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: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
34912789
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- A. Capponi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Market Structure: 1 paper
- Output Quality: 1 paper
- Firm Revenue: 1 paper
- Innovation Output: 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 |
|---|---|---|---|
| Autonomous AI finds hidden links across prediction-market contracts and turns them into profitable signals: agent-identified relationships were correct about 60–70% of the time and yielded roughly 20% average returns in week-long backtests on Polymarket. The result suggests LLM-based agents can uncover latent semantic structure in markets, though live performance, costs and scalability remain untested.arxiv | A. Capponi provider id |
2025-12-02 | 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.