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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2332533310
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Barbara Hammer (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
- Regulatory Compliance: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Exchanging generative modules, not model weights, lets siloed clients recover centralized accuracy in one communication round and supports exact deletion of a client's contribution. The method reduces reliance on pretrained foundation models, lowering coordination costs and creating opportunities (and regulatory questions) for marketplaces in certified generative priors.arxiv | Barbara Hammer provider id |
2026-03-17 | 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.