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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2386066327
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yang Xiang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Other: 1 paper
- Adoption Rate: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| Summarizing planned promotions and holidays with an LLM cuts demand-forecasting errors sharply: EventCast reduces MAE by up to 57% and MSE by up to 83% versus top industrial baselines during event-driven periods, and delivers large improvements versus models that ignore event knowledge.arxiv | Yang Xiang provider id |
2026-02-07 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.