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
12Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2337690647
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Matt Fredrikson (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Other: 1 paper
- Task Allocation: 1 paper
- Error Rate: 1 paper
- Task Completion Time: 1 paper
- Wages: 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 |
|---|---|---|---|
| An AI agent nearly rivalled human pentesters in a live campus network: ARTEMIS discovered nine valid vulnerabilities with an 82% validation rate and outperformed nine of ten professionals, with some variants operating at about $18/hour versus $60/hour for human testers; existing AI scaffolds lagged, and agents still suffer higher false positives and struggle with GUI-driven tasks.arxiv | Matt Fredrikson provider id |
2025-12-10 | 13 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 13 cumulative citations. This is a coverage summary, not an author score or h-index.