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
30Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2424068037
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Anthony Sistilli (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Research Productivity: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Innovation Output: 1 paper
- Market Structure: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| A new Pokemon-based benchmark exposes large capability gaps on multi-agent, partial-observability and long-horizon planning tasks: specialist RL systems and human experts outperform generalist LLMs, and a 20M+ trajectory dataset plus a NeurIPS competition confirm strong community interest and reproducible evaluation.arxiv | Anthony Sistilli provider id |
2026-03-16 | 10 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 10 cumulative citations. This is a coverage summary, not an author score or h-index.