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
309Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2277852611
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- A. Schubert (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 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 industry-designed benchmark shows AI still fails most real-world, long-horizon professional tasks: across 1,000+ GDP-relevant workflows, mainstream systems fully pass only 2.6% of the hardest challenges. Agents' Last Exam (ALE), mapped to O*NET occupational categories and built with 250+ experts, aims to shift evaluation toward measurable economic impact.arxiv | A. Schubert provider id |
2026-06-03 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.