ML researcher at Visa (UK): LLM distillation and self-evolving time series agents, with strong reproducibility signals.
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-02) 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—
Insufficient data: the snapshot records an h-index and i10-index of 0 with only 2 recent works, so output — total publication volume and productivity over time — cannot be scored yet.
Impact—
Insufficient data: no citation counts or uptake signals are available in the snapshot, so impact — how far the work has propagated through citations and downstream use — remains unscored.
Focus0/100
The focus dimension scores 0/100, reflecting a broad footprint rather than a single lane: OpenAlex topic counts show Topic Modeling at 2 and one count each for language and cultural evolution, time series analysis and forecasting, authorship attribution and profiling, and computational text analysis methods, while the field distribution splits 0.5/0.5 between LLM distillation and LLM agents for time series analysis.
Collaboration62/100
Collaboration scores 62/100: only 2 recent works are on record yet 21 distinct coauthors appear, a wide-but-shallow network. In the repeated-partner list Jie Yang, Yan Zheng, and Jiarui Sun each recur, while on both cataloged papers Fang holds a middle-author, non-corresponding position, so leadership patterns cannot yet be read from the data.
Activity32/100
Activity scores 32/100: counts-by-year shows 2 works in 2026 and nothing recorded earlier, with recent3yWorks at 2. Output concentrates in a single year, which reads as a newly visible publication record rather than sustained multi-year productivity — this axis measures publishing cadence over time.
Reproducibility78/100
Reproducibility is the strongest axis at 78/100, a mean computed across 2 reviewed papers. LastOPD carries high readiness — strong diagnostics, though a single run tuned to MATH-500 — while TimeEvo is rated medium, with a rigorous 30-cell protocol but no self-evolving baselines. The axis measures how readily others can re-run the work.
Research summary
Zhengyu Fang, listed at Visa (United Kingdom), is an early-stage machine learning researcher whose visible record centers on two 2026 arXiv preprints reviewed in the Evolve catalog: LastOPD, on taming collapse in latent on-policy distillation, and TimeEvo, on failure-driven self-evolution of a time series agent. On both papers Fang holds a middle, non-corresponding author position, and the field distribution splits evenly — 0.5/0.5 — between LLM distillation and LLM agents for time series analysis, pairing representation-level training methods with agentic tooling. OpenAlex topic counts reinforce the spread: Topic Modeling appears twice, with single counts for language and cultural evolution, time series analysis and forecasting, authorship attribution and profiling, and computational text analysis methods. Collaboration data records 21 distinct coauthors against only 2 recent works, with Jie Yang, Yan Zheng, and Jiarui Sun recurring. Reproducibility is the standout signal at a 78/100 mean across the two reviewed papers — high readiness for LastOPD, medium for TimeEvo. Output and impact dimensions lack sufficient data (h-index and i10-index recorded as 0), so this profile reads as an early snapshot of an industrial researcher.
Collaboration pattern
The record lists 21 distinct coauthors against only 2 recent works — a wide-but-shallow network typical of early industrial collaborations. In the repeated-partner list, Jie Yang, Yan Zheng, and Jiarui Sun each appear twice, alongside Zelin Xu, Xiran Fan, Junpeng Wang, Liang Wang, Qinghua Liu, and Yiwei Cai. On both cataloged papers Fang sits in a middle-author, non-corresponding role, so leadership patterns cannot yet be inferred.
Research keywords
Highlights
LastOPD: stabilizing latent on-policy distillation
TimeEvo: failure-driven self-evolution for time series agents
Reproducibility as a signature strength
Across the two reviewed papers the reproducibility mean is 78/100 — the profile's highest dimension: LastOPD is rated high readiness and TimeEvo medium, anchoring an unusually re-runnable early record.
Timeline
- 2026Two arXiv preprints appear — LastOPD (LLM distillation) and TimeEvo (time series agents) — marking the visible start of the publication record, with counts-by-year showing 2 works in 2026.
- 2026LastOPD released: proposes a 10-step last-layer crossfade to curb collapse in latent on-policy distillation.
- 2026TimeEvo released: introduces failure-driven tool-library synthesis with a paired admission gate for time series agents.
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.
- Bibliometrics come from an OpenAlex snapshot only; h-index and i10-index are recorded as 0, and the output and impact dimensions are marked missing, so no citation-based scoring is shown.
- No ORCID is provided; the profile is name-resolved from public bibliographic data, and "Zhengyu Fang" is not a rare name, so identity confidence is limited.
- Only two papers — both 2026 arXiv preprints — have been reviewed by Evolve; this is an early-stage profile and peer-reviewed venues are not yet represented.
- Both cataloged papers list a middle, non-corresponding author position, so the individual contribution split cannot be verified from the data.