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
13Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1396744110
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Andrea Lacava (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Completion Time: 1 paper
- Developer Productivity: 1 paper
- Error Rate: 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 |
|---|---|---|---|
| GENESIS proposes an agentic AI platform that could dramatically shorten cellular R&D cycles by converting specifications and anomalies into over-the-air-validated solutions and storing them in a compounding knowledge base; however, the framework's claims rest on architectural design with limited empirical evidence and unresolved risks from LLM hallucinations and sim-to-hardware transfer.arxiv | Andrea Lacava provider id |
2026-05-26 | 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.