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:
2365316200
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zhicheng Lin (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Skill Acquisition: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| A concise four-part model dispels polarized myths about large language models, showing six frequent errors—ranging from 'stochastic parrots' to anthropomorphism—stem from conflating training, sampling, memory, and agency; the paper supplies diagnostic questions and practical policy fixes to improve evaluation, deployment, and publisher guidance.arxiv | Zhicheng Lin provider id |
2026-08-19 | 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.