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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2355861946
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Seyedmoein Mohsenimofidi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Task Completion Time: 1 paper
- Adoption Rate: 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 |
|---|---|---|---|
| Short repository instruction files materially speed up AI coding agents: adding an AGENTS.md cut median execution time by ~28.6% and trimmed output token consumption by ~16.6% across tests on 124 GitHub pull requests, suggesting simple config changes can lower runtime and API costs.arxiv | Seyedmoein Mohsenimofidi provider id |
2026-01-28 | 11 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 11 cumulative citations. This is a coverage summary, not an author score or h-index.