Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2348245181
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Qianyu Cheng (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Pinterest’s Generative Engine Optimization repurposes vision-language models to generate query-driven collection pages and authority-aware link graphs, and the company reports a 20% lift in organic traffic and multi-million user gains after deployment across billions of images; however, the brief lacks clear causal tests or transparency about attribution. arxiv | Qianyu Cheng provider id |
2026-02-03 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.