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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2348536851
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yue-Ying Li (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| Malicious third‑party 'skills' in LLM agent registries are rare but potent: 157 of 98,380 skills contained confirmed attacks exploiting hundreds of vulnerabilities, largely driven by one templated threat actor and removed after disclosure.arxiv | Yue-Ying Li provider id |
2026-02-06 | 37 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 37 cumulative citations. This is a coverage summary, not an author score or h-index.