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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
38541211
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Silvère Gangloff (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Creativity: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Innovation Output: 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 |
|---|---|---|---|
| Large language models can generate and verify proofs by recombining known moves, but may fundamentally lack the mechanisms to originate new mathematical concepts; as AI makes proof cheaper, mathematical value will shift toward creative modes current systems cannot yet perform.arxiv | Silvère Gangloff provider id |
2026-08-17 | 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.