Explainability

TL;DR DSM5AgentFlow uses three cooperating LLM agents—Therapist, Client, and Diagnostician—to simulate DSM‑5 Level‑1 screenings and then generate step‑by‑step diagnoses tied to specific DSM criteria. Experiments across four LLMs show a familiar trade‑off: dialogue‑oriented models sounded more natural, while a reasoning‑oriented model scored higher on diagnostic accuracy. For founders and PMs in digital mental health, the win is auditability: every symptom claim can be traced to a quoted utterance and an explicit DSM clause. The catch: results are built on synthetic dialogues, so ecological validity and real‑world safety remain open. ...

When explaining predictions made by Graph Neural Networks (GNNs), most methods ask: Which nodes or features mattered most? But what if this question misses the real driver of decisions — not the nodes themselves, but how they interact? That’s the bet behind GraphEXT, a novel explainability framework that reframes GNN attribution through the lens of externalities — a concept borrowed from economics. Developed by Wu, Hao, and Fan (2025), GraphEXT goes beyond traditional feature- or edge-based attributions. Instead, it models how structural interactions among nodes — the very thing GNNs are designed to exploit — influence predictions. ...

The Grammar and the Glow: Making Sense of Time-Series AI What if time-series data had a grammar, and AI could read it? That idea is no longer poetic conjecture—it now has theoretical teeth and practical implications. Two recent papers offer a compelling convergence: one elevates interpretability in time-series AI through heatmap fusion and NLP narratives, while the other proposes that time itself forms a latent language with motifs, tokens, and even grammar. Read together, they suggest a future where interpretable AI is not just about saliency maps or attention—it becomes a linguistically grounded system of reasoning. ...