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:
49543528
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xiao Liu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Creativity: 1 paper
- Decision Quality: 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 |
|---|---|---|---|
| Large language models generate plausible but cautious research ideas that echo authors' thinking and rarely suggest null hypotheses, scientists find; automated judges misalign with experts, but a reward model trained on human ratings meaningfully narrows the gap.arxiv | Xiao Liu provider id |
2026-06-06 | 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.