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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2341519467
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Stella Dong (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 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 |
|---|---|---|---|
| Simulator-coupled, norm-governed multi-agent LLMs make reinsurance decisions more stable and capital-efficient than deterministic automation or single-model approaches; embedding prudential constraints and typed communication materially improves equilibrium stability and clause interpretation in a calibrated synthetic environment.arxiv | Stella Dong provider id |
2025-12-04 | 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.