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:
1516419367
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yohei Nakajima (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Team Performance: 1 paper
- Other: 1 paper
- Organizational Efficiency: 1 paper
- Decision 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 |
|---|---|---|---|
| Compressing many private clues into one shared recommendation raises the top pick's accuracy but collapses group discovery as agents repeat that single choice; allowing coordinated portfolios or changing rewards (pay only sole discoverers) restores exploration and can achieve first-best discovery.openalex | Yohei Nakajima provider id |
2026-07-20 | 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.