EgoEasy Benchmark: Evaluating Automated Annotation of Egocentric Workflows

Egocentric videos provide a scalable source of real-world activity data for embodied learning, yet converting unedited footage into reliable supervision remains a major bottleneck. Existing benchmarks evaluate only isolated tasks, query-driven segments, or predefined perception outputs, failing to assess whether a system can recover complete, evidence-grounded workflows while limiting unsupported claims. To address this gap, we introduce EgoEasy Benchmark (EEB), a comprehensive benchmark and evaluation suite for query-free annotation of egocentric workflows. EEB comprises 472 videos totaling 38.15 hours (2,891 episodes across 10 environmental and 13 workflow families), capturing rich transitions, navigation, interruptions, and unidentifiable intervals. We establish a unified annotation schema and evaluation protocol that decouples reference-content recovery from evidential reliability via prediction-side semantic canonicalization, evidence-aware adjudication, and capacity-constrained matching. Three complementary dashboards evaluate hierarchical workflow structure, fine-grained propositions, and annotation reliability. Evaluation across seven video-language model baselines reveals that current systems remain weak on complete temporal coverage, role grounding, and evidential calibration, highlighting the need for EEB in advancing robust video-based supervision.