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
2Unique collaborators
0/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2110834082
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Lexuan Sun (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Fiscal And Macroeconomic: 1 paper
- Other: 1 paper
- Ai Safety And Ethics: 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 |
|---|---|---|---|
| Simulated households driven by language models reveal that the wording, timing and source of tariff threats matter: immediate, high-rate, and coherent escalation messages raise average inflation and unemployment expectations, while uncertainty and unspecified rates increase disagreement. Central-bank explanations can tighten consensus even when they do not uniformly lower mean expectations, but these results are derived from agent-based simulations calibrated to survey data and should be treated as hypotheses for human testing.arxiv | Lexuan Sun provider id |
2026-08-31 | Missing, not zero |
Citation observation summary
Semantic Scholar supplied counts for 0 of 1 papers in this view; 1 are missing.