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
9Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2337493478
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xiaowei Jin (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Developer Productivity: 1 paper
- Firm Revenue: 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 |
|---|---|---|---|
| An agentic LLM workflow, LEAN-LLM-OPT, can auto-formulate large-scale optimization models and matches or beats prior methods on new Large-Scale-OR and Air-NRM benchmarks; in a Singapore Airlines revenue-management trial it delivers leading performance across tested scenarios.arxiv | Xiaowei Jin provider id |
2026-01-14 | 13 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 13 cumulative citations. This is a coverage summary, not an author score or h-index.