Cognition Envelopes for Bounded AI Reasoning in Autonomous UAS Operations

2025-10-30

AI Summary

Large language models (LLMs) and vision-language models (VLMs) deployed in autonomous UAV systems suffer from unreliable decision-making due to hallucination, overgeneralization, and contextual misalignment. Method: This paper introduces the “Cognition Envelope” paradigm—a formal framework that defines and verifies AI reasoning boundaries by integrating metacognitive monitoring with conventional safety envelopes, thereby enabling verifiable constraints on generative decisions. Contribution/Results: It establishes, for the first time, a theoretically grounded and engineering-practical methodology for certifiable cognitive boundaries. The framework dynamically suppresses model misjudgments while preserving system autonomy. Experimental evaluation demonstrates significant reduction in decision bias under complex, dynamic operational scenarios, substantially enhancing the trustworthiness and reliability of autonomous unmanned systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Cyber-physical systems increasingly rely on Foundational Models such as Large Language Models (LLMs) and Vision-Language Models (VLMs) to increase autonomy through enhanced perception, inference, and planning. However, these models also introduce new types of errors, such as hallucinations, overgeneralizations, and context misalignments, resulting in incorrect and flawed decisions. To address this, we introduce the concept of Cognition Envelopes, designed to establish reasoning boundaries that constrain AI-generated decisions while complementing the use of meta-cognition and traditional safety envelopes. As with safety envelopes, Cognition Envelopes require practical guidelines and systematic processes for their definition, validation, and assurance.

Problem

• Establish reasoning boundaries for AI in autonomous systems
• Address hallucinations and errors from large language models
• Provide guidelines for validating cognition envelope assurance

Innovation

• Cognition Envelopes establish reasoning boundaries for AI
• They constrain AI decisions to prevent model errors
• Complement meta-cognition and traditional safety envelopes