Referential Accuracy and Multimodal Grounding Signals in Embodied Instruction Tasks: Graph Analysis

Abstract

The rapid advancement of embodied artificial intelligence has necessitated the development of sophisticated models capable of understanding and executing complex natural language instructions within dynamic three-dimensional physical environments. A persistent challenge in this domain is the accurate grounding of linguistic references to their corresponding physical entities, a process defined as multimodal grounding. This paper investigates the assessment of referential accuracy by employing graph analysis techniques to evaluate multimodal grounding signals in embodied instruction tasks. By conceptualizing the physical environment as a dynamic, interconnected graph of semantic nodes and spatial edges, we propose a comprehensive methodology to measure how effectively computational agents resolve ambiguous referential expressions during task execution. Through rigorous experimental design and comparative analysis across standardized simulation benchmarks, this research demonstrates that graph-based topological representations significantly enhance referential disambiguation compared to traditional flat vector representations. The findings provide empirical evidence that integrating structural relationship analysis into multimodal grounding architectures substantially reduces interaction errors and improves overall task completion rates. This study contributes to the theoretical understanding of vision-language integration and offers practical insights for designing robust cognitive architectures for autonomous agents operating in unstructured human-centric environments.