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:
2355444837
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xinshang Wang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Developer Productivity: 1 paper
- Adoption Rate: 1 paper
- Error Rate: 1 paper
- Hiring: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Purpose-trained 8B AI models repair infeasible supply‑chain optimization models to operationally rational solutions far more often than off‑the‑shelf APIs — 81.7% versus a best API of 42.2%; failures cluster in solver interaction and in domain-specific rationale, requiring different fixes: targeted training for solver workflow and explicit, solver‑verifiable checks for rationality.arxiv | Xinshang Wang provider id |
2026-02-23 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.