Safety Engineering

Why this matters now As AI systems move from chatbots to control towers, the stakes of their hallucinations have escalated. Large Language Models (LLMs) and Vision-Language Models (VLMs) now make—or at least recommend—decisions in physical space: navigating drones, scheduling robots, even allocating emergency response assets. But when such models “reason” incorrectly, the consequences extend beyond embarrassment—they can endanger lives. Notre Dame’s latest research introduces the concept of a Cognition Envelope, a new class of reasoning guardrail that constrains how foundational models reach and justify their decisions. Unlike traditional safety envelopes that keep drones within physical limits (altitude, velocity, geofence) or meta-cognition that lets an LLM self-critique, cognition envelopes work from outside the reasoning process. They independently evaluate whether a model’s plan makes sense, given real-world constraints and evidence. ...

Why this paper matters (for operators, not just researchers) Industrial control stacks (think data center DCIM, grids, water, rail) are hostile terrain for “general” GUI agents: custom widgets, nested hierarchies, air‑gapped deployment, and actions that can actually break things. InfraMind proposes a pragmatic agentic recipe that acknowledges these constraints and designs for them. The result is a system that learns an interface before it tries to use it, then executes with auditability and guardrails. ...

TL;DR As AI agents spread into real workflows, incidents are inevitable—from prompt-injected data leaks to misfired tool actions. A recent framework by Ezell, Roberts‑Gaal, and Chan offers a clean way to reason about why failures happen and what evidence you need to prove it. The trick is to stop treating incidents as one-off mysteries and start running a disciplined, forensic pipeline: capture the right artifacts, map causes across system, context, and cognition, then ship targeted fixes. ...