Yi Xu

Output—

Not yet scored: total publication volume and output trajectory cannot be computed from this snapshot, which indexes only 8 works, all in 2026, and lacks a multi-year publication history. This axis would measure cumulative and per-year research output.

Impact—

Not yet scored: citation-based impact is unavailable — the snapshot records h-index 0 and i10-index 0 with no citation counts, expected for 2026-dated work. This axis would measure citations, h-index, and influence on subsequent research.

Focus0/100

Focus scores 0 because output is spread evenly rather than concentrated: the field distribution splits into three equal thirds — robotics/robot manipulation/embodied AI, robot learning/vision-language-action models, and multimodal machine learning/educational technology (0.33 each). Topic counts reinforce this, with reinforcement learning in robotics and robot manipulation and learning at 6 each, while AI-based planning and multimodal applications sit at 3 each, indicating parallel research threads rather than one core line.

Collaboration58/100

Collaboration scores 58, a moderate level. The OpenAlex window records 17 distinct coauthors, and the frequent-coauthor list shows a stable core: Taiyi Su, Zitai Huang, Jian Zhu, and Tianbin Liu recur across papers, joined by Jianjun Zhang, Chong Ma, Weiyi Lu, and Hanli Wang. Across the three reviewed papers Yi Xu holds first, middle, and last authorship but no corresponding-author role, consistent with team-based industrial research.

Activity64/100

Activity scores 64, driven by a concentrated burst of output: recent3yWorks is 8, and the counts-by-year data places all 8 works in 2026, with no indexed output in earlier years. All three Evolve-reviewed papers are 2026 arXiv preprints, corroborating a recently launched or recently indexed research program. The absence of earlier records means the score reflects current momentum rather than a sustained multi-year track record.

Reproducibility32/100

Reproducibility scores 32, computed across 3 reviewed papers. Readiness is low for both robot-learning papers — JEPA-WAM's review notes no code release, no significance tests, and no task-agnostic control, while MaskHarness-WAM lacks component ablations — and medium for KnowVis, whose LLM-judge evaluation and 10-participant study are offset by judge circularity and weak statistics. The pattern indicates real-robot results reported without the open artifacts needed for independent replication.