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:
2374094098
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Peng Jiang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Other: 1 paper
- Training Effectiveness: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 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 production LLM-as-Enhancer system, Taiji, bridges LLM semantics and recommender ID spaces using reverse-engineered chain-of-thought and adaptive reward weighting, and—by the authors' A/B tests—boosts commercial ad performance; deployed on Kuaishou since May 2026, it reportedly serves ~400 million daily users. The evidence is compelling at scale but details on experimental design, effect sizes and reproducibility are limited, so independent validation is needed.arxiv | Peng Jiang provider id |
2026-06-02 | 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.