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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.26905 |
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| _version_ | 1866910040766545920 |
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| author | Granadeno, Pedro Antonio Alarcon Russell, Arturo Miguel Bernal Nelson, Sofia Hernandez, Demetrius Petterson, Maureen Murphy, Michael Scheirer, Walter J. Cleland-Huang, Jane |
| author_facet | Granadeno, Pedro Antonio Alarcon Russell, Arturo Miguel Bernal Nelson, Sofia Hernandez, Demetrius Petterson, Maureen Murphy, Michael Scheirer, Walter J. Cleland-Huang, Jane |
| contents | 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, over-generalizations, 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. In this paper we describe an LLM/VLM-supported pipeline for dynamic clue analysis within the domain of small autonomous Uncrewed Aerial Systems deployed on Search and Rescue (SAR) missions, and a Cognition Envelope based on probabilistic reasoning and resource analysis. We evaluate the approach through assessing decisions made by our Clue Analysis Pipeline in a series of SAR missions. Finally, we identify key software engineering challenges for systematically designing, implementing, and validating Cognition Envelopes for AI-supported decisions in cyber-physical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cognition Envelopes for Bounded Decision Making in Autonomous UAS Operations Granadeno, Pedro Antonio Alarcon Russell, Arturo Miguel Bernal Nelson, Sofia Hernandez, Demetrius Petterson, Maureen Murphy, Michael Scheirer, Walter J. Cleland-Huang, Jane Artificial Intelligence 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, over-generalizations, 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. In this paper we describe an LLM/VLM-supported pipeline for dynamic clue analysis within the domain of small autonomous Uncrewed Aerial Systems deployed on Search and Rescue (SAR) missions, and a Cognition Envelope based on probabilistic reasoning and resource analysis. We evaluate the approach through assessing decisions made by our Clue Analysis Pipeline in a series of SAR missions. Finally, we identify key software engineering challenges for systematically designing, implementing, and validating Cognition Envelopes for AI-supported decisions in cyber-physical systems. |
| title | Cognition Envelopes for Bounded Decision Making in Autonomous UAS Operations |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26905 |