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:
2110652561
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Vipul Gupta (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Task Completion Time: 1 paper
- Adoption Rate: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Other: 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 three‑phase distillation recipe compresses large retrieval models into compact encoders that nearly match teacher accuracy while slashing latency and raising ad revenue; deploying the student retriever in Bing Ads produced ~98% precision recovery, up to 27× lower latency, and a 1% revenue lift in production A/B tests.arxiv | Vipul Gupta 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.