When Drones Think Too Much: Defining Cognition Envelopes for Bounded AI Reasoning
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. ...