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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2244622868
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yun Qu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Training Effectiveness: 1 paper
- Adoption Rate: 1 paper
- Developer Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Research Productivity: 1 paper
- Task Completion Time: 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 predictive prompt-selection method halves (or more) the costly rollouts needed for RL finetuning and improves reasoning accuracy across math, planning and visual-geometry benchmarks; by making iterative finetuning cheaper and faster, the technique could materially lower the marginal compute cost of model improvement for practitioners.arxiv | Yun Qu provider id |
2026-03-11 | 9 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 9 cumulative citations. This is a coverage summary, not an author score or h-index.