EgoArgus: Benchmarking VLMs as Situational Assistants for Modality-Grounded User Supports

Abstract:VLMs are increasingly positioned as daily assistants that perceive first-person environments, follow user dialogue, and decide how to help. Existing egocentric benchmarks mainly evaluate visual understanding in isolation, leaving open whether models can arbitrate between visual evidence and user-provided language when the two are helpful, irrelevant, or conflicting. We introduce EgoArgus, a human-annotated dataset for evaluating egocentric assistants on understanding and decision tasks in five dialogue-video daily scenarios. Our results demonstrate that it is still challenging for current VLMs as reliable egocentric assistants, which requires identifying which modality is trustworthy and deciding when intervention is warranted. Deeper analysis also shows that existing modality bias mitigation methods are quite restricted to enhance performance, providing insights to aid practioners into the deployment of current VLMs as daily assistants.

Computation and Language (cs.CL)

arXiv:2608.25561 [cs.CL]

(or arXiv:2608.25561v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.25561 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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