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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2310230616
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Issa Sugiura (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Firm Revenue: 1 paper
- Error Rate: 1 paper
- Team Performance: 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 90-day simulated coffee economy shows modern LLM agents can run a firm profitably in a controlled multi‑agent market, with active communication linked to higher earnings; however, some models repeatedly choose inaction despite plausible plans.arxiv | Issa Sugiura provider id |
2026-06-15 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.