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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2314158106
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yanchao Sun (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Team Performance: 1 paper
- Decision Quality: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Self-organizing LLM teams systematically underuse their experts and lag behind the best member, suffering performance drops of up to 41% on ML benchmarks; teams average expert and non-expert views—a consensus-seeking bias that grows with size but makes them more robust to adversaries.arxiv | Yanchao Sun provider id |
2026-02-01 | 11 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 11 cumulative citations. This is a coverage summary, not an author score or h-index.