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:
49545932
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yian Yin (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Governance And Regulation: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| Scientists who adopt large language models publish many more preprints — gains of roughly 24–89% depending on field — but much of the extra output is stylistically polished yet substantively weaker. LLM users also draw on a wider, younger literature, forcing journals and funders to rethink how scientific contribution is evaluated.arxiv | Yian Yin provider id |
2026-01-19 | 80 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 80 cumulative citations. This is a coverage summary, not an author score or h-index.