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
14Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2317103435
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jingbo Zhou (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 1 paper
- Ai Safety And Ethics: 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 new 100-task benchmark finds LLM agents can complete long office-suite jobs far faster and cheaper than humans, but still underperform on deliverable quality: the best model scores 17.9 versus a human baseline of 27.8 on OmegaUse-OfficeVal.arxiv | Jingbo Zhou provider id |
2026-07-29 | 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.