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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
107691802
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Gauri Malwe (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Error Rate: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| A large-scale reliability audit shows mid-sized open-weight LLMs can deliver near-production performance for SME agent deployments while smaller models fail mainly at tool initialization. qwen2.5:32b matched GPT-4.1’s flawless results and qwen2.5:14b offered a 96.6% success rate with 7.3s latency, suggesting affordable, reliable on-prem agent deployments are feasible.arxiv | Gauri Malwe provider id |
2026-01-22 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.