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
10Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2390991707
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Shijue Huang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Other: 1 paper
- Task Allocation: 1 paper
- Adoption Rate: 1 paper
- Error Rate: 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 live benchmark shows state-of-the-art LLM agents complete at most two-thirds of realistic workflow tasks: the top model passes 66.7% of 105 controlled tasks. Failures cluster in HR, management and multi-system business workflows, indicating end-to-end workflow automation remains far from solved.arxiv | Shijue Huang provider id |
2026-04-30 | 7 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 7 cumulative citations. This is a coverage summary, not an author score or h-index.