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
9Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2387803356
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kani Chen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Inequality: 1 paper
- Market Structure: 1 paper
- Organizational Efficiency: 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 simulated economy of LLM agents with hierarchical planning and persistent memory sustains autonomous market activity and reproduces heavy-tailed returns and volatility clustering, while gated education and access constraints generate persistent wealth stratification; simplified planners match narrow tasks but the full design is more robust for long-horizon objectives.arxiv | Kani Chen provider id |
2026-02-11 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.