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
12Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2332082364
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Fengyuan Liu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Completion Time: 1 paper
- Other: 1 paper
- Adoption Rate: 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 |
|---|---|---|---|
| A psychometric scale derived from model performance lets researchers predict human task completion time from AI benchmark results. Using this mapping, the authors forecast frontier models' human-time capability and find the 50% solvable-task horizon roughly doubles every six months.arxiv | Fengyuan Liu provider id |
2026-02-06 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 5 cumulative citations. This is a coverage summary, not an author score or h-index.