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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2362617494
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Hyesoo Hong (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Consumer Welfare: 1 paper
- Firm Revenue: 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 |
|---|---|---|---|
| Large language models often treat stored user preferences as global rules rather than context‑dependent signals, leaking or applying preferences in third‑party contexts; stronger personalization improves correct tailoring but also raises harmful misapplication, and prompt‑based fixes only partially mitigate the problem.arxiv | Hyesoo Hong provider id |
2026-03-17 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.