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:
115514916
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Prakhar Khatri (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Developer Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Repository AGENTS.md files do not improve coding agents' pass rates on gold tests across two leading models, with correctness unchanged and only narrow process/efficiency shifts; agent-specific task difficulty bands explain why single-agent studies previously reported conflicting results.arxiv | Prakhar Khatri provider id |
2026-07-28 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.