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
19Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2407203890
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Julia Ulziisaikhan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Innovation Output: 1 paper
- Research Productivity: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 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 two-stage, large-context transformer for ads recommendation unlocks scaling-law gains once semantic features are included and, when deployed as Meta’s largest upstream user model, delivered a 4.3% uplift in conversions on Feed and Reels while keeping latency low.arxiv | Julia Ulziisaikhan provider id |
2026-01-27 | 12 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 12 cumulative citations. This is a coverage summary, not an author score or h-index.