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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2456615882
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yingli Zhou (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Training Effectiveness: 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 proposed 'Causal World System' would make enterprise AI ecosystems answerable and actionable by overlaying an explicit causal graph across heterogeneous data sources so humans, models and agents can ask 'what if' and 'what would have happened'—but the architecture is a conceptual agenda without empirical validation of feasibility or scale.arxiv | Yingli Zhou provider id |
2026-08-07 | 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.