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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2306473132
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Daiwei Chen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Output Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Organizational Efficiency: 1 paper
- Adoption Rate: 1 paper
- Other: 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 |
|---|---|---|---|
| Statistical guarantees make LLM answers 'safer' but often poorer: conformal factuality filtering can ensure claim-level correctness for retrieval-augmented models, yet high-assurance thresholds commonly produce vacuous outputs and break under distribution shift; lightweight entailment verifiers achieve similar reliability at over 100× lower compute, reshaping cost and product trade-offs.arxiv | Daiwei Chen provider id |
2026-03-17 | 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.