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
1/1OpenAlex citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5123546751
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Weibang Dang (openalex, provider refresh)
- Weibang Dang (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Multimodal large language models materially raise the speed and accuracy of routine accounting work—automating reporting, anomaly detection and document classification—yet data-privacy, interpretability and integration hurdles threaten broad adoption.openalex | Weibang Dang provider id |
2025-12-01 | 0 |
Citation observation summary
OpenAlex 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.