Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness
This study proposes and evaluates a compositional trust modeling approach based on quantum natural language processing (QNLP) for embodied intelligence. By comparing classical NLP clustering and QNLP-based clustering on human survey responses, the research demonstrates that quantum-inspired embeddings capture richer latent semantic structures, improved semantic coherence, contextual sensitivity, and robustness. The framework identifies eight trust dimensions, including two emergent dimensions: calibrated trust and predictive reliability.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and AI developers looking for advanced computational frameworks to model trust dynamics in embodied intelligence.
What the document contains
The study covers the comparative evaluation of quantum natural language processing versus classical NLP methods for modeling human trust in embodied AI using survey responses, extracting semantic trust dimensions, and testing model robustness.
The text does not provide large-scale psychometric validation, direct behavioral reliance data, or regulatory compliance frameworks.
- QNLP-based clustering captures richer latent structures and nuanced trust dimensions compared to classical clustering methods.
- The study identifies eight trust dimensions, including two emergent ones: calibrated trust and predictive reliability.
- Quantum embeddings show greater robustness and lower assignment variability under preprocessing variations.
- Trust in embodied AI is demonstrated to be dynamic, context-dependent, and temporally structured.
· Introduction · Literature Review · Method · Results · Discussion · Conclusions
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
1/5Clear responsibility for AI outcomes
Data collection adhered to privacy and confidentiality standards, with all responses de-identified and securely stored.
3.1.1. Raw Data Collection
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Privacy
1/5Protection of personal data and private life
Data collection adhered to privacy and confidentiality standards
3.1.1. Raw Data Collection
screening score · 2026-10-24
Transparency
2/5Openness about how AI systems work and are used
Rather than being based solely on perceived similarity or simple behavior models, trust in these systems must be understood as an ongoing process of calibration, grounded in transparency
1. Introduction
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-24
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
Ethical approval for this study was granted by the Institutional Review Board (IRB) of the authors’ institution
3.1.1. Raw Data Collection
screening score · 2026-10-24