Explainability

Opening — Why this matters now We keep insisting that powerful AI systems must be understood before they can be trusted. That demand feels intuitively correct—and practically paralysing. Large language models now operate in medicine, finance, law, and public administration. Yet interpretability tools—SHAP, LIME, mechanistic circuit tracing—remain brittle, expensive, and increasingly disconnected from real-world deployment. The gap between how models actually behave and how we attempt to explain them is widening, not closing. ...

Opening — Why this matters now The world doesn’t suffer from a lack of information—it suffers from a lack of agreement about what’s true. From pandemic rumors to political spin, misinformation now spreads faster than correction, eroding trust in institutions and even in evidence itself. As platforms struggle to moderate and fact-check at scale, researchers have begun asking a deeper question: Can AI not only detect falsehoods but also argue persuasively for the truth? ...

Large language models can craft elegant arguments—but can they prove them? In law, medicine, and finance, a wrong conclusion isn’t just a hallucination; it’s a liability. The paper LOGicalThought (LogT) from USC and UT Dallas takes aim at this problem, proposing a neurosymbolic framework that lets LLMs reason with the rigor of formal logic while retaining their linguistic flexibility. From Chain-of-Thought to Chain-of-Trust Typical prompting strategies—Chain-of-Thought (CoT), Program-Aided Language Models (PAL), or self-critique loops—focus on improving reasoning coherence. Yet none of them guarantee faithfulness. A model can still reason eloquently toward a wrong or unverifiable conclusion. LogT reframes the task: it grounds the reasoning itself in a dual context—one symbolic, one logical—so that every inference step can be traced, validated, or challenged. ...

TL;DR Pair a classic mixed‑integer inventory redistribution model with an LLM-driven context layer and you get explainable optimization: the math still finds near‑optimal transfers, while the LLM translates them into role‑aware narratives, KPIs, and visuals. The result is faster buy‑in, fewer “why this plan?” debates, and tighter execution. Why this paper matters for operators Most planners don’t read constraint matrices. They read stockout risks, truck rolls, and WOS. The study demonstrates a working system where: ...

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. ...