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: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2354177523
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Danial Amin (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Decision Quality: 1 paper
- Inequality: 1 paper
- Other: 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 Bayesian multi-LLM orchestration cuts simulated hiring pipeline costs by roughly one-third and slashes demographic parity gaps in a 1,000-resume test. Most of the savings come from aggregating diverse LLMs and updating beliefs sequentially, with targeted information gathering providing additional benefit.arxiv | Danial Amin provider id |
2026-01-04 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.