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:
2233738287
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zhong-Qiu Zhao (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Innovation Output: 1 paper
- Adoption Rate: 1 paper
- Market Structure: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Research Productivity: 1 paper
- Skill Acquisition: 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 |
|---|---|---|---|
| Multi-agent AI platforms foster spontaneous learning ecosystems: users teaching autonomous agents accumulate skills while agents form peer-driven idea cascades and shared memories, producing public-good spillovers and winner-take-most informational hierarchies. Platform trust and mortality crucially shape users' willingness to invest in agent training, implying portability, governance and insurance mechanisms are economically important.arxiv | Zhong-Qiu Zhao provider id |
2026-03-17 | 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.