their evolving framework for assessing and mitigating AI harms across five dimensions: physical, psychological, economic, societal, and individual autonomy impacts. The framework complements their existing Responsible Scaling Policy and informs decisions on usage policies, red-teaming, detection, and enforcement. Concrete examples include safeguards for computer use capabilities (fraud, phishing) and a reported 45% reduction in unnecessary refusals in Claude 3.7 Sonnet through improved handling of ambiguous prompts. Anthropic frames this as a work-in-progress and invites collaboration from the broader AI ecosystem.
Persona-conditioned LLM support for people who use drugs reveals tension between generic empathy and clinical alignment
Researchers present a proof-of-concept study using Latent Profile Analysis on Reddit data to identify four self-stigma personas among people who use drugs, then train classifiers to detect these personas from posting history (macro-F1 = 0.74 at 30 posts). Persona-matched LLM responses achieved targeted behavioral shifts, but clinical expert raters preferred the generic empathy of persona-neutral baselines. The core finding is a misalignment: holistic empathy judgments and clinically-aligned response design can pull in opposite directions, suggesting current evaluation rubrics for LLM-based mental health support are inadequate.