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 →
2Distinct papers
27Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1739735954
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Osvald Nitski (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 2 papers
- Productivity: 2 papers
- Adoption: 1 paper
Claim outcomes
- Output Quality: 2 papers
- Other: 1 paper
- Adoption Rate: 1 paper
- Research Productivity: 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 expert-designed benchmark shows top AI models can complete high-value knowledge tasks at roughly 60–64% of expert-level quality, with GPT‑5 leading; however, the sizable gap to human professionals underscores continued limits to AI replacing high-skilled knowledge work.openalex | Osvald Nitski provider id |
2026-01-22 | 0 |
| A new benchmark of complex, cross-application professional tasks finds leading AI agents complete under one-quarter of assignments: the best model scores 24% on Pass@1, with most competitors performing substantially worse, highlighting large remaining gaps for real-world professional productivity.arxiv | Osvald Nitski provider id |
2026-01-20 | 12 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 papers in this view; 0 are missing. The observed paper counts sum to 12 cumulative citations. This is a coverage summary, not an author score or h-index.