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:
2453948874
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Hanyue Huang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Other: 1 paper
- Ai Safety And Ethics: 1 paper
- Error Rate: 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 |
|---|---|---|---|
| LLMs primed with tenants' transaction-cost perceptions more faithfully reproduce survey responses on energy-efficient renovations than demographic-only prompts, with gains observed across GPT-3.5 and two fine-tuned open models; persona-grounded prompting offers a theory-linked path to more interpretable policy simulation.arxiv | Hanyue Huang provider id |
2026-07-27 | 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.