Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
arXiv:2605.23938v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly fuse heterogeneous inputs in ubiquitous systems. Yet, how LLMs implicitly allocate authority when sensor measurements and user claims conflict remains unexamined, raising critical reliability concerns for deployments where physical sensing must retain priority. Unlike explicit traditional fusion, LLMs bury authority allocation within learned representations. We discover this allocation is severely format-dependent: numerical sensor data fails to integrate into answer-relevant model directions, allowing
The rapid deployment and integration of LLMs into ubiquitous systems necessitates a deeper understanding of their internal decision-making, especially as these systems interface with critical physical environments.
This research reveals a fundamental flaw in how LLMs process disparate input types, indicating that current architectural approaches may compromise reliability and safety in real-world applications where physical sensing must be prioritized.
Our understanding of LLM robustness and reliability in multi-modal environments is challenged, suggesting a need for significant architectural or training paradigm shifts to ensure proper authority allocation between sensor data and user input.
- · AI safety researchers
- · Developers of robust multi-modal AI architectures
- · Companies prioritizing AI reliability and verification
- · LLM developers without robust sensor integration strategies
- · Systems relying on unchecked LLM authority allocation in critical environments
- · Applications where safety depends on accurate physical sensing
Increased scrutiny and re-evaluation of LLM design for critical infrastructure and ubiquitous computing applications.
Development of new LLM architectures or fusion techniques specifically designed to explicitly manage authority and trust across heterogeneous input types.
Potential regulatory frameworks requiring transparent authority allocation mechanisms in AI systems deployed in high-stakes environments.
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Read at arXiv cs.LG