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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2239107390
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Brando Miranda (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Developer Productivity: 1 paper
- Error Rate: 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 |
|---|---|---|---|
| Coding agents increase development velocity by roughly 28–38% across repositories, but repositories that did not commit AI configuration suffer far larger quality declines—about a twofold rise in cognitive complexity (≈+53% vs +27%) and a 1.7× increase in static-analysis warnings—while repositories with committed AI configuration show attenuated quality costs.arxiv | Brando Miranda provider id |
2026-08-26 | 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.