This report synthesises findings from 3 peer-reviewed papers addressing the following research question: How does the performance of 7B and 13B VLA models compare in terms of object grounding accuracy and path completion rate in LongNav-R1 when evaluated on R2R-CE with instructions of varying. Generalization in embodied AI is hindered by the "seeing-to-doing gap," which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer "pointing" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: How does the performance of 7B and 13B VLA models compare in terms of object grounding accuracy and path completion rate in LongNav-R1 when evaluated on R2R-CE with instructions of varying complexity, and how does this translate to downstream task success rates?Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.