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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2296564299
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jiahui Xue (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 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 |
|---|---|---|---|
| AI peer feedback nudges scientists to improve their papers: sending LLM-generated critiques to over 31,000 arXiv preprints raised revision rates by about 12.6% and increased later use of LLM tools. The benefits were concentrated among authors in non-English regions, earlier-career teams and less-cited manuscripts, suggesting AI can widen access to timely scientific critique.arxiv | Jiahui Xue provider id |
2026-05-22 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.