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: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2290703224
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Audrey Cheng (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Task Completion Time: 1 paper
- Other: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| An LLM-driven synthesis system shrinks months of expert formal-verification work to hours: IDS fully implements and verifies seven distributed key-value-store specs in ~6.8 hours at $106 each, outperforming prior coding agents and producing implementations up to three times faster than published verified systems.arxiv | Audrey Cheng provider id |
2026-05-22 | 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.