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 →
2Distinct papers
4Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2301177228
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Hengzhi Ye (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 2 papers
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 2 papers
- Other: 2 papers
- Hiring: 1 paper
- Task Allocation: 1 paper
- Turnover: 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 |
|---|---|---|---|
| AI coding agents make code slightly more complex but do not displace new contributors: across matched GitHub projects, adoption raised Python cognitive complexity by ~11% and cyclomatic complexity by 3–4%, while newcomer inflow, onboarding and retention remained unchanged.arxiv | Hengzhi Ye provider id |
2026-07-02 | 0 |
| Large language models produce code whose readability is on par with human solutions overall, yet they show consistent, distinct readability weaknesses and respond only modestly to prompt tweaks; function signatures, constraints and style descriptions are the most influential prompt factors.arxiv | Hengzhi Ye provider id |
2026-05-13 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 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.