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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2300368573
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Severin Field (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Innovation Output: 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 |
|---|---|---|---|
| Leading AI researchers warn automating AI research could spark recursive self‑improvement and constitute one of the field’s gravest risks, though they diverge on timing and policy; frontier lab staff are more likely than academics to expect explosive growth, and most predict advanced R&D systems will be kept private.openalex | Severin Field provider id |
2026-02-13 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.