Yi Zhou
Ministry of Education of the People's Republic of China
Robot learning and NLP researcher: memory-efficient vision-language-action policies and Chinese debate benchmarking.
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-13) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored: no reliable publication-volume data is available for this researcher in the current snapshot. This axis would measure total scholarly output, venue distribution, and first/corresponding-author share across the full career record.
Impact—
Not yet scored: citation and influence metrics are unavailable for this name-resolved profile. This axis would measure the uptake and scholarly influence of the recorded publications once citation data can be linked to them.
Focus0/100
Focus scores 0 because recorded output splits exactly evenly (0.5 / 0.5) between two unrelated fields: robot learning (vision-language-action models) and natural language processing (computational argumentation). The 2026 StateMem paper sits in the former and the Chinese debate benchmark in the latter. With only two reviewed papers, this flat split reads as a deliberate dual-track portfolio rather than demonstrated cross-field drift, and the score should firm up as more papers accrue.
Collaboration48/100
Collaboration scores 48, based on 10 distinct co-authors recorded in the snapshot. The frequent-coauthor list — including Wenzhuo Li, Qiongfeng Shi, Zongrui Yang, Haoyuan Li and others — suggests a compact, recurring team rather than a wide external network, though the snapshot does not record which collaborators map to which paper. Recorded authorship placement is supportive rather than leading: last author on StateMem and a middle author on the Chinese debate benchmark, with neither paper marked corresponding.
Activity—
Not yet scored: the snapshot contains no year-by-year publication counts, so publishing tempo cannot be computed. This axis would measure output cadence and continuity over time, such as papers per year and gaps in the publication record.
Reproducibility55/100
Reproducibility scores 55, the mean readiness rating across the 2 reviewed papers, each graded medium. Evolve's reviews flag concrete gaps rather than fatal flaws: StateMem's memory-task gains are reported without error bars and its inference-time savings are modest, while the Chinese debate benchmark ships solid dataset design and label-reliability analysis but no significance tests. No separate code- or data-release information is recorded in this snapshot.
Research summary
Yi Zhou appears in the Evolve catalog with two 2026 arXiv papers that split evenly (0.5 / 0.5) between two distinct fields: robot learning (vision-language-action models) and natural language processing (computational argumentation). On StateMem, a single-token residual memory architecture with error-gated prefix caching for vision-language-action policies, Yi Zhou is the last author (not corresponding); Evolve's review finds real gains on memory-oriented robot tasks while noting that reported margins lack error bars and efficiency savings are modest. On the Chinese Competitive Debating Dataset and Benchmark — 148 expert-annotated matches covering 3 tasks — Yi Zhou is a middle author; the review judges the dataset design and label-reliability analysis solid, though results lack significance testing. The radar records a collaboration score of 48 with 10 distinct co-authors, a focus score of 0 driven by the even two-field split, and a reproducibility score of 55 averaged over 2 papers, both rated medium readiness. Output, impact, and activity axes lack sufficient data and are deliberately unscored. Because 'Yi Zhou' is a common name and no ORCID is available, this profile is name-resolved from public bibliographic data.
Collaboration pattern
The snapshot records 10 distinct co-authors, and the frequent-coauthor list suggests a stable core team — Wenzhuo Li, Qiongfeng Shi, Zongrui Yang, Haoyuan Li, Zhongsheng Wang and others — though which collaborators map to which paper is not recorded. Yi Zhou's recorded roles are supportive (last author on StateMem, middle author on the debate benchmark; neither corresponding), consistent with contributing within a group. Collaboration score: 48/100.
Research keywords
Highlights
StateMem: lean residual memory with adaptive inference for VLAs
Novel expert-annotated Chinese competitive debating benchmark
Timeline
- 2026StateMem appears on arXiv: single-state residual memory with adaptive inference for vision-language-action policies (last author, non-corresponding).
- 2026Chinese Competitive Debating Dataset and Benchmark appears on arXiv: 148 expert-annotated matches across 3 tasks (middle author, non-corresponding).
- 2026Recorded output splits evenly between robot learning (vision-language-action models) and NLP (computational argumentation), with 10 distinct co-authors captured in the snapshot.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Name-resolved from public bibliographic data: 'Yi Zhou' is a common name, no ORCID is available, and the linkage to these papers is probabilistic.
- Bibliometric scores derive from an OpenAlex-based snapshot covering only the 2 papers Evolve has reviewed; no career-wide record is included.
- Output, impact, and activity axes are unscored (insufficient data) — no publication counts, citations, or per-year tempo were available.
- The listed affiliation is a generic ministry-level body ('Ministry of Education of the People's Republic of China') with no country field; lab-level affiliation is unknown.