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
9Unique collaborators
0/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
3449314
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tianpei Yang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Decision Quality: 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 model-based offline RL system raises per-user net profit in incentivized advertising by about 8% versus a strong baseline, using a world model plus conservative Q-regularization and an independent counterfactual scorer to safely select policies before deployment. The result, validated on large-scale ByteDance logs and live A/B tests, demonstrates a practical route to automate incentive allocation but is narrowly scoped to short-session RTB-driven ad products.arxiv | Tianpei Yang provider id |
2026-08-28 | Missing, not zero |
Citation observation summary
Semantic Scholar supplied counts for 0 of 1 papers in this view; 1 are missing.