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
36Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2342280537
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Amin Sadeghi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Other: 1 paper
- Output Quality: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Qatar's Fanar 2.0 builds an Arabic‑first generative AI stack on 256 H100 GPUs using a curated 120B‑token corpus and targeted continual pre‑training/model‑merging, reporting double‑digit benchmark gains while using far less pre‑training than its predecessor. The project shows language‑specific, quality‑focused strategies can be a cost‑effective alternative to the global scale arms race, enabling sovereign control and niche competitiveness for underrepresented languages.arxiv | Amin Sadeghi provider id |
2026-03-17 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.