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:
2427062434
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yanlong Fang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Governance And Regulation: 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 dynamic auction that picks not only which ad to show but when to show it inside multi-turn LLM conversations extracts timing option value and—under the paper's assumptions—yields truthfulness in expectation and an 11% simulated net-revenue lift over fixed-timing baselines, though the gains rest on synthetic dialog simulations and a bid-independent click estimator.arxiv | Yanlong Fang provider id |
2026-07-31 | 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.