ML researcher bridging LLM-based misinformation detection and industrial generative recommendation
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 (2026-09-20) 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—
This axis is not scored: the current snapshot contains no publication counts or output-volume metrics for Yuting Zhang. It would measure the volume, cadence, and format of research output over time, beyond the two reviewed 2026 arXiv preprints.
Impact—
Impact cannot be scored yet: no citation counts, h-index, or adoption signals are available in the input data. This axis would measure how widely the two reviewed works are cited and taken up by the misinformation-detection and recommender-system communities.
Focus0/100
Focus scores 0 on the 0-100 scale, driven by an exactly even 50/50 field distribution between NLP/graph learning for misinformation detection and industrial recommender systems work. The two reviewed 2026 papers sit on opposite sides of this split: the meta-path paper targets fake news detection, while LIGE-GR targets production re-ranking. The record so far reads as deliberately two-track rather than narrowly specialized.
Collaboration84/100
Collaboration scores 84/100. The bibliometric snapshot records 66 distinct co-authors and a recurring core of twelve, including Ziyi Zhou, Xiaoming Zhang, Hui Pang, Tiesunlong Shen, and Erik Cambria. Such breadth across a young, two-field record suggests heavy team-based work in both academic NLP and industrial recommender settings, with middle authorship positions on both reviewed 2026 papers.
Activity—
Activity is not scored: the snapshot lacks yearly publication or submission cadence data. It would measure research tempo — how consistently new work appears over recent years — which cannot be inferred from two same-year (2026) arXiv entries alone.
Reproducibility38/100
Reproducibility averages 38/100 across the two reviewed papers. The fake news detection paper rates medium readiness: it reports strong low-resource gains but no significance tests or variance estimates. LIGE-GR rates low: its ~1% watch-time lift at ~10% compute rests on internal Meta A/B tests that cannot be verified externally. Reproducibility is the weakest evidenced axis in this record.
Research summary
Yuting Zhang is a machine learning researcher whose reviewed work to date spans two distinct tracks: graph-based misinformation detection with large language models, and industrial-scale generative recommendation. This is a name-resolved profile built from public bibliographic data — the name is common, and institution and ORCID are unconfirmed. Two 2026 arXiv papers carry full Evolve reviews, with Yuting Zhang in middle authorship on both. The first, 'Reasoning through Evolution,' uses a genetic algorithm to discover meta-paths automatically so frozen LLMs can reason over news propagation graphs; the review credits strong low-resource gains while noting the absence of significance tests or variance estimates. The second, 'LIGE-GR,' upgrades existing rankers to listwise generative recommendation in place, reportedly lifting watch time ~1% for ~10% compute, with evidence drawn solely from internal Meta A/B tests. The radar reflects this early, split record: collaboration scores 84/100 on 66 distinct co-authors and twelve recurring collaborators; the field distribution is an even 50/50 across the two tracks, putting focus at 0/100; and reproducibility averages 38/100 over the two reviewed papers, held back by unverifiable industrial evidence. Output, impact, and activity metrics are not yet available and are left unscored rather than estimated.
Collaboration pattern
Collaboration is the strongest evidenced axis, scoring 84/100 with 66 distinct co-authors recorded. Twelve recur most often — among them Ziyi Zhou, Xiaoming Zhang, Hui Pang, Tiesunlong Shen, Bingyu Yan, Erik Cambria, Venkat Srinivas, and Sam Woodmansee. The pattern suggests parallel team science: an academic misinformation-detection group on one track and an industrial recommender team on the other, with Yuting Zhang in middle authorship on both reviewed 2026 papers.
Research keywords
Highlights
Evolutionary meta-paths bring propagation graphs to frozen LLMs for fake news detection
LIGE-GR: listwise generative recommendation with industrial-scale A/B evidence
A two-track record linking misinformation graphs and production recommendation
The field distribution is an even 50/50 between graph-based misinformation detection and industrial recommender systems, and both reviewed papers share LLM-centric methods — multi-agent evolutionary search on one side, listwise generation with beam search on the other — a pattern that connects academic NLP with production machine learning.
Timeline
- 2026arXiv preprint on automatic meta-path discovery for LLM-based fake news detection, with middle authorship and a medium reproducibility rating.
- 2026arXiv preprint on LIGE-GR, moving industrial ranking toward listwise generative recommendation with internal A/B evidence only.
- 2026Bibliometric snapshot records 66 distinct co-authors, twelve frequent collaborators, and an even 50/50 split between the two research fields.
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.
- Bibliometric scores and co-author counts come from OpenAlex-based public bibliographic data; institution, country, and ORCID are unconfirmed in this snapshot.
- 'Yuting Zhang' is a common name; this profile is name-resolved from public bibliographic records and may aggregate homonymous authors.
- Output, impact, and activity axes are unscored: only two papers (both 2026 arXiv preprints) carry full Evolve reviews, and no citation data is available.
- LIGE-GR's reported gains rest on internal Meta A/B tests and cannot be independently verified.