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:
2411005897
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Anthony Twum-Barimah (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Developer Productivity: 1 paper
- Adoption Rate: 1 paper
- Error Rate: 1 paper
- Organizational Efficiency: 1 paper
- Task Completion Time: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Current autonomous workplace agents collapse under dozens of interleaved long-horizon tasks, but CorpGen — using hierarchical planning, sub-agent isolation and tiered memory — boosts completion rates up to 3.5× and preserves performance as workload increases.arxiv | Anthony Twum-Barimah provider id |
2026-02-15 | 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.