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
8Unique collaborators
1/1Semantic Scholar citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2419020552
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kalyn Asher Montague (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Team Performance: 1 paper
- Task Allocation: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Other: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| In a live capture‑the‑flag contest, autonomous agents that self‑direct prompting and tool use beat most human teams, exposing human prompting and context specification as the key bottleneck to effective human–AI collaboration.semantic_scholar | Kalyn Asher Montague provider id |
Fetched 2026-03-18 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.