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:
2280135392
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Guozheng Li (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Allocation: 1 paper
- Worker Satisfaction: 1 paper
- Error Rate: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Vibe coding speeds up visualization prototyping but stumbles on polish: an empirical study finds large language model–driven generation cuts initial development time but struggles with detailed interactions, state coordination and reliable mappings, turning developer work into a costly loop of semantic alignment and manual fixes.arxiv | Guozheng Li provider id |
2026-08-30 | 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.