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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2346808794
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kehang Zhu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Team Performance: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Other: 1 paper
- Wages: 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 |
|---|---|---|---|
| Allowing groups to delegate decisions to an LLM increased joint surplus in experimental bargaining, yet participants overwhelmingly preferred higher-control advice and frequently modified AI proposals — reducing realized gains. The welfare improvement stems not from better model capability but from removing the human filter: autonomous execution captured surplus that advisory and coaching modes lost through user overrides.arxiv | Kehang Zhu provider id |
2026-02-12 | 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.