Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
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 natural-language claims to dominate the final decision, a phenomenon we term \textbf{Authority Inversion}.To diagnose and mitigate this, we develop...
| Authors | Long Zhang, Zi-bo Qin, Wei-neng Chen |
|---|---|
| Primary category | cs.AI |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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