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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2227525604
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Shenglin Yin (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 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 two-stage LLM bidding system that pairs few-shot plan generation with a precision optimizer lifts advertisers' cumulative value under budget constraints in offline tests, outperforming prior RL and heuristic methods; real-world deployment effects remain to be validated.arxiv | Shenglin Yin provider id |
2026-01-21 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.